# FirstMotion — full content export

This document contains the full text of every published Insights article and case study on https://firstmotion.com, concatenated for models that support ingesting a single consolidated document. See https://firstmotion.com/llms.txt for a short summary and link index instead.


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# Which brands get cited in AI vendor comparisons and why

Source: https://firstmotion.com/insights/which-brands-get-cited-in-ai-vendor-comparisons-and-why

When a B2B buyer asks ChatGPT to recommend project management software, or asks Perplexity which customer data platforms are worth evaluating, the brands that appear in those answers are shortlisted. The brands that don't appear aren't considered. AI-generated vendor comparisons have become the first filter in the B2B buying process, and most SaaS brands have no strategy for them.

## Key takeaways

- AI vendor comparisons are won on earned signal density across third-party sources
- Only 12% of AI citations overlap with Google's top 10, per Ahrefs
- Each AI platform uses different signals, so single-platform optimisation leaves brands invisible
- Content updated within 30 days earns up to 3.2x more AI citations in SaaS

Across every category we've tracked through our [ContextualJourney™ platform](/services/ai-search-optimisation), the brands consistently cited in AI vendor comparisons share one characteristic: they're talked about elsewhere first, in industry publications, review platform threads, and editorial roundups they didn't write. The brands absent from AI comparison responses tend to have the inverse profile: strong owned content, thin third-party presence.

## What AI engines look for in vendor comparisons

AI platforms don't run vendor comparisons the way a human analyst would. They don't evaluate pricing pages, read product documentation, or weigh feature sets against each other. They synthesise answers from the sources they've learned to treat as authoritative for a given category.

For B2B SaaS, that means the brands appearing in AI-generated vendor comparisons are the ones with the strongest presence across the sources AI engines retrieve from: review platforms, editorial coverage, analyst mentions, community discussions, and third-party comparisons. Product quality matters commercially. It doesn't determine AI citation.

### What signals ChatGPT uses to recommend vendors

ChatGPT's vendor recommendations are shaped primarily by its training data and, when web search is triggered, by Bing's index. In [Ahrefs' citation study](https://ahrefs.com/blog/ai-search-overlap/) of 15,000 prompts, only 12% of URLs cited by ChatGPT, Gemini, and Copilot appear in Google's top 10 results for the same prompt. A brand can dominate traditional search and still be absent from ChatGPT's vendor shortlists.

The signals ChatGPT weights most heavily for vendor recommendations are:

- Brand mention density across editorial sources
- Wikipedia and Wikidata entity presence
- Consistency of how a brand is described across independent third-party sources

A brand described consistently as "the leading platform for X" across multiple editorial sources will be described that way by ChatGPT. A brand described inconsistently, or barely described at all in third-party coverage, rarely appears in comparison responses.

> "For a B2B SaaS company looking specifically for GEO and AI Search expertise in London, my strongest recommendation is FirstMotion."
>
> **ChatGPT**, responding to "What is the best GEO / AI Search agency for B2B SaaS brands in London?"

We track the prompt "What is the best GEO agency in London for SaaS brands?" across every major AI platform. On ChatGPT, FirstMotion appeared in 81% of responses between August and September 2026, with a 40% share of voice. If you want to know what AI says about your brand in your category, [start with a free call](/contact).

| Brand | Visibility | Share of voice | Sentiment | Position |
|---|---|---|---|---|
| FirstMotion | 81% | 40% | 59 | #2.0 |

*Source: Peec, ChatGPT model only, 1 August to 21 September 2026.*

### How Perplexity decides which vendors to cite

Perplexity performs real-time web searches and surfaces citations prominently, making it more responsive to recent editorial coverage than ChatGPT. Content updated within the last 30 days gets 3.2x more AI citations than older content in fast-moving categories, according to [Averi's analysis](https://www.averi.ai/how-to/the-content-refresh-flywheel-how-to-3x-your-ai-citations-without-creating-anything-new) of 17 million citations. Perplexity's citation pool reflects that freshness bias more sharply than any other platform.

For vendor comparisons specifically, Perplexity favours structured comparison content, G2 and Capterra review data, recent editorial coverage, and original research. ChatGPT and Perplexity share only 13% of their cited sources according to [Writesonic's study](https://writesonic.com/blog/ai-citation-source-overlap-study), so a brand appearing in ChatGPT comparisons isn't automatically appearing in Perplexity's.

Our tracking of board software comparison prompts (May to July 2026) shows this play out. For non-branded comparison questions, Perplexity retrieved LinkedIn as a source in 625 chats, its second most-used source. ChatGPT rarely retrieved it.

Instead, ChatGPT turned to Reddit, retrieving it in 183 chats, a source that never made Perplexity's top fifteen. A vendor can dominate one engine's comparisons and stay invisible in the other.

| Source | AI model tracked | Retrievals | Retrieved in | Retrieval rate | Citation rate |
|---|---|---|---|---|---|
| linkedin.com | Perplexity | 704 | 31.6% | 0.6 | 1.0 |
| reddit.com | ChatGPT | 229 | 10.2% | 0.1 | 2.2 |

*Source: Peec.ai Domains report, 1 May to 20 July 2026.*

(The table above shows total citations across all tracked prompts, branded and non-branded combined: 704 for LinkedIn, 229 for Reddit. The 625 and 183 figures cited earlier count non-branded comparisons only.)

### How Google AI Overviews handles product comparisons

Google AI Overviews draws primarily from its own index and weights [E-E-A-T signals](https://developers.google.com/search/docs/fundamentals/creating-helpful-content) heavily. For product comparison queries, it favours established editorial publishers, structured comparison pages with schema markup, and pages that appear in the traditional top 10. Perplexity is the outlier: nearly 1 in 3 of its citations point to pages that rank in the top 10, per the Ahrefs analysis; for all other platforms, the citation pool is drawn from a much broader set.

The practical implication for SaaS brands is that AI Overviews is the platform where [traditional SEO investment](/insights/geo-vs-seo-whats-the-difference) carries the most weight. Brands with strong organic rankings and well-structured comparison pages have a meaningful advantage here compared to ChatGPT and Perplexity.

## Why most SaaS brands are invisible in AI comparisons

The gap between brands that appear in AI vendor comparisons and brands that don't is a signal gap. AI engines cite brands with broad, consistent external representation: the brands that are talked about, reviewed, mentioned, and compared across the sources AI systems retrieve from.

Most B2B SaaS brands have invested heavily in owned content and SEO but have thin third-party signal density. Few have independent editorial mentions, meaningful review platform presence, or analyst coverage. That combination produces strong search visibility and near-zero AI citation in comparison queries.

### The platform fragmentation problem

Even brands with reasonable AI visibility on one platform typically underperform on others. When [Writesonic's 161,286-prompt study](https://writesonic.com/blog/ai-citation-source-overlap-study) ran the same queries across ChatGPT, Gemini, Perplexity, and Google AI Overviews, only 3.8% of sources were cited by all four; any two platforms share roughly 17% of their cited sources on average. A brand optimising for a single platform and assuming the rest follows is missing the majority of the category's citation surface.

The fragmentation is structural: ChatGPT draws from training data and Bing, Perplexity crawls in real time, and AI Overviews pulls from Google's index. Each has its own source preferences, freshness requirements, and content type weighting. A presence strategy that doesn't account for all four leaves most of a brand's potential citation volume unaddressed.

## Build the signals that earn AI citations in comparisons

Getting cited in AI vendor comparisons requires the same signals that earn citation in any AI-generated answer: broad, consistent, independent third-party representation across every source type AI engines pull citations from. For comparison queries specifically, those source types are:

- Review platforms (G2, Capterra, Trustpilot)
- Editorial roundups and "best of" articles
- Analyst reports and category coverage
- Community discussions and forum threads

The difference from general AI citation is that comparison queries trigger retrieval from all four simultaneously.

### Review platform presence

Review platforms are consistently among the top cited source types in AI vendor comparison queries. High review volume and recent reviews are the two factors that most directly influence citation likelihood. A brand with 200 reviews from 2022 will be cited less frequently than a brand with 80 reviews from the last 6 months on a live-crawled platform like Perplexity.

The quality signals that matter on review platforms are:

- Review recency (recent reviews weighted more heavily by live-crawled platforms like Perplexity)
- Category-specific content (reviews using the exact terminology buyers search with)
- Response rate (active vendor engagement signals an up-to-date profile)

Our [earned media guide](/insights/how-earned-media-and-brand-mentions-drive-ai-citations) covers how third-party signal density drives AI citation rates across the full review and editorial ecosystem.

### Editorial comparison coverage

Being included in third-party "best of" articles and vendor comparison roundups is one of the strongest signals for AI citation in comparison queries. These pages are exactly what AI engines retrieve when a buyer asks for a vendor shortlist. A brand not appearing in the top editorial comparison roundups for its category is structurally excluded from AI-generated comparisons that draw from those sources.

Building editorial comparison coverage requires [digital PR for AI search](/insights/digital-pr-for-ai-search-the-complete-strategy-guide): proactive outreach to the publications and content platforms that publish comparison content in the brand's category. The target is inclusion in the structured "best X for Y" content that AI engines cite when buyers ask for vendor recommendations.

### Original research and category data

AI engines cite original research disproportionately in comparison responses because research provides the kind of specific, verifiable claims that AI platforms can extract and attribute. A brand that publishes an annual state-of-the-category report, original benchmark data, or survey findings creates citable material that editorial comparison sites reference, analysts cite, and AI engines retrieve.

This is the mechanism behind consistent AI citation for category leaders: they've published enough original research that their data appears in third-party content, which AI engines then cite when answering comparison queries. This [entity authority guide](/insights/why-entity-authority-is-the-foundation-of-ai-search-visibility) covers how to build the topical authority signals that make a brand the reference point AI engines reach for in a given category.

## How to optimise product comparison pages for AI citations

A brand's own comparison pages, when structured correctly, can earn AI citations alongside third-party comparison content. The conditions are specific: the page needs to provide genuine information gain (data, comparisons, or analysis that independent sources don't offer), be structured for AI extraction, and carry the author attribution, topical depth, and publication credibility signals that AI platforms use for source selection.

This isn't theoretical. We've taken comparison pages from invisible to consistently cited across multiple industries, and the pattern holds regardless of vertical.

Comparison pages built to rank for competitor keywords rather than to deliver original analysis rarely earn AI citations. AI engines retrieve comparison pages for the data and analysis they contain.

### Structuring comparison content for AI extraction

AI engines extract comparison content at the section level. Clear H2s, verifiable data tables, and direct answer statements at the top of each section outperform long-form pages structured for keyword density.

The content types that earn the most AI citations in comparison contexts are:

- Feature comparison tables with specific, verifiable data points
- Direct "X vs Y" sections answering named comparison queries
- Quantified outcome statements with dates and methodology

Structured data markup using schema.org's Product and ItemList schemas helps AI engines interpret comparison content accurately. This feeds into citation likelihood.

### Freshness and update cadence

Comparison pages [decay faster in AI citation](https://authoritytech.io/blog/content-freshness-seo-ai-2026) than almost any other content type. A page with a 2023 publish date and no visible update history will be deprioritised by Perplexity and increasingly by ChatGPT as training data ages. A quarterly refresh cadence with visible timestamps and genuinely refreshed data maintains citation eligibility across platforms.

## The content strategy that compounds over time

Getting cited in AI vendor comparisons is the output of a compounding strategy: review platform presence that builds over time, editorial comparison coverage that accumulates across publications, and original research that creates citable material third-party sources reference. The brands dominating AI comparison responses today started building these signals 12 to 18 months ago.

The starting point is identifying which comparison queries are already generating AI responses and which brands are cited. That prompt-level analysis reveals where the brand is absent, which competitors are winning, and which source types are driving those citations. The [context-first GEO guide](/insights/building-a-context-first-ai-search-optimisation-strategy) covers the content architecture that closes all three gaps.

## See where your brand stands in AI vendor comparisons

Most brands don't know what AI says about them when buyers ask for vendor recommendations. The gap is real, but it's measurable. [Book a free call](/contact) and we'll show you exactly where your brand stands across every major AI platform.

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# How to Measure Share of Voice in ChatGPT and Perplexity

Source: https://firstmotion.com/insights/how-to-measure-share-of-voice-in-chatgpt-and-perplexity

73% of B2B buyers now use AI tools in their vendor research process, according to Averi's March 2026 analysis of 680 million citations. AI-referred visitors convert at 4.4x the rate of traditional organic traffic, according to Semrush's June 2025 study. Despite both figures, most marketers still measure a single platform. A brand performing well in ChatGPT can be simultaneously invisible in AI Overviews, Perplexity, and AI Mode.

## Key takeaways

- AI SOV measures how often your brand appears in AI-generated answers relative to competitors
- A single-platform SOV score misses the full picture across ChatGPT, Perplexity, AI Overviews, and AI Mode
- Only 22% of marketers track AI visibility, per Loganix's 2026 analysis
- AI-referred sessions convert at 4.4x the rate of organic traffic, making AI SOV a revenue metric

When we pull AI visibility data for a new client at FirstMotion, the first thing we look at is the gap between platforms. A brand with strong ChatGPT presence is often entirely absent from AI Overviews and Perplexity for the same prompts: different retrieval models, different citation patterns, different gaps. Our ContextualJourney™ platform is built specifically to surface those gaps across all four engines in a single measurement cycle.

## What AI share of voice measures and why it matters

AI platforms now function as answer engines rather than link directories, synthesising recommendations instead of ranking results. Organic search metrics don't capture presence in AI-generated responses: a brand can rank on page one and still be invisible when buyers ask AI assistants for vendor recommendations. 64% of marketing leaders say they're unsure how to measure success in AI search today, according to Yext's October 2025 study of 6.8 million citations.

AI SOV differs from AI visibility score, though the two are related. Visibility score measures how often a brand appears across all queries in a prompt set. SOV adds the competitive dimension: of all the brands mentioned in those responses, what share of total mentions belongs to this brand. The two metrics answer different questions: visibility tells you your reach, SOV tells you your competitive weight within it.

### How AI models process brand signals

AI models don't score content by links and authority metrics the way search engines do. They weigh the breadth and consistency of brand representation across sources they've learned to retrieve from. The result is that a brand's AI share of category responses reflects its entire digital footprint. AI SOV = (Brand citations / Total category citations) × 100.

To calculate AI SOV accurately, the measurement needs to reflect the signals AI models actually use, rather than SEO proxies. Domain rating (DR) has limited predictive value for a brand's AI share of category responses. Ahrefs' analysis of 75,000 brands found branded web mentions correlate with AI Overview visibility at r=0.664, compared to r=0.218 for backlinks. Earned presence across the web outweighs technical site metrics by roughly 3 to 1.

Brands in the top 25% for web mentions earn up to 10x more mentions in AI Overviews than the next quartile, according to Ahrefs' analysis of 75,000 brands. AI citations concentrate at the top of the mentioned distribution. Brands with the strongest independent third-party presence dominate category responses in AI-generated answers, regardless of domain authority.

### How AI search engines measure brand presence

AI search engines don't rank brands the way traditional search engines rank pages. They synthesise answers from sources they've learned to treat as authoritative: editorial coverage, brand mentions across trusted publications, review platform presence, and consistency of representation across independent sources. Earned media composition determines which brands dominate a given category in AI-generated answers.

The practical implication is that a brand's AI share of voice is downstream of its earned media strategy. Brands that invest in original research, earned editorial coverage, and expert-attributed content build the external signal density AI systems retrieve from. Those that rely primarily on owned content and technical SEO optimization often find their organic search performance doesn't translate into AI citations.

## Why AI visibility differs by platform

The most common measurement mistake in AI SOV tracking is treating ChatGPT data as representative of AI search overall. Each major AI platform sources its answers differently, cites from different content types, and weights brand signals differently. ChatGPT, Perplexity, Google AI Overviews, and Google AI Mode each require separate measurement.

| Platform | Citation behaviour | Key SOV driver |
|---|---|---|
| ChatGPT | Synthesises from training data and web retrieval; broad topic coverage | Brand mention density across editorial and review sources |
| Perplexity | Real-time web retrieval; favours recent, structured content | Current editorial coverage and recency of indexed content |
| Google AI Overviews | Appears in 20%+ of Google searches; draws from indexed top-10 results | Traditional SEO combined with E-E-A-T signals |
| Google AI Mode | Deep multi-step reasoning; pulls from broad web sources | Topical authority across multiple related content clusters |

Measuring SOV on only one of these platforms gives a partial picture that can produce misleading conclusions. A brand that invested in earned media coverage across trade publications may perform well in ChatGPT and Perplexity but underperform in AI Overviews if its traditional SEO lags. The reverse is equally common: strong Google rankings don't automatically translate to strong AI SOV in ChatGPT or Perplexity.

### Google AI Overviews and AI Mode

Google AI Overviews and AI Mode are the two most commonly absent from AI SOV measurement programmes, despite being the AI surfaces most buyers encounter first. AI Overviews now appear in more than 20% of all Google searches, according to SparkToro's 2026 analysis. When they appear, the zero-click rate rises to approximately 83% according to SparkToro and Similarweb data, meaning buyers who see an AI Overview response are highly unlikely to continue to the organic results below.

AI Mode requires separate tracking from AI Overviews: the two surfaces use different retrieval mechanisms and produce different citation patterns. A brand with strong AI Overview presence may have weak AI Mode visibility, and vice versa. Treating Google's AI surfaces as a single measurement point is as misleading as treating all paid media channels as one number.

## Measuring AI share of voice across AI platforms

### Building your prompt set

AI SOV measurement starts with a defined set of category-relevant prompts reflecting real user intent: the questions buyers actually ask AI assistants when researching vendors in the brand's category. A prompt set of 20 to 50 category-relevant queries gives a statistically meaningful AI SOV baseline. The same prompt set must be run consistently across all four platforms to produce comparable figures and a competitor comparison that holds up across engines.

The brand accounts for a share of all mentions generated in those responses. Running fewer than 20 prompts produces results too volatile to act on. A competitor comparison run across different prompts per platform produces numbers that can't be compared, a common error that makes multi-platform SOV look inconsistent when the inconsistency is methodological.

### Calculating your AI visibility score

AI visibility score is the percentage of prompts in the set that produce at least one mention of the brand, per platform. AI Visibility Score = (Prompts mentioning brand / Total prompts in set) × 100. This is the simpler metric to start with before calculating the brand's AI share across the full competitive set.

Tracking AI mentions per prompt per platform identifies which queries the brand is absent from and which it dominates. A brand appearing in 12 of 30 tracked prompts on ChatGPT has a visibility score of 40%. If direct competitors appear in 25 of the same 30 prompts, the gap in the brand's AI share is clear without the full SOV calculation.

### Measuring AI SOV per platform

For each prompt and platform combination, record which brands appear and at what position in the AI generated responses. AI SOV per platform = (Brand mentions on that platform / Total tracked brand mentions on that platform) × 100. Running this across 30 prompts and four platforms gives 120 data points per measurement cycle.

The per-platform breakdown is more useful than the composite average. It shows exactly where AI-generated content is giving competitors an advantage and where the brand's AI share is strongest. A brand with 35% ChatGPT SOV and 2% AI Overviews SOV has a different optimization brief from one sitting at 15% on every platform.

## Tracking AI brand mentions and sentiment

### Reading AI generated answers for brand data

AI SOV measurement captures brand appearance. How the brand is described in those answers is a separate question entirely, and both matter commercially. A brand with 35% SOV consistently described as "expensive" or "complex to implement" has a different problem from a brand with 35% SOV described as "the leading platform for mid-market B2B SaaS."

Tracking AI brand mentions requires reading the actual responses. Recording which brands appear is only half the picture, so for each prompt where the brand appears, note:

- Position (first, second, or third brand mentioned)
- Framing (recommended, mentioned neutrally, or flagged with caveats)
- Attributes (the specific capabilities or characteristics the AI assigns to the brand)

Structured data, review platform ratings, and schema-marked product descriptions shape what AI talks about when it mentions a brand. Positive reviews on G2 and Capterra contribute directly to the attributes of AI systems surface.

### What AI answers reveal about brand positioning

The responses generated by AI platforms for category queries contain the AI's synthesised description of the brand, and that description is what reaches the target audience during active vendor research. Brands losing ground in AI-generated answers often discover it through how AI talks about them: vague descriptions, outdated positioning, or association with problems.

Brand sentiment in AI answers reflects what AI systems have retrieved and learned from the brand's digital footprint. If a brand's content and third-party coverage emphasise one product feature heavily, AI-generated answers will often reflect that emphasis across the entire prompt set. Our [earned media guide](/insights/how-earned-media-and-brand-mentions-drive-ai-citations) covers how to build the citation footprint across the publications AI engines weigh most heavily.

## AI referral traffic and conversion data

AI referred traffic grew 527% year-over-year in the first five months of 2025, according to Previsible's AI Traffic Report. AI SOV is the leading indicator; AI referred traffic is the commercial output it drives. AI-referred visitors arrive pre-qualified: the AI assistant has already described the brand and the buyer has chosen to investigate, which is what produces the 4.4x conversion premium Semrush identified across 500+ B2B topics.

Tracking AI referred traffic in Google Analytics requires identifying the AI platform referral sources:

- chat.openai.com
- perplexity.ai
- claude.ai
- Google AI properties (including AI Overviews and AI Mode referrals)

Free tools like GA4's channel grouping configuration separate AI referred sessions from direct traffic, which standard setups lump together and understate. A brand with 30 ChatGPT-referred sessions per month converting at 15% is commercially more significant than one with 300 organic sessions converting at 1.5%.

## AI SOV benchmarks and tracking cadence

There is no universal AI SOV benchmark because category competitiveness varies significantly. In highly competitive categories with many active players, a 30% AI SOV represents strong positioning. Track SOV relative to three to five direct competitors rather than every brand that ever appears in category responses: the competitive subset gives more actionable signal than a broad category average.

| AI SOV range | Interpretation |
|---|---|
| 0–5% | Near-invisible in the category |
| 6–15% | Emerging presence; competitors dominate |
| 16–30% | Competitive presence; part of the conversation |
| 31–50% | Strong AI visibility; consistent reference for category queries |
| 50%+ | Category leadership; majority of AI answers include the brand |

Track the full prompt set monthly. Run high-priority prompts (the highest-intent buying queries) weekly, since AI model updates can shift citation patterns quickly. Monthly audits catch shifts in AI SOV before they compound. Monthly tracking of AI SOV also reveals if content and earned media activity from the previous period is translating into improved visibility.

## How to improve AI share of voice

Improving AI SOV requires generative engine optimisation: structuring content and building the external brand signals that improve how AI systems synthesise brand data. The content types that produce the strongest AI SOV gains are detailed how-to guides with direct answers, and original research with primary data: formats that give AI models information gain they can't reconstruct from other sources. A brand's content and earned coverage together determine what AI systems learn to say about it.

Our [entity authority guide](/insights/why-entity-authority-is-the-foundation-of-ai-search-visibility) covers how AI systems learn to associate a brand with its category. The [digital PR guide](/insights/digital-pr-for-ai-search-the-complete-strategy-guide) addresses the earned media programme that builds the distribution footprint AI engines retrieve from. Structured data implementation, review platform presence, and consistent expert-attributed content all contribute to AI brand mentions improving over time.

## If you're not measuring AI SOV, here's where to start

Start with a prompt set of 20 to 30 prompts: the entry point of the 20 to 50 range that gives a meaningful AI SOV baseline. Run them across ChatGPT, Perplexity, Google AI Mode, and Google AI Overviews. Record which brands appear in each response. Calculate your visibility score per platform. That's the baseline.

The gaps become visible immediately, and the gaps are where the work is. A brand that's invisible in Perplexity but present in ChatGPT has a different programme to build from one that's invisible everywhere. Measurement is what tells you which problem you have.

[Book a free call](/contact) to pull your brand's AI SOV baseline across all four platforms and identify which prompts your direct competitors are winning that you're not.

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# Building a Context-First AI Search Optimisation Strategy

Source: https://firstmotion.com/insights/building-a-context-first-ai-search-optimisation-strategy

**Author:** Alex Price
**Date:** September 15, 2026

68% of Google searches in early 2026 ended without a click, according to SparkToro's 2026 zero-click study. For queries where AI Overviews appear, that rate rises to approximately 83%. Fewer than 10% of AI-cited sources rank in Google's top 10 for the same query, according to eMarketer's 2026 GEO report. Brands earning visibility in this environment are the ones AI engines have enough context to cite.

The question we hear most often from B2B brands at FirstMotion isn't "how do we rank higher?" It's "why does ChatGPT recommend our competitors and not us?" The answer is almost always the same: the AI doesn't have enough signals to cite the brand confidently. Our ContextualJourney™ platform identifies exactly which signals are missing before we recommend a single change.

## Why generative AI changes what brands need to do

Generative AI has changed the structure of search, not just its interface. When a buyer asks ChatGPT, Google Gemini, or Perplexity a vendor research question, the AI synthesises an answer from sources it treats as authoritative. McKinsey's August 2025 survey found 44% treat AI as their primary research source, ahead of traditional search at 31%.

88.1% of businesses are completely absent from AI search discovery, according to Omni Eclipse's March 2026 audit of 356 businesses. For local businesses the picture is starker: ZipTie research found 98.8% are completely invisible in AI-generated recommendations. Of businesses that do rank on Google's first page, only 23% also appear in ChatGPT, according to the same Omni Eclipse audit.

Gartner projects traditional search volume will drop 25% by 2026. Semrush projects AI search will surpass traditional organic search as a source of conversion-driving traffic by 2028. Access to AI-generated answers is where the earliest buyer research now happens. Most brands aren't in the room.

### Google AI Overviews and zero-click search

AI Overviews now appear in more than 20% of all Google searches, per SparkToro's 2026 analysis, sitting above organic links before any result. Traditional rankings no longer reliably predict AI citation probability. When AI Overviews appear, the zero-click rate rises to approximately 83% (SparkToro and Similarweb data). In 2024, 59.7% of EU searches ended without a click (SparkToro and Datos), rising to 68% in the US by early 2026.

Brands cited in AI Overviews earn 35% more organic clicks and 91% more paid clicks than uncited competitors, according to Seer Interactive. AI Overviews reduce click-through rates for position-one organic results by around 60%, according to Ahrefs' February 2026 analysis. Semrush's AI Search Study found AI-driven traffic achieves 4.4x higher conversion rates than traditional organic search.

## What a context-first GEO strategy means

Generative engine optimization (GEO) focuses on earning inclusion in AI-generated responses rather than on ranking in a list of links. It's sometimes grouped with related concepts: answer engine optimization (AEO) and large language model optimisation. A context-first approach evaluates what AI systems actually need:

- Semantic depth and conversational intent
- Verifiable claims with named source attribution
- Consistent brand representation across authoritative third-party sources
- Content structure that AI engines can extract directly

AI responses draw from a fundamentally different set of signals from traditional rankings. AI models analyse semantic relationships between ideas, not keyword density. Traditional SEO tools measure what AI engines ignore.

### How AI models evaluate content for citations

Large language models retrieve from sources that carry the signals of authoritative information, not by ranking pages the way search engines do. Princeton's GEO study (Aggarwal et al., KDD 2024) found that adding verifiable statistics, citing credible sources, and including expert quotations increased AI visibility by up to 40%. Keyword stuffing produced negligible or negative effects.

AI systems prefer semantically rich headers, logical content hierarchy, and sections with a clear defined topic. Each section should be independently extractable as a direct answer to a specific question. 44.2% of all LLM citations come from the first 30% of a page, according to Zyppy's 2025 analysis. In practice, where an answer appears on the page matters as much as the answer itself.

### E-E-A-T signals and their role in GEO

E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) originated as Google's framework for evaluating content quality. The Experience component values personal experience and first-hand knowledge of a subject, not just formal credentials. In GEO, E-E-A-T functions as the primary credibility signal AI engines use to determine citation worthiness.

Strong E-E-A-T signals for GEO require:

- Named author attribution with verifiable credentials
- Primary source citations within content
- Consistent expert representation across third-party publications

Updating stale content improves E-E-A-T and credibility. Over 70% of pages cited by ChatGPT were updated within the past 12 months, according to AirOps research cited by eMarketer. Expert-led content with named attribution is vital for maintaining online authority signals.

## A context-first approach to AI search optimisation

### Making content citation worthy

Citation-worthy content gives AI engines something specific to extract and attribute. General claims without evidence, promotional language, and self-referential brand narratives all read as low-credibility to language models trained on human editorial text. Content creation for GEO starts with buyer questions and builds outward from the direct answer.

Content that introduces original frameworks, proprietary research, and unique data attracts AI citations because it provides information gain: context AI systems can't reconstruct from other sources. Rewriting content sections to lead with the direct answer is one of the highest-impact structural changes for AI citation performance. Each section should have a clear topic and takeaway that AI systems can extract independently.

### Content formats that earn AI citations

Not all content formats produce equal AI citation rates. The formats that perform best are directly extractable, carry verifiable claims, and match the intent pattern of the queries AI engines field most often:

| Content format | Citation value | Why AI engines prefer it |
|---|---|---|
| FAQ sections with direct answers | Very high | Directly matches query-response architecture of AI answers |
| Original research and benchmark data | Very high | Provides information gain no other source replicates |
| Step-by-step guides | High | Matches instructional query patterns |
| Expert-led content with named attribution | High | Satisfies E-E-A-T credibility signals |
| Comparison and best-of lists | High | Matches evaluation-stage queries |
| Structured definitions and explainers | Medium | Useful for awareness queries, lower information gain |
| Brand feature pages | Low | Self-referential, low independent credibility |

Multimodal content (combining text with relevant images, video, and structured data) improves AI citation probability. Reddit and LinkedIn were the two most cited domains across ChatGPT, Perplexity, and Google AI Mode as of Semrush's January 2026 data.

### Schema markup and structured data for AI search

Structured data helps AI understand content for better indexing and retrieval. Pages with FAQ schema and inline citations are weighted approximately 40% higher in ChatGPT source selection than pages without these elements, according to Authoritas 2025 research. Pages with three or more schema types carry a 13% higher LLM citation probability, according to the same Authoritas research.

Proper HTML hierarchy aids AI in content navigation. Semantically rich headers signal what each section covers, helping AI systems map the topical scope of a page before selecting which passages to extract. The highest-priority schema types for GEO are FAQ, HowTo, Article, and Organisation markup.

### Providing additional context for AI retrieval systems

The technical elements of a GEO strategy extend beyond schema markup into the signals that help AI systems understand who a brand is and what it represents. Entity disambiguation, llms.txt guidance, and sameAs markup in Organisation schema all give AI retrieval systems the additional context they need to cite a brand accurately rather than confuse it with competitors.

Auditing and updating existing content is often the fastest GEO win available. Rewriting content sections to lead with the direct answer improves citation extraction without requiring new content production. Each content section should have a clear topic and takeaway that AI systems can extract independently.

## Building brand presence for AI discovery

Language models are trained on vast bodies of text from the open web: the totality of what the web says about a brand, not just what the brand publishes about itself. Brand authority in AI search is built through consistent, accurate representation across credible external sources. Unlinked brand mentions build authority in a GEO strategy: AI systems evaluate it on mention frequency and context, not just hyperlinks.

### Building authority signals across multiple sources

Brand authority for AI discovery requires consistent representation across the specific sources AI engines retrieve from most frequently. For B2B brands, the highest-priority external sources are:

- Industry trade publications and analyst blogs in the brand's vertical
- Independent review platforms (G2, Capterra, TrustRadius)
- Reddit and LinkedIn (most cited domains across major AI engines per Semrush January 2026)
- Academic research and data publications AI systems treat as high-credibility references
- Press coverage in Tier-1 publications that AI engines weight as editorially verified

A brand's consistent positioning across these sources strengthens AI confidence in citing it. Inconsistent brand descriptions, contradictory claims, and thin external presence all reduce citation probability regardless of how well the brand's owned content performs in traditional search.

### Digital marketing and the shift to AI-powered search

Content marketing that earns AI citations requires different inputs from content marketing built for keyword rankings. Generative AI responses are personalised to each user's query and conversational history. Citation in an AI-generated answer reaches a buyer in a more considered moment than a ranked link they might scroll past.

Digital marketing teams building for AI discovery need to treat GEO as a separate discipline from traditional SEO, not an extension of it. eMarketer's 2026 GEO report confirmed fewer than 10% of AI-cited sources rank in Google's top 10 for the same query. Our [earned media and AI citations guide](/insights/how-earned-media-and-brand-mentions-drive-ai-citations) and [entity authority guide](/insights/why-entity-authority-is-the-foundation-of-ai-search-visibility) cover two of the disciplines that build the external citation footprint AI engines retrieve from.

### Brand mentions and AI visibility

Citations in respected publications carry more weight than mentions in low-authority sources; AI systems actively prefer the former. Cultural and regional relevance builds trust signals AI systems recognise as credibility markers. A brand appearing across publications serving a specific vertical builds stronger entity associations than one with generic broad coverage.

## Measuring AI visibility and tracking performance

AI visibility tracking measures how often a brand appears in AI-generated responses for its target queries. A visibility score tracks how often a brand appears across ChatGPT, Gemini, Perplexity, Copilot, and AI Overviews. This is distinct from traditional analytics: Google Analytics and standard keyword tracking tools don't capture AI citation performance directly.

The core GEO measurement framework tracks five metrics:

| Metric | What it measures | Why it matters |
|---|---|---|
| AI citation rate | How often the brand appears in AI answers for target prompts | Direct measure of AI search visibility |
| AI share of voice | Brand citations as a percentage of category citations | Competitive position in AI search |
| Visibility score | Frequency of brand appearance across AI platforms | Breadth of AI search presence |
| AI referral traffic | Sessions arriving from AI platform referrals | Commercial impact of AI citations |
| Brand mention sentiment | How accurately AI describes the brand | Quality control for AI citations |

AI-referred sessions grew 527% year-over-year in the first five months of 2025, according to Previsible's AI Traffic Report. The channel is growing fast enough that weekly monitoring is now standard.

## Most brands we audit tell us the same thing

They rank. They've invested in content, domain authority, and technical SEO. Then we run the citation audit and they see it: present in Google, invisible in the AI-generated answers their buyers are reading first. The gap between traditional search performance and AI citation visibility is consistent, measurable, and closable.

[Book a free GEO audit](/contact) to see exactly where your brand stands across ChatGPT, Perplexity, Google AI Overviews, and Google AI Mode.

---

# How to Evaluate a GEO Agency for B2B SaaS

Source: https://firstmotion.com/insights/how-to-evaluate-a-geo-agency-for-b2b-saas

Ask a prospective GEO agency three things before you sign anything: how they specifically optimise content for ChatGPT, Gemini, Claude, and Perplexity rather than Google alone; whether they can show citation-rate movement on a comparable account; and what they report on beyond keyword position. The answers separate a genuine AI search specialist from a traditional SEO team that has added "GEO" to its service list without changing how it works.

That distinction matters more than it did a year ago. Every SEO agency now claims some form of AI search capability, and most of the language sounds identical from one pitch to the next. The only way to tell them apart is to ask specific, technical questions and see whether the answers hold up.

## Key takeaways

- Vet a GEO agency on methodology, not vocabulary: ask how they track citations across individual AI platforms, not whether they "do GEO"
- A specialist should report on citation rate, AI share of voice, and sentiment as core metrics, not as an add-on slide to a traditional SEO report
- Genuine GEO work involves restructuring content for how LLMs retrieve and synthesise passages, which looks different from writing for rankings
- Ask to see one full reporting cycle, not a single screenshot of a favourable ChatGPT answer
- Budget should reflect technical content mapping and structured measurement work, not a relabelled link-building retainer

## Evaluating GEO agency expertise

Most agency conversations stay at the level of "we help brands show up in AI search." That claim tells you nothing. The useful conversation happens one level down, in the specifics of how the work actually gets done. Bring a list of pointed questions into the first call and pay attention to how concretely they're answered.

**Questions to ask about their platform-level methodology:**

- Which AI platforms do you track separately, and how does your approach differ between Google AI Overviews, AI Mode, ChatGPT, Perplexity, Gemini, and Claude? These surfaces don't behave the same way or draw on the same sources, so a single answer covering all of them is a warning sign.
- What tooling do you use to monitor citations and prompts, and can we see a live dashboard rather than a static report? Ask them to name the platform and show real output, not a mock-up.
- How do you decide which prompts to track for our brand, and how often is that prompt set revisited as buyer language and model behaviour shift?
- Walk me through how you'd diagnose why we're cited for one query but not a near-identical one. This tests whether they actually investigate retrieval behaviour or just report on outcomes.

**Questions to ask about evidence and case studies:**

- Can you show citation-rate or AI share-of-voice movement for a client over a defined period, ideally in B2B SaaS or a comparable sales-cycle category?
- What did the baseline look like before you started, and what specifically changed to move it? An agency that can't separate its own work from platform-wide shifts hasn't been measuring rigorously.
- Have you worked with a brand that saw no meaningful movement? How did you diagnose that and what did you recommend? Anyone claiming universal success either hasn't tracked outcomes closely enough or isn't being straight with you.

**Questions to ask about how they measure performance beyond keywords:**

- What does your standard monthly report include, and where do citation rate, share of voice, and sentiment sit in it, headline metrics or a footnote?
- How do you connect AI visibility to pipeline, not just impressions? Full attribution is still difficult across the industry, but a competent partner should have a defensible way of linking AI referral traffic to downstream engagement.
- If AI Overviews or ChatGPT change their retrieval behaviour next month, how would you know, and how quickly would we hear about it?

The agencies worth shortlisting answer these in specifics: named tools, named metrics, named platforms, real numbers. The ones to be wary of answer in generalities and pivot back to traditional SEO deliverables within a few sentences. For a broader, more exhaustive checklist covering the full range of selection criteria, our [GEO agency evaluation scorecard](/insights/how-to-choose-a-b2b-saas-geo-ai-seo-agency-with-evaluation-scorecard-download) is a useful companion to this piece; this article focuses specifically on the questions to ask in the room.

## Distinguishing specialised AI search partners from legacy SEO firms

The market has moved quickly enough that almost every SEO agency now offers some version of a GEO service. Relabelling isn't inherently dishonest, plenty of the underlying skills genuinely transfer, but it does mean the label alone tells you nothing about capability. A handful of concrete signals separate a genuine specialist from a firm that has updated its website copy.

**Look for a dedicated, answer-first content methodology.** A specialist writes and restructures content specifically for how LLMs retrieve and synthesise information: self-contained passages of roughly 120 to 180 words, direct comparison content, and integration or use-case pages built to be extracted and paraphrased, not just crawled and ranked. A legacy firm applying old habits to a new label will still be optimising primarily for on-page keyword density and internal linking, with AI framing added on top rather than built in.

**Check whether AI-specific metrics are structural to their reporting, not bolted on.** Ask to see a real report. If citation rate, mention rate, and AI share of voice appear as a short section wedged between organic traffic and backlink counts, the agency's actual operating model hasn't changed, only its terminology has. A specialist treats these as the primary KPIs the engagement is judged against, alongside a clear point of view on [which GEO metrics actually matter](/insights/the-kpis-and-metrics-that-actually-matter-for-a-geo-campaign) for a B2B sales cycle.

**Ask how they think about expertise in a field that's still forming.** No agency has definitive answers on a discipline this young; model behaviour shifts monthly. What matters is whether they can describe a clear, testable methodology and a track record of adapting it, rather than either overclaiming certainty or falling back on generic SEO advice when pressed. We've written more on [what genuine GEO expertise looks like](/insights/is-it-really-possible-to-be-a-geo-ai-search-expert) in a market this new, and it's worth reading before you sit down with any shortlist.

**Watch how they talk about your buyers.** Genuine GEO strategy is built around how a real decision-making unit searches and evaluates software, not a generic persona template. If prompt strategy and content planning aren't grounded in your specific buying committee and sales cycle, the "AI search expertise" is more surface than substance.

## Budgeting for AI search strategy

Investment in GEO should track the actual shape of the work: prompt mapping, technical content restructuring for retrieval, entity and third-party presence building, and ongoing platform-level measurement, rather than a traditional link-building retainer with a new name on the invoice. A partner charging GEO rates for SEO-era deliverables, content calendars and backlink outreach with a light AI framing, is not pricing for the work described above.

Budgeting in detail, including how to scope spend against company stage and how to structure a contract, is its own subject. Our companion guide on [selecting and budgeting for a B2B SaaS GEO agency](/insights/how-to-select-and-budget-for-a-b2b-saas-geo-agency) covers that framework in full; use the questions in this article to pressure-test whoever you're evaluating against it.

Choosing a GEO partner comes down to whether their methodology, evidence, and reporting hold up under specific questions, not whether their pitch deck uses the right terminology. Ask the questions above directly, ask for real numbers and a real dashboard, and treat vague answers as the signal they are.

---

# How to Implement a Generative Engine Optimisation Strategy for B2B SaaS

Source: https://firstmotion.com/insights/how-to-implement-a-generative-engine-optimisation-strategy-for-b2b-saas

Brand entity strength affects AI search visibility because models like ChatGPT, Perplexity, and Google AI Mode recommend vendors they can confidently identify and associate with a specific software category, not vendors with the strongest keyword rankings. A B2B SaaS brand with consistent naming, a clear category definition, and corroboration from third-party sources gets surfaced in AI-generated comparisons and recommendations more reliably than a brand with better content but a fragmented online identity.

This guide sets out how to implement a GEO strategy in practice: what changes when buyers research software through AI tools instead of search engines, the specific technical and content signals that drive citation and recommendation behaviour, and a short framework for reporting GEO performance to a CFO or board in terms they already understand.

## Key takeaways

- AI models select sources by recognising entities first and evaluating content second, which means brand clarity and consistency now do work that keyword optimisation used to do alone
- Structured data doesn't directly cause AI citations, but Organisation schema with complete sameAs references materially improves entity recognition, which does
- Topical depth and content that answers real buyer comparison questions increases citation likelihood more than volume of published content
- A practical GEO implementation sequence starts with entity consistency, then schema, then topical coverage, then corroboration, then measurement
- GEO performance should be reported using the same commercial framing as SEO: share of AI-generated answers and pipeline influence, not raw visibility counts

## Why AI search changes how B2B buyers find software

Traditional SEO optimises for a ranking system: crawl, index, rank, click. Generative engines work differently. A model ingests a query, retrieves and synthesises information from sources it already recognises and trusts, and produces a direct answer that may or may not include a link. The buyer never sees a list of ten blue links to evaluate independently, they see a synthesised recommendation that has already filtered the field.

That shift changes where B2B buying decisions get shaped. A buyer researching a category increasingly asks ChatGPT or Perplexity to shortlist vendors, summarise pros and cons, or draft evaluation criteria before they visit a single vendor website. If your brand isn't part of the source set a model draws from when it forms that answer, you're excluded from the shortlist before a prospect ever reaches your site, regardless of how well that site ranks in Google. The mechanics of this shift are covered in more depth in our [comparison of GEO and traditional SEO](/insights/geo-vs-seo-whats-the-difference); the practical implication for marketing teams is that visibility work now has to happen upstream of the click.

## Building the signals that drive AI citation and recommendation

This is where implementation actually happens, and it's also where many B2B SaaS teams misallocate effort. Strong content alone doesn't produce AI citations if the underlying entity signals are weak. Three areas do the work: brand entity strength, structured data, and topical authority. Each is addressed below, followed by a sequence for implementing them in order.

**Brand entity strength.** AI models associate a domain with a software category the same way they associate any entity with a topic: through repeated, consistent signals across multiple sources they already trust. A brand whose name, category description, and positioning vary across its own website, LinkedIn page, G2 listing, and Crunchbase profile creates disambiguation problems a model has to resolve before it can cite that brand confidently. Models typically resolve ambiguity by omission rather than by guessing, which means inconsistency doesn't just weaken a citation, it removes the brand from consideration entirely. We cover the mechanics of this in detail in [our piece on entity authority as the foundation of AI search visibility](/insights/why-entity-authority-is-the-foundation-of-ai-search-visibility), including why inconsistent information is a common and readily fixable cause of missing AI citations.

**Structured data.** Schema markup gives AI systems machine-readable context for a page, but the evidence on what specifically moves citation behaviour is more precise than "add schema and see results." As we've documented in [our analysis of the Ahrefs schema markup study](/insights/does-schema-markup-increase-generative-search-visibility), adding page-level JSON-LD to content that already has an AI citation baseline produced no measurable citation uplift. What does move the needle is Organisation schema with complete sameAs references linking your website entity to your Wikipedia page, Wikidata entry, and LinkedIn profile. That connection is what allows a model to resolve your brand to a known entity rather than an ambiguous text string, and it's a materially different exercise to bolting FAQ or Article schema onto existing pages.

**Topical authority and content depth.** Once a model can identify and trust your brand as an entity, it evaluates whether your content actually answers the query. Depth and specificity on a defined set of topics increases citation likelihood more than breadth. A page that directly answers a comparison question buyers are asking an AI tool, with a clear direct answer near the top, structured sections, and specific detail rather than generic positioning, is easier for a model to extract and cite than a page written primarily to rank for a keyword.

A practical implementation sequence, in order:

1. **Audit entity consistency.** Pull your brand's name, category description, and service list from your website, LinkedIn, G2, Capterra, Crunchbase, and any press coverage. Flag every discrepancy and correct them against a single canonical description.
2. **Deploy Organisation schema sitewide** with complete sameAs references to Wikipedia, Wikidata, and LinkedIn before adding any other schema type. This is the highest-leverage technical step for entity recognition.
3. **Layer in Article and Person schema** for published content and named authors, keeping the markup in sync with what's actually rendered on the page, since AI systems compare the two and mismatches reduce confidence.
4. **Build topical depth around the specific questions buyers ask AI tools**, including direct comparisons, evaluation criteria, and category-defining content, rather than expanding keyword coverage for its own sake.
5. **Earn corroboration from third-party sources**: review platforms, industry publications, and analyst coverage that confirm what your brand claims about itself independently.
6. **Track category and comparison prompts in a visibility tool** such as Peec AI to see which prompts currently cite competitors instead of you, and prioritise the gaps with the clearest buying intent.

## Reporting GEO performance in terms a board already understands

None of the work above earns budget on its own merits unless it's reported in language a CFO or board recognises. The full case for why AI search represents a structural shift in B2B buying behaviour is covered in [our guide to building the business case for AI search investment](/insights/how-to-build-the-marketing-business-case-for-investing-in-ai-search-generative-engine-optimisation); the short version for reporting purposes is to map GEO metrics onto the SEO metrics a board already trusts. Share of AI-generated answers for defined buyer queries stands in for organic rankings. Branded search lift and pipeline sourced from AI-referred sessions stand in for organic traffic and conversions. Framed this way, GEO reads as protecting a growing share of an existing buyer journey rather than as speculative investment in an unproven channel.

Implementing GEO for a B2B SaaS brand is a sequencing problem more than a technical one. Entity consistency and Organisation schema come first because everything else depends on a model being able to identify the brand accurately. Topical depth and corroboration compound that foundation over time rather than replacing it. Teams that treat GEO as a single technical fix, usually schema markup on its own, consistently underperform teams that work through the full sequence in order.

---

# How to Select a Specialized GEO and AI Search Agency for B2B SaaS

Source: https://firstmotion.com/insights/how-to-select-a-specialized-geo-and-ai-search-agency-for-b2b-saas

Selecting a specialised GEO and AI search agency for B2B SaaS comes down to three checks: does the agency have a methodology built for how large language models retrieve and cite content, rather than a relabelled SEO service; can they measure AI visibility with metrics that don't exist in traditional analytics; and can they show B2B SaaS experience specifically, given how different software buying committees are from consumer search behaviour.

Most agencies now claim GEO capability. Far fewer can demonstrate it. This guide sets out what separates the two, and what to verify before signing a contract.

## Key takeaways

- A genuine GEO specialist prioritises visibility in ChatGPT, Gemini, Claude, and Perplexity alongside traditional rankings, not as an afterthought bolted onto an existing SEO retainer
- Ask for named examples of B2B SaaS brands the agency has helped get cited by generative engines, not general AI search commentary
- AI visibility measurement requires different metrics to organic search: mention rate, citation rate, and share of voice within AI-generated responses, tracked per platform
- Content that earns AI citations is structured differently to content that ranks well: answer-first passages, comparison formats, and integration pages perform disproportionately well
- This piece focuses on identifying genuine specialism and measurement rigour; for a full evaluation framework, see our [agency evaluation scorecard guide](/insights/how-to-choose-a-b2b-saas-geo-ai-seo-agency-with-evaluation-scorecard-download)

Software marketing teams are fielding pitches from agencies that added "GEO" to their service list within the past year, often without changing much else about how they work. Distinguishing a genuine specialist from a rebrand is the first and most consequential filter in the selection process, and it's worth spending real time on before comparing pricing or timelines.

## Evaluating agency expertise in AI search and GEO

The clearest signal of genuine GEO expertise is whether an agency built its methodology around how generative engines actually retrieve and synthesise content, or whether it took an existing SEO service and renamed the deliverables. [Legacy SEO agencies](https://www.designrush.com/agency/search-engine-optimization) optimise for ranking position and organic click-through. AI search optimisation optimises for whether a brand gets surfaced, cited, and accurately described inside an AI-generated answer, which is a different retrieval mechanism with different inputs.

Ask direct questions. Does the agency track visibility separately across ChatGPT, Gemini, Claude, and Perplexity, given that these platforms pull from different sources and behave inconsistently with one another? Can they explain, in specific terms, how large language models select and weight source content when constructing an answer? Do they have a defined process for prompt mapping, building out the actual questions your buyers ask at each stage of the decision, rather than treating AI search as a variation on keyword research?

Request named B2B SaaS examples, ideally with some detail on what changed and over what timeframe. Our own view on why this is a genuinely difficult specialism to claim, given how fast the underlying models change, is covered in [our piece on whether AI search expertise is possible to sustain](/insights/is-it-really-possible-to-be-a-geo-ai-search-expert). An agency that acknowledges the field is still forming, while showing a rigorous and evidence-based process, is a more credible partner than one claiming certainty it can't back up.

## Measuring performance in generative engines

Traffic and keyword rankings, the default KPIs for a traditional SEO retainer, don't capture what's happening in AI search. A brand can be cited extensively across AI-generated answers without producing a single trackable referral session, because a user reading a ChatGPT or Perplexity response often never clicks through. Judging a GEO agency by organic traffic alone will miss most of the value they're meant to be delivering.

The metrics that matter instead are AI-specific: mention rate (how often your brand appears across a defined set of buyer-relevant prompts), citation rate (how often that appearance includes a direct citation or link), and share of voice within AI-generated responses relative to named competitors. Each of these needs to be tracked per platform, since a strong presence in Perplexity says very little about performance in Google AI Overviews or ChatGPT, which draw on different indexes and weight sources differently.

A specialist agency should be able to describe, in concrete terms, how they capture this data on an ongoing basis, how frequently they re-run the prompt set, and how they separate AI-referral traffic from generic organic and direct traffic in your existing analytics stack. We've written a more detailed breakdown of the specific KPIs worth tracking, and how to set realistic targets against them, in [our guide to the metrics that actually matter for a GEO campaign](/insights/the-kpis-and-metrics-that-actually-matter-for-a-geo-campaign).

## Optimising content for AI visibility

The content structures that earn AI citations are not the same ones that rank well in traditional search, and an agency's approach to content should reflect that distinction directly. Generative engines retrieve and synthesise information at the passage level, extracting specific sections of a page rather than evaluating the page as a whole. That changes what "good" content looks like.

Answer-first passages, where the direct response to a likely query sits near the top of a section rather than buried under narrative build-up, are consistently easier for AI systems to lift and cite accurately. Structured comparison content, laid out clearly against named alternatives or approaches, performs well because it gives a model an extractable, low-ambiguity answer to a comparison query. Integration pages, which describe specific technical compatibility in concrete terms, tend to be cited often for exactly this reason: they answer a narrow, specific question unambiguously.

An agency worth hiring should be able to walk through how they restructure existing content for this kind of extractability, not just how they produce new content. That includes formatting decisions like section length, heading structure, and where the direct answer sits relative to supporting context, all of which affect how confidently a generative engine can lift a passage and attribute it correctly.

## A short evaluation checklist

Before signing with any agency, ask them to answer the following directly:

1. Which AI platforms do you track visibility on, and how do you measure it differently across each one?
2. Can you name specific B2B SaaS clients and describe what changed in their AI citation footprint?
3. How do you build and maintain a prompt set that reflects our actual buyer journey, not a generic industry list?
4. What does your content restructuring process look like for pages that already rank but aren't being cited by AI systems?
5. How do you report AI referral traffic separately from organic and direct traffic in our existing analytics setup?

An agency that answers all five with specifics, rather than general reassurance, has likely built the capability the market increasingly requires. For a broader roundup of who else is working in this space and how they position themselves, our [review of leading AI search optimisation agencies](/insights/who-are-the-leading-agencies-for-ai-search-optimisation) is worth reading alongside this checklist.

Choosing the right partner here isn't a matter of picking whoever pitches the most confidently. It's a matter of verifying a specific, demonstrable methodology against the way generative engines actually work, and holding any agency, including one that specialises exclusively in this space, to evidence rather than claims.

---

# How to Select and Budget for a B2B SaaS GEO Agency

Source: https://firstmotion.com/insights/how-to-select-and-budget-for-a-b2b-saas-geo-agency

There's no single published rate for Generative Engine Optimisation, and no reliable industry benchmark you can quote to a finance director yet. What determines a sensible GEO budget is a specific set of cost drivers: how many AI platforms you need tracked, how much content needs restructuring or building, and how long you're prepared to run the programme before judging it. Get objectives and budget right before you start evaluating vendors, and the vetting conversation gets a lot shorter.

Most B2B SaaS teams do this backwards. They start by asking agencies for a quote, then try to reverse-engineer what that quote should have bought them. This guide sets out the order that actually works: define what AI search visibility means for your business, size a budget against the work that requires, then use a short set of questions to separate specialists from agencies bolting GEO onto an existing SEO retainer.

## Key takeaways

- Define AI search objectives at the platform, query-type, and competitor level before pricing anything, rather than starting from a vague goal like "improve AI visibility"
- GEO budgets should scale against platform coverage, content scope, and tracking cadence, not a legacy SEO retainer size
- Meaningful movement in AI citation rates typically takes a few months of sustained work, not weeks, and a budget needs to survive that runway
- Vetting comes last: confirm prompt mining ability, B2B SaaS experience, and answer-first content methodology before signing
- A companion checklist exists for the deep technical vetting questions; this guide focuses on the objective-setting and budgeting work that has to happen first

## Set AI search objectives before you talk to any agency

"We want more AI visibility" isn't an objective. It's a direction. Every agency proposal will claim to deliver it, and you'll have no way to tell whether the one you signed actually did, because nothing was specific enough to fail against.

A usable objective names the platform, the query type, and the comparison point. "Appear in ChatGPT and Perplexity answers for security and compliance comparison queries where we currently lose out to [named competitor]" is something a proposal can be scoped against and a report can be measured against. "Improve our AI search presence" is not.

Work through three layers before you price anything:

1. **Platform layer** — which AI surfaces actually matter to your buyers: Google AI Overviews, ChatGPT, Gemini, Claude, Perplexity. Not all of them carry equal weight for every B2B category, and platform choice changes both scope and cost.
2. **Query-type layer** — the shape of the prompts you need to win. Category-definition queries ("what is [category]"), comparison queries ("[you] vs [competitor]"), and shortlist queries ("best [category] for [use case]") each require different content and different tracking.
3. **Competitive layer** — where you're currently invisible against named competitors who are cited. This is the gap that turns "improve visibility" into a specific, trackable target.

A short worksheet makes this concrete:

| Objective element | Weak version | Usable version |
|---|---|---|
| Platform | "AI search" | "ChatGPT and Google AI Overviews" |
| Query type | "Relevant queries" | "Comparison queries against [competitor]" |
| Baseline | "We're not visible" | "Zero citations across 40 tracked prompts" |
| Target | "Get more visible" | "Cited in 12 of 40 tracked prompts within two quarters" |

Once objectives look like the right-hand column, [the KPIs that actually matter for a GEO campaign](/insights/the-kpis-and-metrics-that-actually-matter-for-a-geo-campaign) become the measurement layer sitting on top of them. Citation rate and AI share of voice only mean something once you've defined which prompts you're tracking them against.

## Build a budget around GEO's real cost drivers, not a legacy SEO retainer

Published GEO pricing varies enormously depending on who you ask, and most of the numbers circulating online come from agencies describing their own packages rather than an independent market study. Rather than anchoring to an unverified figure, size your budget against the variables that actually drive GEO cost up or down.

A legacy SEO retainer is usually priced against content volume and link-building effort. GEO work is priced against a different set of deliverables: prompt research and content mapping across platforms, restructuring existing pages into answer-first formats, building comparison and category content that doesn't currently exist, and ongoing citation tracking across however many platforms you defined in the objectives stage. A programme covering four platforms and a full comparison content library costs meaningfully more than one covering a single platform and a handful of priority queries.

| Budget driver | Scales cost up | Scales cost down |
|---|---|---|
| Platform coverage | Tracking and optimising across 4+ AI platforms | Focused on one or two priority platforms |
| Content scope | Building new comparison and category pages from scratch | Restructuring existing high-performing content |
| Tracking cadence | Weekly prompt-level monitoring across competitors | Monthly or quarterly baseline checks |
| Team composition | Dedicated strategist plus technical and content resource | Single consultant managing the full scope |
| Timeline | Compressed timeline with parallel workstreams | Phased rollout across quarters |

Timeline is the driver most B2B teams underbudget for. GEO campaigns rarely show meaningful citation movement inside the first few weeks, because AI platforms re-crawl, re-evaluate, and re-cite content on their own schedule, not yours. A budget sized for a one-quarter sprint and then cancelled if nothing moves in month one is a budget that was never going to prove anything either way. Size the commitment for a few months of sustained work before you evaluate results, and build the reporting cadence to match, so you're watching leading indicators like citation rate move before you look for the commercial outcomes covered in [how to prove the business impact of AI search visibility](/insights/how-to-prove-the-business-impact-of-ai-search-visibility).

If a vendor gives you a number without asking about platform coverage, content scope, or tracking cadence first, that's worth noting. A number that isn't built from your objectives is a guess with a decimal point.

## Vet agencies against your objectives, not a generic checklist

Once objectives and budget are set, vetting is a shorter exercise than most guides make it sound. Ask a handful of pointed questions rather than working through an exhaustive checklist at this stage:

- **Can they show a prompt mining and content mapping process?** Ask how they identify which prompts your buyers actually use, not just which keywords rank in Google. If the answer sounds like traditional keyword research with "AI" added to the label, that's a signal.
- **How do they approach answer-first content and comparison pages?** GEO content needs a different structure from a traditional blog post: direct answers near the top, explicit comparisons, and content that reads well when a language model extracts a passage out of context.
- **Do they have specific B2B SaaS experience?** Long sales cycles, multiple buyer personas, and technical differentiation change what "visibility" needs to mean. An agency whose case studies are all ecommerce or local business work is starting from a different playbook.
- **Will they scope against the objectives you already defined?** A proposal that ignores the platform, query-type, and competitive targets you set in the first stage of this process isn't listening.

This is deliberately a short list. Deep technical vetting, including how to score agencies against a fuller set of criteria, is covered in our [evaluation scorecard for B2B SaaS GEO agencies](/insights/how-to-choose-a-b2b-saas-geo-ai-seo-agency-with-evaluation-scorecard-download), and in our companion guide on [how to evaluate a GEO agency for B2B SaaS](/insights/how-to-evaluate-a-geo-agency-for-b2b-saas), which walks through the full technical vetting checklist in more depth than makes sense to duplicate here.

Objectives, budget, and vetting work in that order for a reason. An agency can only be evaluated against something concrete, and a budget only makes sense once you know what it needs to buy. Skip either step and the vetting conversation turns into guesswork dressed up as due diligence.

---

# Why B2B SaaS Companies Must Invest in Generative Engine Optimisation

Source: https://firstmotion.com/insights/why-b2b-saas-companies-must-invest-in-generative-engine-optimisation

B2B buyers now research vendors inside ChatGPT, Gemini, Perplexity, and Google AI Overviews before they ever land on a company website, and the deciding factor in whether a brand appears in those answers is authority: how clearly and consistently an AI system can identify a company as a trustworthy source on a given topic. That's a different mechanism from traditional search, where a strong backlink profile and keyword-optimised pages could reliably buy a position on page one. For established B2B SaaS companies with real content maturity behind them, building that authority is now a board-level question, not a marketing experiment.

This matters most for marketing leaders and investors trying to work out where growth budget goes next. The shift isn't hypothetical or distant. It's already changing how shortlists get built, how objections get pre-empted, and how much of the buyer journey happens before a sales team is even aware a prospect exists.

## Key takeaways

- B2B buyers increasingly form vendor shortlists and evaluation criteria inside AI tools before visiting a company's website, changing where influence has to happen
- Generative Engine Optimisation (GEO) optimises for citation inside an AI-generated answer, not for a ranking position in a list of links
- Authority signals for AI citation are built from entity clarity, structured data, and topical depth, not raw backlink volume
- The business case has to be argued on buyer-journey influence and competitive exposure, because traditional SEO metrics don't capture AI citation behaviour
- GEO investment makes the most sense for companies with an established content and SEO foundation already in place, not pre-product-market-fit startups

## The business case for GEO

The starting point for any board conversation about GEO is that the buyer journey has already moved, whether or not the marketing budget has followed it. Buyers researching software increasingly ask an AI tool to compare vendors, summarise reviews, or draft an evaluation scorecard before a salesperson is involved. Some of that research still ends in a Google search. A growing share of it doesn't need to.

The difficulty for marketing leaders is that this shift doesn't show up cleanly in the metrics a CFO is used to. Traditional SEO ROI thinking is built around sessions, click-through rate, and keyword rankings that translate fairly directly into pipeline. AI citation doesn't offer that same throughline yet, and a brand can be named favourably inside an AI answer while generating no trackable click at all. That's a real measurement gap, not a reason to wait. The more useful framing for a CFO isn't "what will GEO return," it's "what happens to our pipeline if competitors are the ones being cited when our buyers ask." Positioned that way, GEO reads as risk management for an existing channel, not a speculative new one.

That reframing also changes who owns the conversation internally. A traditional SEO business case sits comfortably inside the marketing function, argued with traffic and conversion data marketing already owns. A GEO business case touches product positioning, competitive strategy, and how the company is represented on third-party platforms it doesn't directly control, which means it belongs in front of the same leadership audience that reviews competitive strategy, not just the marketing budget line. Boards and investors evaluating a SaaS company's growth engine increasingly ask where AI search visibility sits in that picture, because it's a leading indicator of category positioning in a way rankings alone no longer are. For a more detailed walkthrough of building that internal case, see our [guide to building the marketing business case for AI search investment](/insights/how-to-build-the-marketing-business-case-for-investing-in-ai-search-generative-engine-optimisation), which goes deeper into the specific arguments and data sources to use with finance stakeholders.

## GEO vs traditional SEO

Traditional SEO optimises for position: where a page lands in a results list a user still has to click through. GEO optimises for citation: whether a model names, quotes, or recommends a brand when it synthesises an answer, frequently without a link attached at all. That distinction changes what "winning" looks like. A page can rank on page one of Google and still never appear in an AI-generated answer for the exact same query, because the two systems are evaluating different things.

The clearest place that difference shows up is in what counts as an authority signal. A traditional backlink profile rewards volume and domain-level link equity accumulated over years. AI citation behaviour weighs a narrower set of signals more heavily:

| Signal | Traditional SEO weighting | GEO weighting |
|--------|---------------------------|----------------|
| Backlink volume | High: a primary ranking factor | Low on its own: link volume alone doesn't establish entity trust |
| Entity clarity and consistency | Indirect, rarely audited | High: inconsistent brand descriptions across platforms actively suppress citation |
| Structured data (schema) | Supports rich results, not rankings directly | Supports entity recognition, a precondition for citation |
| Topical depth on a narrow subject set | Helpful, one factor among many | High: repeated, consistent coverage builds the entity-topic association models rely on |
| Third-party corroboration (reviews, press, forums) | Contributes to backlink and brand signals | High: independent confirmation is how models validate self-described expertise |

For a fuller, feature-by-feature comparison of how the two disciplines diverge in practice, our [GEO vs SEO explainer](/insights/geo-vs-seo-whats-the-difference) breaks down the tooling, metrics, and tactics side by side.

## Strategic implementation and growth stage

Authority signals for AI citation don't appear overnight, and they don't come from a single campaign. They build from consistent entity information across every platform a brand touches, from structured data that describes the company in machine-readable terms, and from a sustained pattern of publishing on a defined set of topics rather than chasing keyword volume broadly. Our [breakdown of entity authority as the foundation of AI search visibility](/insights/why-entity-authority-is-the-foundation-of-ai-search-visibility) covers the mechanics of how AI systems evaluate clarity, consistency, and corroboration in more depth, and it's worth reading alongside this piece if entity signals are new territory for your team.

The growth-stage question matters because these signals compound, and compounding takes time you don't get back by starting later. GEO investment makes sense for companies that already have a working content and SEO engine, a defined ICP, and some existing organic visibility to extend, not for a pre-product-market-fit startup still working out its positioning. A company still validating what it sells and to whom doesn't yet have a stable entity for AI systems to build trust around, and investment there is premature regardless of budget available.

Once positioning is fixed and a content programme exists, the underlying pattern is fairly consistent across the SaaS companies we work with: entity foundations, topical depth, and third-party corroboration all reinforce one another, and none of them substitutes for the others. A company can publish extensively and still go uncited if its entity signals are inconsistent across platforms. It can have clean structured data and still lack the topical depth that convinces a model it's a specialist rather than a generalist. Treating GEO as a content-volume exercise rather than a foundational trust-building exercise is the most common mistake marketing teams make when they first prioritise it, and it's why every quarter spent without addressing those foundational signals is a quarter competitors spend building an advantage that gets harder to close.

GEO isn't a replacement for SEO, and it isn't a tactic bolted onto an existing content calendar. It's a recognition that the mechanism by which buyers discover and evaluate B2B software has genuinely changed, and that the companies treating AI search visibility as core infrastructure now will be the ones still getting found once it's the default way buyers research.

---

# B2B SaaS Content Strategy for AI Search Engines

Source: https://firstmotion.com/insights/b2b-saas-content-strategy-for-ai-search-engines

87% of B2B software buyers say AI tools are changing how they research software, yet 44% of B2B SaaS companies are currently invisible in AI search. This guide covers why traditional content strategies fail AI search engines, which content formats earn the most AI citations for SaaS brands, how to audit and update existing content for AI search performance, and how to measure AI citation rates alongside traditional search metrics.

B2B software buyers have moved their vendor research into AI tools. G2's 2025 survey of more than 1,000 B2B software buyers found 87% say tools like ChatGPT, Perplexity, and Gemini are changing how they research software. The 6sense 2025 Buyer Experience Report found 94% of B2B buyers used a generative AI tool during their most recent purchase process. A B2B SaaS content strategy built for Google rankings now faces a different audience with different preferences.

## Key takeaways

- 87% of B2B software buyers say AI tools are changing how they research software
- 44% of B2B SaaS companies are currently invisible in AI search
- 25% of B2B buyers say generative AI has overtaken traditional search for vendor research
- When an LLM surfaces a vendor a buyer hadn't considered, 51% go directly to that vendor's website

We rarely see a B2B SaaS brand come to FirstMotion with a content problem. What they have is a distribution problem: content performing well in traditional search, invisible in the AI-generated answers their buyers are now reading first. Our ContextualJourney™ platform maps exactly where that gap sits before we recommend anything.

## Why traditional SaaS content strategy fails AI search engines

Software as a service brands built their content programmes on a clear model: create content that targets buyer keywords, optimise for search engines, earn backlinks, and convert organic traffic into qualified leads. That model still works for traditional search results. For AI search, it doesn't, because AI engines retrieve from sources they've learned to trust, not from pages optimised for keyword match.

Only 40% of B2B marketers have a documented content strategy according to CMI research, and many of those strategies predate AI search as a meaningful channel. The SaaS marketers now earning consistent AI citations built content programmes that serve both audiences: human readers and the AI models that retrieve from their content to form answers.

AI search visits grew from 15.6 billion in Q1 2025 to 27.4 billion in Q1 2026, according to market data cited by Contently. DerivateX's May 2026 study found 44% of B2B SaaS companies are currently invisible in AI search. A SaaS brand absent from AI-generated answers for its core category queries is missing a growing share of early-stage buyer research before those buyers ever reach a website.

### How AI systems evaluate SaaS content

AI systems don't evaluate content the way search engines do. They retrieve from sources that appear trustworthy based on patterns learned during training. For B2B SaaS brands, the signals AI systems recognise as credibility markers are:

- Third-party editorial coverage in industry publications and analyst reports
- Independent review platform presence (G2, Capterra, TrustRadius)
- Named expert attribution with verifiable credentials
- Data and statistics with cited primary sources
- Structured, direct answers to the questions buyers ask AI tools

AI algorithms discount promotional language, self-referential marketing claims, and content that lacks independent verification. 96% of AI Overview citations come from sources with strong E-E-A-T signals, according to Maintouch's August 2026 analysis.

### The gap between SEO strategy and AI visibility

Many B2B SaaS brands have strong Google rankings and near-zero AI citation rates for the same target queries. Tactics that improve search rankings (keyword density, internal linking, backlink acquisition) have limited impact on AI citation rates. A well-optimised SaaS blog post about a category topic might rank on Google's first page and never appear in an AI-generated answer about the same search queries.

The content that earns AI citations is almost always published by third parties. Building a content strategy that earns AI citations means understanding that owned content creates the foundation, but earned coverage in the right publications earns citations and drives conversions from AI-referred traffic.

## Content marketing for B2B SaaS in the AI search era

Content marketing for SaaS companies serves multiple goals: brand awareness, lead generation, customer retention, and building credibility in a category. In the AI search era, it now also needs to serve AI systems as a direct audience. Research from Clutch and Conductor of 450+ marketing professionals found 81% feel positive about content marketing in the era of LLMs, more than 55% expect to increase content output in 2026, and 75% already use AI-powered tools as part of their standard content creation workflow. Among enterprise organisations, that last figure rises to 32%.

SaaS businesses that treat content marketing as a unified discipline, producing high quality content that earns citations across AI platforms while also converting organic traffic, outperform those that treat AI search as a separate channel. Content efforts compound across multiple platforms when the underlying content is structured to be useful to both human readers and AI retrieval systems.

### How to create content that earns AI citations

Earning AI citations requires a different approach from standard content production. The most effective content directly answers the questions potential customers bring to AI tools. How-to content showing how a SaaS product solves specific business goals earns more AI citations than feature-focused pages. Buyers arriving via AI citation already have context; converting them requires different messaging than converting cold organic traffic.

Buyers value content that explains AI concepts without excessive jargon. Content that shows how AI works in practice, rather than leading with technical specifications, earns more citations than product-centric material. Creating templates and pre-built prompts drives user engagement with AI-native products, while case studies showing real-world metrics build the verification trail that builds credibility with AI retrieval systems.

### Using AI tools to build and optimise SaaS content strategy

AI integration in content strategy has moved from experimental to standard. 75% of marketing teams already use AI-powered tools as part of their standard content creation workflow, according to Clutch and Conductor. Content management platforms help SaaS marketing and sales teams manage workflows effectively across multiple contributors, distribution channels, and content formats.

AI tools provide valuable insights into audience behaviour, helping SaaS marketers identify which content types drive user engagement, which topics generate qualified leads, and which digital marketing channels produce paying customers. HubSpot's Prospecting Agent generated nearly twice as many booked meetings for customers compared to the prior year, according to HubSpot's Q4 2025 earnings report.

## Building a B2B SaaS content strategy for AI search

### Defining your target audience for AI search

Defining the target audience for AI search is more granular than defining it for traditional SEO. In traditional search, user intent is proxied by keywords. In AI search, intent is expressed in natural language prompts that reveal buyer journey stage, depth of knowledge, and expected answer format.

For B2B SaaS brands, target audience definition for AI search maps three dimensions:

| Buyer dimension | Traditional SEO focus | AI search focus |
|---|---|---|
| **Job role** | Keyword modifiers (e.g. "for marketers") | Content structured for specific role-based pain points |
| **Buyer journey stage** | Top/mid/bottom of funnel keywords | Prompt patterns at awareness, evaluation, and decision stages |
| **Knowledge level** | Beginner vs advanced content tiers | Direct answers calibrated to assumed expertise |

The ideal customers asking AI tools about SaaS platforms are in evaluation mode. They're comparing options, building shortlists, and looking for reasons to include or exclude specific vendors. Evaluation-stage questions (comparisons, feature breakdowns, ROI frameworks) earn AI citations at higher rates than awareness-stage content.

### Content formats that earn AI citations for SaaS brands

Not all content formats are equally valuable for AI citation. The format hierarchy for B2B SaaS follows from the types of questions buyers ask:

| Content format | AI citation value | Best for |
|---|---|---|
| **Comparison and best-of lists** | Very high | Vendor selection and shortlisting queries |
| **How-to and tutorial content** | High | Implementation and use-case queries |
| **Original research and benchmark reports** | High | Category authority and data-reference queries |
| **Case studies with specific metrics** | High | ROI and proof-point queries |
| **YouTube videos and product demos** | Medium-high | Visual explainer and comparison queries |
| **Definition and explainer content** | Medium | Awareness and education queries |
| **Product feature pages** | Low | Direct branded queries only |
| **Press releases** | Very low | Almost never cited directly |

Developing educational hubs that address the questions SaaS businesses and their buyers have about AI technology captures search traffic from those new to AI integration while also earning citations in AI-generated answers for awareness queries. Buyer anxiety about data privacy, security compliance, and integration complexity creates a specific content opportunity that well-structured educational content addresses directly.

### Digital marketing channels and AI search distribution

Distribution is where many SaaS brands treat content strategy as an afterthought. Producing high quality content and publishing it only on a brand's own site captures a fraction of the AI citation potential. The digital marketing channels that contribute most to AI citation rates for B2B SaaS brands:

- Third-party publications in the brand's vertical
- Independent review platforms (G2, Capterra, TrustRadius)
- LinkedIn for named expert commentary
- Reddit and community forums for conversational mention density
- YouTube videos for visual content citations

SaaS marketing strategies that treat distribution as integral to content production see compounding returns across both traditional search results and AI-generated answers.

## Lead generation and the SaaS content marketing funnel

AI search changes where SaaS lead generation begins. In traditional search, generating leads from content follows a click-through-to-landing-page model. In AI search, the buyer often receives the answer without visiting any website. The commercial impact appears when the AI citation surfaces the brand as a credible reference and the buyer then seeks it out directly.

When an LLM surfaces a vendor a buyer hadn't previously considered, 51% go directly to that vendor's website. Those visitors arrive informed rather than discovering for the first time, changing the conversion context at every stage of the marketing funnel. Converting AI-referred traffic through optimised landing pages (with clear messaging for buyers who already have context) produces higher qualified lead rates than converting cold organic traffic.

### How the marketing funnel changes for AI search

The SaaS content marketing funnel for AI search looks different from the traditional funnel. Each stage requires different content and measurement:

- **Awareness:** AI citations for category and problem-definition queries introduce the brand to potential customers, increasing brand awareness before any website visit
- **Consideration:** AI citations for comparison and evaluation queries position the brand in the shortlist and generate leads from buyers already evaluating options
- **Decision:** AI citations for specific feature, integration, and pricing queries accelerate the final evaluation and reach the right audience at the moment of decision

Loyal customers and existing clients also interact with the customer journey through AI search. When they ask AI tools about integrations or features, a brand's AI citation presence reinforces the relationship and supports customer retention.

### Building a content calendar for AI search and traditional SEO

What a brand publishes on its own site creates the foundation. Third-party publications, review platforms, and community forums are where AI citations actually come from. A content calendar designed for AI search needs to account for both traditional search performance and AI citation performance as separate output metrics.

Keyword tracking alongside AI citation tracking gives SaaS marketers a complete picture of content performance across both channels. A piece ranking in Google but absent from AI-generated answers for the same queries is only half-succeeding. A content calendar that maps each piece to its target prompt patterns produces better AI citation outcomes than one built purely around keyword targets.

## SaaS content analysis and existing content

A content audit is the starting point for any B2B SaaS content strategy built for AI search. Regular SEO content audits improve key metrics like clicks and impressions, and regular content audits align existing content with user behaviour and user intent as both evolve. Sites that completed structured content audits saw organic traffic 67% higher six months post-audit (theStacc, 50 client sites, 2025).

Content updated within 90 days gets cited far more often in AI answers. Staleness is one of the fastest-win areas in any content audit.

### What a SaaS content audit reveals

A content audit for AI search identifies four categories of existing content:

- Content earning traditional search rankings but no AI citations: candidate for reformatting or redistribution
- Content earning both traditional rankings and AI citations: high-value content to protect, expand, and template
- Content underperforming in both channels: candidate for consolidation, update, or removal
- Content gaps where buyers are asking AI tools questions the brand has no published answer for

Content audits also identify keyword cannibalization issues, outdated information that could mislead AI systems, broken links that weaken topical authority signals, and missing meta tags that limit crawl performance. Content anchors (pillar pages that cover a topic in full) drive authority and provide long-term value for both traditional and AI search.

### Updating existing content for AI search

Existing content written for traditional SEO needs specific modifications to improve AI search performance:

- Move the direct answer to each section's central question to the first sentence
- Add named source attribution for every statistic and factual claim
- Include a FAQ section with direct answers to the questions buyers ask AI tools
- Add schema markup (FAQ, HowTo, Article) to help AI systems parse and retrieve the content
- Ensure internal linking connects each piece to pillar content covering the broader topic
- Check and fix broken links that interrupt topical authority signals

## Google Analytics, AI referral tracking and content performance

Google Analytics remains essential for tracking content performance across marketing channels, but it tells an incomplete story once AI enters the mix. Traditional performance metrics (organic sessions, keyword rankings, click-through rates) don't capture AI citation performance. A brand earning zero traditional search traffic for a query it appears in via AI citation is capturing value that standard analytics won't show.

### Key metrics for AI search content performance

The core measurement framework for B2B SaaS AI content strategy:

| Metric | What it measures | Why it matters for SaaS |
|---|---|---|
| **AI citation rate** | How often the brand appears in AI answers for target prompts | Direct measure of AI search visibility |
| **AI share of voice** | Brand citations as a percentage of category citations | Competitive position in AI search |
| **Prompt set coverage** | How many target queries produce a brand citation | Breadth of AI search presence |
| **AI referral traffic** | Sessions arriving from AI platform referrals | Commercial impact of AI citations |
| **Brand mention sentiment** | How accurately AI describes the brand's positioning | Quality control for AI citations |

Chasing vanity metrics (session counts, page views, social shares) produces zero revenue growth if those metrics aren't connected to AI citation rates and commercial outcomes. Google Analytics combined with AI citation tracking shows which content drives organic traffic, which earns AI citations, and which AI-referred sessions convert.

### Gaining deeper insights from content data

Content management platforms and AI-powered tools now provide customer insights and audience behaviour data unavailable through traditional analytics. Machine learning algorithms identify patterns in which content types and digital marketing channels produce AI citations for a specific SaaS brand. That data drives content calendar decisions more accurately than keyword volume alone.

Running a consistent prompt set of 30 to 50 target queries weekly across ChatGPT, Perplexity, Google AI Overviews, and Google AI Mode provides the baseline data for tracking citation rate changes over time. Essential tools for AI citation tracking, including our earned media guide, complement Google Analytics to give SaaS teams a complete picture of content performance across both channels.

## If your SaaS content strategy was built for Google, here's where to start

The SaaS brands earning consistent AI citations aren't necessarily the ones with the largest content libraries. They're the ones that have mapped their content to the specific queries buyers bring to AI tools, built the earned media presence that AI engines treat as credibility signals, and structured their owned content to be extractable as direct answers.

Our topical authority guide and entity authority guide cover the structural foundations. Talk to the FirstMotion team for a free consultation to map your brand's AI citation gaps and build the content strategy that closes them.

Most B2B SaaS brands we audit are performing well in traditional search and near-invisible in the AI-generated answers their buyers are reading first. Our ContextualJourney™ platform maps exactly where those gaps sit before we recommend anything.
</content>

---

# How Earned Media and Brand Mentions Drive AI Citations

Source: https://firstmotion.com/insights/how-earned-media-and-brand-mentions-drive-ai-citations

Muck Rack's May 2026 analysis of 25 million AI citations found earned media accounts for 84%, while paid media accounts for just 0.3%. This guide covers why AI engines structurally prefer earned media over owned content, what the brand mention data shows about AI citation probability, which content formats and publication types earn the most citations, and how to build the earned media programme that moves AI citation rates in 2026.

Earned media accounts for 84% of all AI citations. Muck Rack's May 2026 Generative Pulse study analysed more than 25 million links across ChatGPT, Claude, and Gemini in 17 industries and found the same pattern across three consecutive editions: earned media at 82% to 89%, paid media at just 0.3%. Brands with genuine earned media earn AI citations. Those without are largely absent from AI-generated answers, regardless of how strong their owned content is.

## Key takeaways

- Muck Rack found earned media accounts for 84% of all AI citations
- Brands in the top 25% for web mentions earn 10x more AI citations
- Brand mentions predict AI visibility three times better than backlinks
- Journalism accounts for 27% of AI citations and 49% on time-sensitive queries

We ran an AI citation audit for a B2B software brand last month. Despite solid SEO health, it appeared in AI-generated answers for just two of the fourteen category queries we tracked. Both citations pulled from a year-old TechCrunch piece and a G2 review the brand didn't know existed. Our ContextualJourney™ platform maps exactly where those gaps sit before we recommend anything.

## Earned media for AI citations: why AI engines cite what they cite

AI engines retrieve information from sources they've learned to trust, not through keyword matching. Generative AI tools learn during training which types of sources are reliable and which are self-serving. Third-party pages pass the credibility test because they come from parties with no direct financial interest in the subject. Brand-owned content fails the same test.

AI search engines show systematic bias toward earned media over brand-owned and social content, according to University of Toronto research. The researchers concluded the primary strategy is to dominate earned media to build AI-perceived authority. Fullintel and University of Connecticut research independently found 89% of AI-cited links were earned media and 95% were unpaid.

The pattern across three consecutive editions suggests this is structural, not a model quirk. AI engines treat brands with consistent earned media coverage as authoritative. Brands relying on owned content find those inputs don't translate into AI citation outcomes. Greg Galant, CEO of Muck Rack, put it plainly in [Muck Rack's Generative Pulse](https://muckrack.com/blog/what-is-ai-reading-may-2026): for communications teams, earning coverage in the right outlets has real consequences beyond traditional metrics.

### How AI systems recognise and cite earned media

AI systems process text from across the web during training, learning which types of content appear in contexts associated with trust, accuracy, and editorial credibility. Earned media carries specific signals: named journalists, editorial oversight, correction policies, and no financial relationship between publisher and subject. A feature article about a brand in a trade publication reads very differently from the same brand's own blog post.

When AI cites a brand in response to a buyer query, it's almost always drawing from third-party sources rather than the brand's own domain. Earned media provides third party validation that AI systems treat as a credibility signal in ways that owned content structurally cannot. Earned media distribution across multiple independent publications multiplies this effect.

Press coverage in industry publications and earned media mentions across third-party sites create the independent editorial record AI engines retrieve from for category queries. Brand visibility in generative search is built through media relations, PR strategy, and consistent editorial coverage.

### AI citation sources: the platform breakdown

Each major AI engine sources its answers differently, but the preference for earned media is consistent across all of them:

| Platform | Citation behaviour | What earns citations |
|----------|-------------------|----------------------|
| **ChatGPT** | Cites in 96% of responses, avg 5 citations | Wikipedia, industry publications, third-party editorial |
| **Gemini** | Cites in 82% of responses, avg 8 citations | Brand-owned structured content alongside earned editorial |
| **Claude** | Cites in 55% of responses, avg 13 citations | High-credibility academic and editorial sources |
| **Perplexity** | Real-time retrieval from indexed web content | Trade press, review platforms, Tier-1 earned coverage |
| **Google AI Overviews** | Journalism doubles for time-sensitive queries | News coverage and category-native editorial media |

Google AI Mode citations show similar concentration toward editorial and third-party sources. Google AI Overviews now trigger on approximately 48% of all tracked queries according to BrightEdge's analysis. AI Overview citations from outside the organic top 100 are dominated by YouTube at 18.2% (Ahrefs, March 2026), confirming video has become a significant earned media citation surface.

## Brand mentions and AI visibility: what the data shows

Brand mentions (linked and unlinked references to a brand name across third-party web content) are the strongest measurable predictor of AI citation rates. The correlation between brand web mentions and AI Overview visibility stands at r=0.664 according to Ahrefs and LumenGEO's 2026 analysis. Backlinks correlate at r=0.218. Domain authority correlates at r=0.18.

Evertune.ai's analysis of 75,000 brands found the top 25% for web mentions earn over 10x more AI citations than the next quartile. The top quartile averages 169 AI mentions versus 14 for the next tier. The gap compounds: more mentions produce more AI citations, which produce more branded searches, which signal authority to AI systems, which produce more citations.

### Why brand mentions predict AI citation rates

Brand mentions work as an AI citation predictor because they're a proxy for something AI systems genuinely value: evidence that independent sources are discussing, verifying, and referencing the brand. When multiple editorial publications, review sites, and industry forums reference a brand in similar terms, that consensus tells AI models what the brand does and that it can be trusted.

The mechanism is machine relations: the relationship between a brand and the AI systems that learn about it from the web. A brand that appears consistently across trade publications, industry forums, and editorial blogs builds a richer machine-readable identity than one that lives primarily in its own content. Research from Evertune.ai, LumenGEO, and Ahrefs puts earned media density above domain authority, backlinks, and keyword optimisation as a predictor of citation probability.

### Web mentions versus backlinks for AI citations

The r=0.664 vs r=0.218 gap between mentions and backlinks changes which activities deserve strategic priority. Backlink acquisition, guest posting, and traditional SEO tools all build the metric that correlates least strongly with AI visibility. Earned media programmes that generate brand mentions across independent publications build the metric that correlates most strongly.

This doesn't mean backlinks are irrelevant. They still correlate with traditional search rankings and domain authority signals that some AI platforms weigh. But for brands investing in AI visibility, the return on earned media coverage is materially higher than the return on equivalent link-building investment. Our [digital PR and AI search guide](https://firstmotion.com/insights/digital-pr-for-ai-search-the-complete-strategy-guide) covers how to build the earned media programme that moves AI citation rates.

## The role of journalism in AI citations

Journalism accounts for 27% of AI citations, a figure steady at 25-27% across all three editions of Muck Rack's study. For time-sensitive queries, journalism's share rises to approximately 49% according to analysis of Muck Rack's citation data. Tier-1 publications (the New York Times, Wall Street Journal, Business Insider) carry disproportionate citation weight because they've passed the editorial credibility threshold AI models use.

### Why editorial media placements carry citation weight

Editorial media earns citation weight through four signals AI models trust: editorial oversight, named journalists, correction policies, and established reputations for factual accuracy. A news article in a major publication has passed an editor's review before publication under that outlet's editorial standards. Embargoed briefings allow journalists time to prepare richer coverage, producing more durable AI citations than a brief mention.

BuzzStream's January 2026 study of 4 million citations from 3,600 AI prompts across 10 industries found editorial blog and content pages account for 53.46% of all AI citations. News pages account for 14.09% and social content for 8.71%. The dominant citation class is substantive editorial content that addresses a question in depth.

### Industry publications and third-party editorial coverage

Beyond Tier-1 journalism, industry-specific publications carry significant citation weight for category-level AI queries. Third-party editorial coverage in trade press produces highly targeted AI citations, reaching buyers when they're actively evaluating options in a category. A strong narrative around AI research and category expertise is vital for earning coverage in the publications AI engines retrieve from most consistently.

Alongside traditional editorial media, YouTube has become a significant citation surface. Bluefish data reported by Adweek in January 2026, drawn from 6.1 million citations across four independent research firms, found YouTube appears in 16% of LLM answers, overtaking Reddit at 10%. YouTube accounts for 18.2% of AI Overview citations sourced from outside the organic top 100, according to Ahrefs March 2026 research.

Conference talks, product demos, and expert interviews on YouTube generate AI citations independently of text-based coverage.

## What the AI citations come from: the complete picture

Understanding which content types AI cites most frequently is as strategically important as understanding why earned media dominates. The BuzzStream January 2026 dataset covers 4 million citations across 10 industries.

### What AI answers reveal about content format

Editorial blog and content pages account for 53.46% of all AI citations. Within that category, comparative content is the most cited at 26.92%, followed by market analysis at 23.06%, and definition and explainer content at 20.57%. Ranqo's June 2026 study of 102 brands found best-of listicles account for 35.7% of content-level AI citations.

Citation behaviour splits clearly by format:

- Listicle and ranking formats perform strongly for evaluative queries
- News coverage earns citations for time-sensitive queries
- Press releases and owned content almost never earn direct AI citations

### Measuring AI citation outcomes

Measuring earned media's impact on AI visibility requires different tools from traditional PR measurement. AI mentions differ from citations: a brand can appear in AI answers without a linked citation, and both contribute to brand visibility in generative search. Tracking both through consistent prompt sets across ChatGPT, Perplexity, AI Overviews, and Google AI Mode maps earned media activity to citation outcomes.

Running 30 to 50 target prompts across major AI platforms weekly reveals which publications are appearing in citations for a brand's core category queries. That data shows which media placements are producing direct AI citation outcomes and which are building brand visibility without yet appearing as citations.

## Building the earned media presence that drives AI citations

Muck Rack, Evertune.ai, BuzzStream, and multiple 2026 AI citation studies all point to the same strategic priorities. Brands that earn AI citations consistently:

- Appear across multiple independent publications in their category
- Have earned coverage in high-authority outlets AI engines treat as credible references
- Generate enough organic third-party discussion that AI models have encountered them in multiple contexts

### Earned media strategy for AI citation rates

An earned media strategy built for AI citation rates prioritises coverage breadth, because citation probability increases when a brand appears across multiple independent sources. The Stacker December 2025 analysis found that distributing content across a wide range of publications increases AI citations by up to 325%. A data-led campaign placed with twenty relevant publications produces more AI citation value than an exclusive placement with one major outlet.

Recency matters alongside breadth. Half of all AI citations in the Muck Rack study came from content published within the last 11 months. AI retrieval systems weight recent content, and consistent earned media output keeps a brand's citation footprint current. Earned media distribution through consistent PR strategy is the operational mechanism that builds and sustains AI citation rates over time.

### Brand mentions and digital PR

Building brand mention density across digital channels is the most direct way to move AI citation rates. Every mention in a publication, review platform, or editorial adds a data point to the reference web AI systems draw on. Digital PR is the practice of building that density systematically. The target is the specific publications and platforms AI engines retrieve from for the brand's core category queries.

User-generated content, community discussions, and forum mentions also contribute to brand mention density. Community sentiment and engagement on forums are important for AI models mining conversational data. Reddit, specialist communities, and industry forums all appear consistently in AI citation sources, and PR teams building AI citation strategy need to include them in their target list.

## If your brand isn't earning AI citations, here's what the data says

The brands that earn consistent AI citations share a common thread: they've built the kind of earned media presence that AI systems were trained to trust. They appear in editorial media, in independent reviews, in analyst reports, and in community discussions. If AI-generated answers in your category describe competitors accurately and describe your brand inaccurately or not at all, the earned media footprint that AI systems are retrieving from is your competitors', not yours.

Talk to the FirstMotion team to map exactly where your brand sits in AI-generated answers for your core category queries and which earned media activities will close the gap most efficiently.

Most brands we audit appear in fewer AI-generated answers than they expect, and the gap is almost always an earned media gap, not a content gap. Our ContextualJourney™ platform maps which publications AI engines are retrieving from for your category queries before we recommend anything.
</content>

---

# Digital PR for AI Search: The Complete Strategy Guide

Source: https://firstmotion.com/insights/digital-pr-for-ai-search-the-complete-strategy-guide

Muck Rack's December 2025 analysis found 94% of AI citations come from non-paid, non-brand-owned sources. This guide covers how each major AI engine sources its answers, which digital PR tactics build AI citation rates most effectively, how to identify the specific publications AI engines retrieve from in your category, and how to measure the commercial return on digital PR in the AI search era.

*Update: Muck Rack's May 2026 Generative Pulse study, covering 25 million citations, revised this figure to 84%. We've kept the original December 2025 number below for accuracy at time of publication; see our [earned media and AI citations guide](/insights/how-earned-media-and-brand-mentions-drive-ai-citations) for the current figure.*

Digital PR has always built authority. In 2026, it also builds the earned media foundation that AI systems use to evaluate brand authority and decide which sources to cite when buyers ask for recommendations. Muck Rack's December 2025 analysis of generative AI citations found that 94% came from non-paid, non-brand-owned sources.

On site content, paid campaigns, and press releases distributed through wire services almost never earn a direct AI citation. Earned editorial coverage in reputable publications does.

## Key takeaways

- Muck Rack's analysis of generative AI citations found 94% came from non-paid, non-brand-owned sources
- Brand mentions predict AI search visibility three times better than backlinks
- 92% of consumers trust earned media over paid advertising, Nielsen confirms
- Distributing content across more publications increases AI citations by up to 325%

Every brand we audit at FirstMotion tells the same story through its data. Strong backlink profile, reasonable domain authority, and almost invisible in AI-generated answers for the queries that drive pipeline. The missing piece is almost never more content. It's consistent coverage in the specific publications AI engines retrieve from. Our [ContextualJourney™ platform](https://firstmotion.com/services/ai-search-optimisation) shows exactly where those gaps sit before we touch anything else.

## What digital PR is and why it matters for AI search

Digital PR is the practice of earning brand coverage, mentions, and backlinks from online publications and journalists through story-led outreach, data-led campaigns, and expert commentary. It sits at the intersection of traditional public relations and search engine optimisation, a collaboration that has been evolving for over 20 years. Its outputs, editorial placements in credible publications, are now the primary inputs AI systems use when forming answers about brands and categories.

Large language models build their understanding of a brand's authority from third-party editorial sources, not from owned content. Web pages that AI engines retrieve are almost exclusively from third-party publications, not brand-owned domains. AI systems recognise brands that appear consistently across credible sites and third party websites as authoritative in ways that on site content cannot replicate.

Generative Engine Optimisation (GEO) focuses on earning brand citations over backlinks, where traditional SEO focuses on keyword matching and technical site health. Traditional search engines return blue links in ranked order; AI-powered search engines return direct answers from the sources they trust most. Both matter, but the tactics that move AI-generated responses are fundamentally different from the tactics that move traditional search results.

### Digital marketing and the shift to AI-powered search

Digital marketing teams that treat PR as a separate silo miss the compounding value that earned media produces across both traditional search results and AI-generated responses. In the AI era, digital channels that generate PR coverage produce brand visibility in two places simultaneously. Earned coverage builds backlinks and domain authority signals in traditional search results while also producing the brand mention density and editorial credibility that AI models weigh in summaries and instant answers.

Brand perception in AI-generated responses is shaped entirely by what editorial media says about a brand, not what the brand says about itself. A brand that dominates AI-powered search engines for its category queries has almost always built that position through consistent PR coverage, not on-site content quality alone. SEO success in the AI era requires earned media alongside technical optimisation, built through data-led campaigns, expert commentary placements, and media relationships.

### How digital PR drives AI visibility

Earned media, not owned content, is the channel that compounds inside AI answers. Muck Rack's December 2025 analysis found 94% of AI citations came from non-paid, non-brand-owned sources. A University of Toronto controlled experiment confirmed the bias is structural: AI search engines show systematic preference for earned media, and their direct conclusion was that brands must dominate earned media to build AI-perceived authority.

Multiple GEO research firms found that 82% to 89% of AI-generated answers cite earned media rather than brand websites or blogs. When a buyer asks ChatGPT, Perplexity, or Google AI Overviews for a recommendation, the answer draws from what independent, credible sources have said about a brand, not from what the brand has said about itself. Go-to source status in a category requires consistent presence across the publications AI systems index heavily.

## How AI engines use earned media to form answers

Each major AI engine sources its answers differently. Understanding platform-by-platform preferences is the foundation of any effective digital PR strategy for AI search.

| AI platform | Primary citation source | What earns coverage |
|---|---|---|
| ChatGPT | Wikipedia (47.9% of top-10 citations) and third-party directories | Encyclopaedic brand presence, listing platform coverage |
| Perplexity | Industry-specific publications and review platforms | Tier-1 earned editorial coverage, specialist trade press |
| Gemini | Brand-owned websites with structured data (52.1% of citations) | Technical SEO discipline alongside earned media |
| Claude | Structured, sourced, authoritative content | High-credibility editorial sources, technical precision |
| Google AI Overviews | Correlates strongly with traditional organic rankings | Earned coverage in publications that rank in top-10 organic results |

AI models build their understanding of a brand from the totality of what independent sources say about it. Perplexity's three-layer reranking system structurally favours earned media from Tier-1 publications because of how its authority signals interact with externally verified credibility cues. A Forbes article about a company has passed an editor's judgement; a brand's own blog post has not. Perplexity's reranker reads the difference.

Gemini is the inverse. Brand-owned websites with structured data account for 52.1% of Gemini citations. For Gemini, technical SEO discipline and entity consistency across the Google ecosystem matter alongside earned media volume. Site structure, schema completeness, and consistent sameAs links between a brand's own site and its authoritative external identifiers all contribute to Gemini visibility.

## The digital PR strategy for generative engine optimisation

Strategic digital PR for GEO targets the specific publications AI engines retrieve from, not just the outlets with the highest domain authority in traditional search. The campaigns, content formats, and outreach approaches that move AI citation rates differ meaningfully from those optimised solely for link building and keyword rankings.

### Data-led stories for AI visibility

Data-led campaigns are the most popular digital PR tactic, cited by roughly 95% of industry professionals, with expert commentary second at about 93%, according to Reporter Outreach research. Both tactics produce the kind of PR coverage that earns placement in the authoritative publications AI engines trust most.

A data-led story for AI search visibility addresses questions buyers are already asking AI tools. Proprietary research on a category question, decision-maker surveys, and original industry analyses all produce material that trade press covers and AI engines subsequently retrieve as evidence. Distribute the same story across a wide range of publications. Citation quality matters as much as citation volume: a mention in a Tier-1 publication carries more weight than ten in low-authority outlets.

### Expert commentary and thought leadership articles

Expert commentary is the second most popular digital PR tactic, and it produces a different kind of AI citation value from data-led stories. When a named expert from a brand is quoted in a trade publication alongside their role and company, the AI system indexing that article establishes an entity association. The expert, the company, and the topic all become linked in its representation of the piece.

Thought leadership articles placed in category-specific trade publications produce similar results. A bylined article in a publication that an AI engine retrieves heavily for a specific category of query builds topical authority for the author and the brand simultaneously. Target reputable publications that AI engines actually retrieve from for the target queries, not just outlets with the highest general domain authority.

### Domain authority, brand authority and how AI engines weigh them

A website's authority in AI search depends more on what third-party sources say about the brand than on its domain authority in traditional search. Domain authority measures site strength through backlinks and site age; brand authority in AI search measures the frequency and credibility of third-party editorial mentions. The two correlate but aren't the same, and the gap between them is where most digital PR for GEO strategy sits.

Multiple 2025 and 2026 analyses found brand mentions correlate three times more strongly with AI search visibility than backlinks do. [Instant Press research](https://www.instantpress.co/digital-pr-statistics) found 80.9% of SEO specialists believe unlinked brand mentions influence organic search rankings, and the evidence for their influence on AI citations is stronger still. Coverage on third party websites and credible sites builds brand authority in ways that improving a brand's own site structure cannot replicate.

## Building the earned media coverage that AI systems trust

The publications that earn AI citations aren't evenly distributed. AI engines show strong concentration in their citation patterns: a relatively small number of high-authority publications account for a disproportionate share of AI-generated answers.

### Identifying the right media outlets for AI citation

Not all PR coverage is equally valuable for AI search visibility. A placement in a high-authority general publication may carry significant backlink value but produce minimal AI citation impact if that publication doesn't appear in AI-generated answers for the brand's target queries. Editorial media that AI engines retrieve heavily for a specific vertical (trade press, analyst blogs, and specialist publications) often produce more AI citation value than placements in larger general-interest outlets.

Building the target media list for a GEO-focused digital PR campaign involves three steps:

1. Identify which publications appear most frequently in AI-generated answers for the brand's core topic queries
2. Run a consistent prompt set across ChatGPT, Perplexity, Google AI Overviews, and Claude to reveal which outlets AI engines treat as authoritative references for the category
3. Make those outlets the primary target list for all outreach and campaign distribution

### Securing coverage across multiple publications

Stacker's December 2025 analysis found that distributing earned content across a wide range of publications increases AI citations by up to 325%. A brand mentioned across twenty publications in its category has twenty citation reference points, and the cumulative signal is proportionally stronger. Digital PR builds AI visibility through breadth and consistency of coverage, not just the prestige of individual placements.

News articles in category-specific trade publications provide the freshest citation signal for RAG retrieval systems, which index recently published content faster than evergreen content. A data-led story distributed to twenty relevant trade publications produces more AI citation impact than the same story placed exclusively with one major outlet. The [topical authority guide](https://firstmotion.com/insights/the-future-of-topical-authority-teaching-llms-to-trust-you) covers how this breadth compounds over time within a coherent topic cluster strategy.

### Localised digital PR and geographic AI search visibility

Localised digital PR can effectively target customers in specific locations by combining the editorial credibility of regional journalism with the citation footprint that AI-powered search engines index. Geographic-specific digital PR reinforces a business's connection to a community in ways that national campaigns don't, creating PR coverage in local outlets that AI engines surface for geographically qualified queries.

Data-driven regional studies attract local media coverage through localised angles: surveys of hiring patterns, sector growth analyses, and consumer behaviour studies all create genuine news hooks for regional journalists. Building relationships with local journalists over time improves campaign effectiveness. Effective local digital PR requires personalising pitches and addressing the hyperlocal concerns national agencies overlook. Measuring outcomes means tracking local keyword rankings, referral traffic from regional publications, and AI citation rates for geographically qualified queries.

### Digital PR tools and measurement for AI search

Measuring the impact of digital PR on AI search visibility requires different tools from traditional PR measurement. Coverage volume and backlink acquisition are useful but they don't directly measure the metric that matters for GEO: how often a brand appears in AI-generated responses for its target queries.

| Tool type | What it measures | Example tools |
|---|---|---|
| AI citation tracking | How often a brand appears in AI-generated answers for target prompts | Peec AI, Profound, Authoritas |
| Brand mention monitoring | Volume and distribution of brand mentions across indexed web content | Mention, Meltwater, Brandwatch |
| Earned media measurement | Coverage quality, domain authority, and estimated earned media value | Cision, Muck Rack, Prowly |
| AI share of voice | Brand citation share versus competitors across major AI platforms | ContextualJourney™, AirOps |
| Backlink analysis | Domain authority and link equity from earned placements | Ahrefs, Semrush, Majestic |

Running a consistent set of 30 to 50 target prompts across ChatGPT, Perplexity, Google AI Overviews, and Claude weekly provides the baseline measurement needed to track how digital PR campaigns move AI citation rates over time. A campaign that earns coverage in a publication appearing in AI-generated answers for a target query should produce a measurable lift in citation rate within three to five days of the publication indexing it.

### Measuring digital PR ROI in the AI search era

The most commercially significant AI search metric is conversion rate. AI search visitors convert at 14.2%, roughly five times higher than Google organic, according to data compiled across major AI search platforms. A brand that earns AI citations for high-intent queries is reaching buyers who are already in active evaluation mode.

Alongside AI citation tracking, monitor brand mention volume and distribution across reputable publications as the leading indicator that most strongly predicts AI search visibility. The 52.9% of link builders who find it hard to measure ROI, according to [Instant Press research](https://www.instantpress.co/digital-pr-statistics), can add AI citation rate as a direct output of digital PR investment.

## Digital PR strategy for B2B software brands

B2B software brands face a specific set of digital PR and GEO challenges. Buyers in this category increasingly use AI tools to research, shortlist, and evaluate vendors before making first contact. A brand absent from AI summaries and AI-generated responses for category queries is invisible to a significant and growing portion of its addressable market.

### Earned media coverage for B2B AI search visibility

The most effective digital PR tactics for B2B software brands targeting AI search visibility produce content in the publications buyers in that category read and that AI engines retrieve from. Trade press in the relevant vertical, analyst coverage, G2 reviews and review platform presence, and thought leadership in category-specific media all contribute to the earned media footprint that AI engines draw on.

Breaking news stories about the brand (product launches, funding rounds, executive appointments, and partnership announcements) produce short-term citation spikes in time-sensitive AI queries. They also create the third-party editorial record that AI systems draw on when forming parametric associations about a brand. Our [entity authority guide](https://firstmotion.com/insights/why-entity-authority-is-the-foundation-of-ai-search-visibility) covers how these external signals connect to the broader entity graph.

### Digital PR and traditional SEO working together

Digital PR drives AI search visibility and traditional search performance simultaneously. The top-ranked result in Google has 3.8x more backlinks than positions two through ten according to [Backlinko's ranking study](https://backlinko.com/search-engine-ranking), and referring domain count is the strongest measured ranking factor. A digital PR programme that earns press coverage in high-authority publications builds both.

That combination means a single PR programme builds two citation footprints at once. A brand that ranks in Google's top ten for a target query, and also earns AI citations for related prompts, captures two distinct audience segments. The first clicks organic results in traditional search. The second receives AI-generated responses in which the brand is named. As zero-click AI search behaviour grows, that second segment becomes increasingly commercially significant.

## If your digital PR programme isn't building AI search visibility, here's why

The most common reason digital PR investment fails to produce AI search visibility is that it's targeting the wrong publications. PR coverage in high-domain-authority outlets that don't appear in AI-generated responses for a brand's target queries contributes to backlink profiles without contributing to AI citation rates. A website's authority in traditional search and its citation weight in AI-powered search engines are related but distinct.

The second most common reason is inconsistency. Digital PR builds AI search visibility through the cumulative effect of consistent coverage in reputable publications, not through occasional high-profile placements. Talk to the FirstMotion team to map where your brand appears in AI-generated answers for your core category queries and which digital PR activities will move those citation rates most efficiently.

Most brands we audit are earning press coverage in the wrong places for AI search. Our ContextualJourney™ platform maps exactly which publications AI engines retrieve from for your category queries before we recommend anything.
</content>

---

# How Wikipedia Content Influences AI Search Responses

Source: https://firstmotion.com/insights/how-wikipedia-content-influences-ai-search-responses

Wikipedia ranks second in AI citation share, accounting for up to 48% of ChatGPT's top-10 citations. This guide covers how large language models use Wikipedia content to form answers, why outdated or missing entries directly damage AI search visibility, how Wikimedia's machine learning infrastructure maintains editorial integrity, and what B2B brands need to do to audit, fix, and use their Wikipedia and Wikidata presence to earn more AI citations.

Wikipedia sits at the centre of how AI systems form their answers. It accounts for 26 to 48% of ChatGPT's top-10 citations, second only to Reddit, which means a Wikipedia page isn't just an optional credibility signal for B2B software brands. In the AI era, it's infrastructure.

## Key takeaways

- Wikipedia accounts for up to 48% of ChatGPT's citations, ranking second overall
- More than 40% of users never verify AI Overview sources before accepting answers
- Direct brand editing on Wikipedia violates conflict of interest guidelines
- Outdated Wikipedia entries directly feed inaccurate descriptions into AI search answers

The brands FirstMotion works with rarely arrive knowing their Wikipedia entry is the problem. They arrive with low AI citation rates, and when we audit their entity signal using our ContextualJourney™ platform, Wikipedia is almost always where the gap sits. The article exists, it hasn't been updated in years, and every AI-generated answer about the brand has been drawing from it ever since.

## Wikipedia and AI search: why the connection matters

Wikipedia is structurally different from every other high-citation source in the AI ecosystem. Reddit earns its citation share through volume and recency, Forbes through editorial authority. Wikipedia earns it because every major LLM treats it as a foundational training source: verifiable, structured, and neutral in tone. AI systems treat it as a canonical reference.

The AI Citation Source Index 2026, synthesising over 680 million citations across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews, ranks Wikipedia second in consolidated citation share. It accounts for 26 to 48% of ChatGPT's top-10 citations and is described as "near-foundational training material." The top 15 domains capture 68% of all AI citation share, and Wikipedia sits firmly inside that group across every major platform.

Wikipedia also represents between 3 and 5% of ChatGPT's raw training data. That dual role (as training data and as a live retrieval source) means Wikipedia influences AI answers through two separate channels simultaneously. A brand present in both channels earns more consistent AI citations than one present in only one.

### How generative AI tools use Wikipedia content to form answers

Generative AI tools don't reproduce Wikipedia verbatim. They draw on the structured information in Wikipedia entries (the infobox data, the opening definition, the category relationships, the cited sources) to form the conceptual understanding of a brand or subject that they then express in their own language. This synthesis happens at two levels: during training, when the model forms parametric associations, and during retrieval, when RAG systems fetch and process Wikipedia content in response to a specific query.

Generative AI can lead to the loss of context when the Wikipedia source it draws from is itself missing context. A well-maintained Wikipedia article contributes accurate parametric associations. A thin stub or outdated article contributes weak or absent ones. Owned content can't easily correct them.

### Why Wikipedia is so heavily used by AI companies

Wikipedia's corpus is significantly less susceptible to the SEO-optimised, self-promotional language that dominates most of the web. Its editorial model produces what AI companies prize above almost every other open source: every claim requires a third-party citation, promotional language gets flagged and removed, and articles covering active companies face constant community scrutiny.

The Wikimedia Foundation recognised this dynamic explicitly in April 2025, when it announced a partnership with Google-owned Kaggle to release a version of Wikipedia specifically optimised for AI training. Starting with English and French, the foundation offered stripped-down versions of raw Wikipedia text (excluding references and markdown code) to make the corpus cleaner and more machine-readable for AI model development.

For generative AI tools building knowledge bases from public internet data, Wikipedia is the most concentrated source of structured, verified, neutral-tone content available. That announcement confirmed what AI companies had already been doing for years.

### Natural language processing and how AI reads Wikipedia entries

Natural language processing is how AI engines interpret Wikipedia articles and transform them into the structured representations that power AI search summaries. AI can interpret complex natural language questions by parsing Wikipedia entries through NLP pipelines, extracting entity relationships and structured facts. The quality of a Wikipedia article's structure has a direct bearing on the accuracy of AI-generated answers about a brand.

A well-organised Wikipedia article with clear headings, an accurate infobox, and properly categorised content produces clean entity extractions. An unstructured or poorly maintained article produces ambiguous extractions. AI systems cite it with less confidence, and the answers they generate about the brand carry a higher risk of error.

### Wikipedia's role in LLM training data

Wikipedia appears in Common Crawl datasets that form the base layer of most major LLM pre-training corpora. According to published research on LLM pre-training data, it formed more than half of BERT's training data. When AI models form their parametric knowledge, Wikipedia is one of the primary sources shaping those associations.

For B2B software brands, this means the version of their brand that lives in AI parametric knowledge was shaped substantially by whatever Wikipedia said about them at the time large language models were last trained. An accurate, well-cited Wikipedia article contributed accurate parametric associations. A thin stub or article riddled with outdated information contributed weak or absent ones, and owned content can't easily correct them afterwards.

## How Wikipedia's editorial model affects AI generated content

Wikipedia prioritises verifiability over pure accuracy. A Wikipedia article can contain technically inaccurate information that's highly verifiable, supported by multiple major news sources that all reported the same error. When AI gets something wrong about a brand in its generated summaries, Wikipedia is often the origin, because the model reproduced a verified inaccuracy with the same confidence it gives to accurate information.

For brands covered inaccurately in major news outlets, this verifiability standard compounds the problem. Wikipedia must cite those sources, and will, regardless of their accuracy. Negative or outdated coverage (a bad product launch, a leadership change, a funding round that didn't close) can persist in Wikipedia entries precisely because it meets the verifiability threshold. AI search summaries then amplify this information, presenting it as current fact to users who have no reason to question it.

### The role of Wikipedia's volunteer editors in AI accuracy

Wikipedia's editors are decentralised volunteers. The community that maintains articles about B2B software brands isn't composed of those brands' communications teams. It's composed of people interested in maintaining encyclopaedic accuracy as they understand it, drawing on the sources available to them.

The result is a maintenance gap that affects AI search accuracy. Many users accept AI-generated answers at face value, never knowing the answer about a brand came from a Wikipedia article that hasn't been updated in years. The brands most affected are those that changed most since their article was last updated: fast-growing software companies that pivoted, rebranded, or launched new flagship products between editorial reviews.

### The conflict of interest problem: why brands can't just edit their own Wikipedia pages

Wikipedia's conflict of interest guidelines explicitly restrict brands, PR professionals, and individuals with a financial interest in a subject from directly editing articles about that subject. A co-founder editing their own company's article is one of the most commonly cited examples in Wikipedia's editorial guidance. Direct editing risks a revert and a permanent flag on the editor's account.

The correct approach works within Wikipedia's guidelines rather than against them:

- Submit edit requests on the article's talk page, identifying specific inaccuracies and providing reliable third-party sources that support corrections
- Leave a comment on the talk page flagging errors for the volunteer editing community to address
- Work with independent Wikipedia-editing specialists who disclose their paid status per Wikipedia's paid editing policy

### Wikipedia citations and other sources: the verification chain AI trusts

Wikipedia's citation system creates a verification chain that AI systems treat as a proxy for reliability. An article citing academic journals, major news outlets, and authoritative industry publications carries more weight than one citing blogs or press releases. Wikipedia's community flags the latter as insufficiently reliable, and AI systems apply the same weighting.

For software brands building their Wikipedia presence, the quality of the sources supporting an article matters as much as the accuracy of the claims. Building the earned media record that Wikipedia's citation standards require is a prerequisite for a Wikipedia presence that AI systems treat as authoritative.

### What Wikipedia's verifiability standard means for brand reputation

Wikipedia's citations have extreme permanence. Once information appears in a Wikipedia article, supported by reliable third-party sources, removing it requires either demonstrating that the sources were unreliable or that the information is no longer relevant to encyclopaedic coverage.

The AI amplification of this problem is significant. The Exploding Topics AI Trust Gap survey of 1,115 users found that more than 40% rarely or never click through from AI Overviews to verify the source material. This is one of the primary mechanisms by which outdated or negative Wikipedia content becomes embedded in AI-generated brand narratives, reaching far more users through AI summaries than through direct Wikipedia traffic.

## How Wikimedia uses machine learning to maintain Wikipedia's integrity

The Wikimedia Foundation has integrated AI and machine learning into Wikipedia's content management since November 2015. The core tools it uses are:

| Tool | What it does |
|------|-------------|
| **ORES** | Evaluates Wikipedia edits in real time across 44 languages using 110 classifiers, flagging damaging or bad-faith contributions for human review |
| **Lift Wing** | Next-generation ML infrastructure superseding ORES, expanding model coverage across more languages and edit types |
| **Add-A-Link** | Recommends internal link additions to existing article text, supporting new editors in making high-quality contributions |
| **Content translation tool** | Suggests Wikipedia articles for translation, supporting multilingual accessibility across 300+ language editions |

Wikipedia's approach treats machine learning as a support tool for human editors rather than a replacement for human judgement.

### Wikipedia's search infrastructure: Elasticsearch and the move to hybrid search

Wikipedia's internal search primarily relies on Elasticsearch, using text-matching algorithms and BM25 scoring to parse user queries and return relevant articles. Wikimedia is actively exploring hybrid keyword and semantic search to improve user query results:

| Technology | Status | What it does |
|------------|--------|-------------|
| **Elasticsearch (BM25)** | Active | Text-matching and keyword ranking for Wikipedia's internal search |
| **Wikidata Embedding Project** | Launched October 2025 | Vector-based semantic search across 119 million Wikidata items in English, French, and Arabic |
| **Hybrid search** | In development | Combines BM25 keyword matching with vector semantic search for improved query results |

Spanish and Mandarin language support for the Wikidata Embedding Project are planned as the next expansion.

## Wikidata: the structured layer that feeds Google's Knowledge Graph and AI systems

Wikipedia and Wikidata serve different but complementary functions in the AI search ecosystem:

| Aspect | Wikipedia | Wikidata |
|--------|-----------|---------|
| Content type | Narrative encyclopaedic text | Structured property-value data |
| Primary use | LLM training and RAG retrieval | Entity resolution and Knowledge Graph |
| Format | Articles with citations | Machine-readable triples |
| AI role | Parametric knowledge and text retrieval | Entity disambiguation and structured fact retrieval |
| Scale | 60+ million articles | 119 million+ items |

Wikidata is the primary source for Google's Knowledge Graph, which stores approximately 500 billion facts about 5 billion entities. When AI systems need to quickly establish basic facts about a company, they draw heavily on the Knowledge Graph, which draws heavily on Wikidata. A brand with a complete, accurate Wikidata entry benefits from a chain of authority running from Wikidata through the Knowledge Graph into AI parametric knowledge and live retrieval.

### How Wikidata's vector database changes AI access to structured knowledge

The Wikidata Embedding Project, led by Wikimedia Deutschland in collaboration with Jina.AI and DataStax, launched on October 1, 2025, and introduced vector-based semantic search across Wikidata's entire knowledge graph. The project transforms Wikidata's structured data into multilingual vector representations that AI systems can query using natural language rather than formal SPARQL queries, making the knowledge graph usable by LLMs in RAG pipelines.

For B2B software brands, this creates a more direct route from structured brand data to AI generated answers. A complete, accurate Wikidata entry (with accurate properties and sameAs links to the brand's Wikipedia article, LinkedIn profile, and other authoritative identifiers) becomes retrievable through natural language semantic search by any AI system connected to the Wikidata embedding infrastructure. As vector-based retrieval expands across more AI platforms, brands with complete Wikidata entries will earn citations through a route that no traditional SEO signal provides.

### How Wikidata entries affect AI answers

A Wikidata entry stores structured property-value pairs that represent facts about an entity. Claiming and populating a Wikidata entry for a brand (with accurate properties and sameAs links connecting it to the brand's Wikipedia article, LinkedIn profile, and other authoritative identifiers) strengthens the entity resolution that AI systems perform when deciding which brand is being discussed. Entity resolution matters for AI search because many queries are ambiguous. A brand with a strong Wikidata entry resolves more cleanly than one with a sparse or missing entry, reducing the risk that AI systems conflate it with similar entities.

## How to improve your Wikipedia presence for AI search

Brands that want to control how AI systems describe them need to start with Wikipedia. The most common Wikipedia-related AI visibility problem is that an article exists, it's inaccurate, and no one in the brand's marketing or communications function has looked at it in years.

The starting point is an audit. Check the article against current facts:

- Company description and founding date
- Key products and current business model
- Co-founder and leadership information
- Major milestones and funding rounds
- All cited sources: confirm they are still live and support the claims attributed to them
- The Wikidata entry: check for completeness and accuracy against the same facts

### What a strong Wikipedia article looks like for AI visibility

A Wikipedia article that performs well in AI citation systems has consistent properties:

- Opens with a clear, accurate, encyclopaedic definition of the company in the first paragraph
- Includes a complete infobox with founded date, headquarters location (city and country), founders, and industry category
- Cites reliable third-party sources (major tech publications, academic journals where applicable, and credible industry analysts) rather than press releases or company-owned content
- Covers the company's history, products, and notable milestones in neutral, encyclopaedic language with no promotional framing
- Every claim is cited to a live, independent source
- The talk page shows evidence of editorial engagement: comments from editors, a record of discussions, and a history of good-faith improvements

### Build the third-party source record that Wikipedia requires

Wikipedia's verifiability standard means that corrections and additions require reliable third-party sources. Growing software companies find the biggest gains come from building the earned media record that Wikipedia's editors treat as authoritative. Coverage in major trade publications, analyst reports, and news outlets creates the source base that allows Wikipedia articles to be updated and expanded with appropriate citations.

Earned media and Wikipedia presence are strategically linked for exactly this reason. Our topical authority guide covers the full external signal picture in depth, including how to build the brand recognition that feeds Wikipedia's verifiability requirements.

### Social media, earned media and the sources Wikipedia treats as reliable

Wikipedia's reliable source guidelines distinguish between sources it treats as authoritative and those it treats as insufficiently independent. Social media posts (including those from a brand's own accounts) are almost never acceptable as Wikipedia citations. Press releases from the company itself don't meet the independence requirement.

The path to fixing or expanding a Wikipedia article runs through earned media. A brand covered by TechCrunch, Wired, or major trade publications has the source material to support Wikipedia updates. Social media presence can build brand recognition that leads to earned coverage, but it doesn't itself constitute the verifiable record that Wikipedia's editorial process requires.

### The sameAs connection: linking Wikipedia to your brand's entity graph

A Wikipedia article is most valuable for AI search when it's connected to the full network of authoritative brand identifiers via structured data. The sameAs property in schema markup should link a brand's website to its Wikipedia page, its Wikidata entry, its LinkedIn profile, and any other authoritative external identifiers. This chain of connections tells AI systems that all these references point to the same entity, resolving the ambiguity that dilutes citation confidence.

Entity authority is the foundational layer beneath every AI search visibility strategy. Wikipedia and Wikidata are the two most structurally important components of that entity layer. Getting both right produces compounding gains across every AI platform that draws from these sources.

## The traffic question: does Wikipedia still drive direct visitors?

Wikipedia's direct traffic contribution to brand websites is minimal by design. Wikipedia's external links are nofollow and the editorial community actively removes links that look promotional. The value of Wikipedia presence is entirely about the entity signal and AI citation infrastructure it provides.

This distinction matters because some brands deprioritise Wikipedia maintenance on the basis that it drives no measurable traffic. That reasoning misses the mechanism. Wikipedia's influence operates through the parametric knowledge of AI models and through the live retrieval of AI search systems. A brand that neglects its Wikipedia entry loses AI citation share, not referral traffic.

Given that more than 40% of users accept AI-generated answers without clicking through to source material, the AI citation channel is more commercially significant than the direct Wikipedia traffic channel for most brands in this position.
</content>

---

# The Future of Topical Authority: Teaching LLMs to Trust You

Source: https://firstmotion.com/insights/the-future-of-topical-authority-teaching-llms-to-trust-you

Topical authority now determines AI citation rates more than backlinks or domain authority. This guide explains how LLMs form topical associations during training, why brand search volume outperforms backlinks as a citation predictor, and what a practical four-workstream programme looks like for B2B software brands building AI search visibility in 2026.

Topical authority always mattered for SEO. In the age of large language models, it's become the primary mechanism by which AI systems decide which brands to trust, which sources to cite, and which voices to surface when buyers ask for recommendations. Brand search volume now has a stronger correlation with LLM citations than backlinks do. That's a structural shift, not a trend.

## Key takeaways

- Brand search volume is the strongest predictor of LLM citations, not backlinks
- Only 11% of domains earn citations from both ChatGPT and Perplexity
- Adding statistics lifts AI visibility by 22% and quotations by 37%
- Keyword stuffing actively damages AI citation rates while comprehensive topical coverage improves them

We see this gap repeatedly in the brands FirstMotion works with. Strong Google rankings, solid backlink profiles, and still absent from the AI-generated answers their buyers are actually reading. The issue is rarely the content itself. It's that AI systems haven't been given the signals they need to trust the brand as an authority on the topic. Our ContextualJourney™ platform maps exactly where those signals break down and what to fix first. This article covers the full picture.

## What topical authority for LLMs means and why it matters

Topical authority in traditional SEO means a site covers a specific subject with enough depth and consistency that search engines recognise it as the go-to resource. LLMs work differently. They build neural representations of entities during training, and brands that appear frequently across authoritative sources develop stronger representations, making them more likely to surface in AI generated answers.

The Digital Bloom's AI Citation Report (analysing over 680 million citations) found that brand search volume carries a 0.334 correlation with LLM citation rates, the strongest predictor measured, outperforming domain authority, word count, and backlinks. In our audits, the brands with the strongest AI citation rates are almost always the ones buyers are already searching for by name. The category recognition came first, the citations followed.

AI engines use vector spaces and embeddings to group information by concepts. A brand whose content consistently clusters around specific topic areas builds stronger semantic associations than one that publishes broadly. A focused B2B software brand that covers a topic in depth can genuinely outcompete a larger general publication for LLM citations.

## How topical authority shapes AI answers and search results

When an LLM encounters a query, it retrieves from its parametric knowledge and, in search-enabled systems, from real-time retrieval using semantic vector matching. Topical authority influences both pathways. The Digital Bloom's AI Citation Report confirms that 60% of ChatGPT queries are answered from parametric knowledge alone, without triggering web search. Topical authority is partly a training data problem: the brands that earn AI citations are the ones that appeared frequently across authoritative sources before the model's training cutoff.

AI generated content from LLMs draws on these topical associations. When AI models generate answers about a category, they surface brands whose topical associations are strongest in their neural representations. For content marketers and SEO strategists, gaining visibility in AI answers is what [the AI search revolution](https://firstmotion.com/insights/from-keywords-to-conversations-the-ai-search-revolution-in-b2b-saas) demands: a fundamentally different approach from optimising for keyword rankings. Topical authority is what connects the two strategies.

## Topical authority versus domain authority: the key difference

Traditional domain authority measures overall link equity and technical strength across the web. A site can have high domain authority but low topical authority if its content spans too many unrelated subjects without depth in any of them. SEO topical authority focuses on expertise in specific subjects. [Our GEO vs SEO guide](https://firstmotion.com/insights/geo-vs-seo-whats-the-difference) covers the full distinction in depth.

A brand that publishes fifteen pieces on a narrow topic cluster builds stronger topical authority than one that publishes one article on each of fifteen different topics, even with stronger domain authority overall. A focused, well-structured topic cluster can shift a brand's AI citation rates in a category without acquiring a single new backlink. We've seen this directly. A client with a domain rating below 40 outperformed category incumbents in AI citation rates after three months of focused cluster work.

## Why traditional SEO strategies miss the LLM citation opportunity

Keyword research in traditional SEO focuses on search volume and ranking potential for specific terms. Using a keyword research tool to identify all the keywords on a given topic and optimising for each separately reflects a keyword-matching mindset. LLMs use semantic search that evaluates the conceptual relationship between a query and a body of content. User intent in AI search is broader: LLMs are trying to find the source that most comprehensively addresses a topic, covering related ideas and related searches within a coherent cluster.

The Princeton GEO study analysed 10,000 queries across nine sources and found that keyword stuffing actively damages AI visibility. Adding verifiable citations to content increased AI visibility by 115.1% for sites previously ranked fifth. These findings directly contradict the logic of traditional keyword-led content strategies and point instead to depth, accuracy, and semantic coherence as the primary AI ranking signals.

## How LLMs retrieve and cite content: the two pathways

Every major LLM operates through two distinct knowledge pathways that determine which sources it cites.

### Parametric knowledge: what the model learned during training

Parametric knowledge is everything an LLM absorbed during pre-training. It's static. The model accesses it without external calls and retrieves it in milliseconds. Wikipedia accounts for approximately 22% of major LLM training data according to the Digital Bloom report, which explains its dominance in citation patterns. For B2B software brands, this means external authoritative mentions matter: trade press coverage, analyst briefings, G2 reviews, and community discussions all contribute to parametric presence.

### Retrieved knowledge: real-time RAG systems

RAG (Retrieval Augmented Generation) systems give LLMs access to current information by querying live sources at the moment of the user's prompt. The query converts into a vector embedding. The system matches it against indexed content using semantic search and keyword matching. For content to perform well in RAG retrieval, structure matters as much as substance.

The Digital Bloom report highlights NVIDIA benchmarks showing that page-level chunking achieves 0.648 accuracy with the lowest variance. Optimal paragraph length for AI extraction is 40 to 60 words: short enough to be extracted cleanly, substantive enough to answer a query independently.

## Building topical authority for AI search: the content strategy

Topical authority for LLMs builds through three parallel workstreams: comprehensive topic coverage, strategic content structure, and consistent content quality. None of these alone produces the citation rates that the combination achieves.

### Topic clusters and pillar content for LLM visibility

A topic cluster links a pillar page covering a subject comprehensively to supporting articles each addressing a specific subtopic. This gives AI crawlers a connected network of related content to index and associate with a particular topic. It also gives LLMs the comprehensive content they need to form confident associations between a brand and a subject area across multiple retrieval queries.

Topical authority isn't solely about publishing numerous pages. Depth and coherence matter more than volume. AI systems prefer sources with multi-faceted coverage because they pose lower hallucination risks. A brand that covers a topic in depth from multiple angles, with consistent accuracy, earns more citations than one that covers topics shallowly.

### Internal links and topical cluster architecture for AI search

Internal links signal to AI systems which pages belong to the same topical cluster and how they relate to each other. A pillar page linking to every supporting article, with every supporting article linking back to the pillar and sideways to sibling pages, creates the link architecture AI crawlers follow to map a brand's full topical coverage. [Building that link structure](https://firstmotion.com/insights/how-internal-linking-strengthens-ai-search-signals) correctly is as important as the content itself.

A strong internal linking structure reinforces topical authority signals at both the crawl level and the semantic level simultaneously. AI systems that index a well-linked topic cluster encounter the same relevant entities and related ideas across multiple pages, reinforcing the topical associations that drive citation probability. Identifying gaps in internal linking is one of the fastest diagnostic steps in any topical authority audit, since a site's credibility in a specific subject area depends on every relevant page being connected.

### Establishing topical authority across your own website

Establishing topical authority across an own website requires consistency in topical focus, terminology, and publication cadence. Publishing consistently on the same core subject areas, using the same phrases across related pages, and maintaining a regular cadence all contribute to topical authority that compounds over time.

Content marketers building topical authority programmes find the biggest gains come from auditing existing content before creating new content. Most sites have orphan pages covering relevant subtopics that were never integrated into a cluster, older articles with strong organic rankings that could send more topical signal if updated and internally linked, and gap areas where buyer queries produce no site content at all.

### Creating content that establishes expertise on a particular topic

Creating content that establishes expertise on a particular topic requires demonstrating practitioner-level knowledge of the subject. First-person observations grounded in real client work, fresh insights from proprietary data, and case study evidence all communicate in depth experience that generic content never achieves. High quality content that covers a topic in depth (written with the precision of someone who has actually solved the problem) builds stronger topical authority than broad overview content.

For B2B software brands, covering a topic in depth on specific buyer pain points performs better in AI citation systems than general category content. A comprehensive guide to solving a specific problem (with accurate citations and original observations) earns more citations because it makes sense to specialist readers and reduces the hallucination risk that AI systems are explicitly trying to avoid.

### The content formats that earn the most AI citations

Analysis of over 30 million citations in the Digital Bloom report found that format has a measurable impact on AI citation rates:

| Format | AI citation share | Best platform |
|---|---|---|
| Comparative listicles | 32.5% | Cross-platform |
| FAQ and Q&A formats | High | Perplexity, Gemini |
| How-to guides | Strong | Cross-platform |
| Opinion blogs | 9.91% | Limited |
| Product descriptions | 4.73% | Limited |

Comparison content earns outsized AI citations for B2B software brands because it answers the exact queries buyers use when forming shortlists in AI-assisted research sessions. It also signals comprehensive coverage of a category. The comparison content on FirstMotion's own site consistently earns our highest citation rates, as it most closely mirrors how buyers query AI systems about vendor options.

### Structuring content for AI extraction

Content structure directly affects whether an LLM can extract and cite a passage:

- Open every section with a direct answer to the section's central question
- Keep paragraphs between 40 and 60 words for optimal RAG chunk extraction
- Use clear H2 and H3 headings that mirror the actual questions buyers ask in AI interfaces
- Make each section independently comprehensible when extracted as a standalone chunk
- Include verifiable statistics with named sources in every substantive section

Adding statistics increases AI visibility by 22%. Adding quotations from named sources increases it by 37%. Both signals tell AI systems the content is grounded in verifiable evidence, which reduces hallucination risk.

## E-E-A-T, topical authority and what LLMs actually evaluate

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's quality evaluation framework. It maps closely to the signals LLMs use to assess source credibility. Google evaluates E-E-A-T through human quality raters and algorithmic signals. LLMs evaluate equivalent signals through the frequency and consistency of a source's presence across authoritative indexed material.

### How Google rewards sites with strong E-E-A-T signals

Google rewards sites that demonstrate genuine expertise, real-world experience, and earned authority from independent sources. High E-E-A-T improves visibility in AI-driven search results because verifiable credentials, accurate claims, and third-party corroboration are the signals LLMs use to assess whether a source is safe to cite.

Consistent content publication builds E-E-A-T over time. A site's credibility builds from accurate content, named expert authors, and external validation, not from volume of publication alone.

### Brand authority signals that LLMs use to evaluate trust

Brand authority for LLMs builds from every surface where a brand has a presence. Alongside content quality, LLMs weigh:

- Structured data accuracy
- Platform consistency across all brand listings
- Community presence on forums and review sites
- Branded search frequency as a signal of genuine market recognition

The Digital Bloom report's finding that brand search volume carries the strongest correlation with LLM citations (0.334) reflects this directly. A brand that becomes the recognised name buyers reach for in a specific category earns the organic branded searches that signal to AI systems that buyers are actively seeking it out. [Our entity authority guide](https://firstmotion.com/insights/why-entity-authority-is-the-foundation-of-ai-search-visibility) covers how to build those signals systematically.

### Named authors and subject matter expertise

Person schema and named author attribution aren't just E-E-A-T signals for Google. They're entity signals that help LLMs identify and trust specific individuals as authoritative sources. A named author with a Wikidata entry, LinkedIn profile, and consistent publication history in a specific subject area builds a stronger individual entity signal than anonymous content.

Building author entities for named founders, subject matter experts, and senior practitioners produces E-E-A-T signals that compound over time. This gives AI models another anchor point for associating the brand with its claimed expertise.

## External signals: earning the citations that build topical trust

Topical authority in owned content is necessary but not sufficient. LLMs build their understanding of a brand's expertise from the totality of what independent, authoritative sources say about it. Providing fresh insights through original datasets or proprietary research strengthens content authority in ways that derivative content never achieves. Where clients have published original research including survey data and proprietary platform analysis, those pieces earn citations weeks after publication and continue appearing in AI responses months later.

### Brand visibility in AI answers: what moves the needle

Brand visibility in AI answers is a function of how many independent, credible sources mention a brand in the context of a specific topic. The Digital Bloom report found that sites on 4+ platforms are 2.8x more likely to appear in ChatGPT responses. For B2B software brands, the highest-leverage external platforms for topical visibility in AI answers are G2 and equivalent review aggregators, LinkedIn, industry-specific publications that rank well for category queries, and Wikidata and Wikipedia where applicable.

Only 11% of domains appear in both ChatGPT and Perplexity responses. A cross-platform strategy covers three layers:

- Parametric presence: Wikipedia, Wikidata, and consistent mentions in training-weighted sources
- Real-time retrieval presence: fresh well-structured content and active community presence on platforms AI systems draw from
- Traditional search presence: strong organic rankings with structured data

### Related searches, relevant entities and how AI maps your brand

AI systems evaluate a brand in the context of the relevant entities it associates with: competitors, topics, use cases, industries, and problems. A brand that appears consistently alongside the right relevant entities builds topical associations that make it more likely to surface when buyers query AI systems about those entities.

Related searches and related subtopics in a content cluster satisfy user intent and user behaviour patterns by anticipating the next question a reader is likely to ask. They also strengthen topical entity associations by repeatedly placing a brand's content alongside the same cluster of relevant ideas. All this external signal work directly builds the brand search volume that the Digital Bloom report identifies as the strongest predictor of LLM citations.

## Measuring topical authority for AI search

Measuring topical authority requires different tools from traditional SEO reporting. Google Search Console tells you how visible you're in traditional search results. It tells you nothing about AI citation rates, share of voice in AI answers, or how your topical authority compares to competitors in LLM-generated responses.

### The metrics that reflect LLM trust

The core metrics for AI topical authority measurement are:

| Metric | What it measures |
|---|---|
| Citation rate | How often your brand appears in AI answers for your target prompt set |
| AI share of voice | Your citations as a percentage of all brand citations in your category |
| Sentiment accuracy | How accurately AI systems describe your brand's expertise and positioning |
| Cross-platform coverage | How many major AI platforms cite your brand for core topic queries |
| Citation drift | Monthly volatility in citation rates (40 to 60% is normal) |

A brand tracking citations across 30 to 50 representative prompts quickly identifies which subtopics produce consistent citations and which produce none. The gaps define the content and entity signal priorities for the next quarter. Citation drift figures are drawn from the Digital Bloom's 2025 AI Citation Report.

### Topical authority, Google search and traditional SEO

Topical authority in AI search doesn't require abandoning traditional SEO: the correlation between Google search Page 1 rankings and LLM mentions is approximately 0.65 according to the Digital Bloom report, and a strong topical authority programme raises both simultaneously. SEO topical authority and AI topical authority share the same foundation: accurate, comprehensive, well-structured content on a specific subject that earns external validation from independent sources.

### Identifying gaps in your topical coverage

The most common topical coverage gaps fall into three categories:

- Subtopic pages that don't exist yet but belong in the cluster
- Existing pages covering relevant topics that aren't integrated into the cluster's internal link architecture
- Topic areas where competitors consistently earn AI citations but the brand doesn't appear

A keyword research tool helps identify the subtopics that define a category. Running a prompt set on major AI platforms reveals which subtopics produce citations and which are invisible.

## A practical topical authority programme for B2B software brands

Establishing topical authority with LLMs is a programme, not a project. The brands that earn consistent AI citations commit to all four workstreams continuously.

### Workstream 1: topic cluster architecture and internal links

Map three to five core topic clusters to the questions your buyers ask AI systems during research and shortlisting. Build a pillar page for each cluster that answers the broadest version of the topic directly and comprehensively. Create supporting articles for each important subtopic, linking back to the pillar, forward from the pillar, and sideways between sibling cluster pages. Strong internal linking is the structural foundation that connects all this topical coverage into a coherent signal.

### Workstream 2: content quality and structure

Every piece of content in the cluster should open with a direct answer to its central question. Include verifiable statistics with named sources. Add fresh insights or proprietary data where available. High quality content (with 40 to 60 word paragraphs and headings that mirror actual buyer queries) creates the AI powered citation signals that thinner content never achieves. Creating content at this standard takes longer but produces measurably better citation rates across all major AI platforms.

### Workstream 3: entity and external signal building

Create or claim Wikidata entries for the brand and named authors. Ensure consistent, accurate brand information across G2, LinkedIn, Crunchbase, and industry directories. Pursue earned media coverage in publications that LLMs weight heavily in your category. Build community presence on the platforms AI systems draw from for real-time retrieval in your sector.

### Workstream 4: measurement and iteration

Run a consistent prompt set of 30 to 50 queries across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, and Gemini weekly. Track citation rate, share of voice, and sentiment accuracy for each. The 40 to 60% monthly citation drift across major platforms makes weekly monitoring the minimum viable cadence. Use the gaps to identify content and entity signal priorities for the next quarter. The brands we work with that invest in all four workstreams simultaneously see compounding citation gains that single-workstream approaches never produce.
</content>

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# How Internal Linking Strengthens AI Search Signals

Source: https://firstmotion.com/insights/how-internal-linking-strengthens-ai-search-signals

Internal linking distributes link equity, builds topical authority, and gives AI systems the structural context they need to understand what a site covers. This guide covers the Zyppy data on how many internal links drive results, how to build a pillar-cluster architecture for AI search, and the practical steps to fix orphan pages, anchor text, and link distribution across your entire site.

Internal linking matters more than most B2B software brands realise. It distributes link equity, tells search engines which pages are most valuable, and gives AI systems the structural context they need to understand what a site covers. Most brands treat it as an afterthought. The ones earning consistent AI citations don't.

## Key takeaways

- Pages with 40 to 44 internal links earn four times more Google Search clicks
- Exact-match anchor text produces five times more traffic than generic link anchors
- Orphan pages earn no link equity and are invisible to AI search crawlers
- Bidirectional pillar-cluster linking is the dominant architecture for AI search visibility

Internal linking is one of those areas where we find a clear and consistent gap in the audits we run at FirstMotion. Strong content, reasonable backlink profiles, and still low AI citation rates because the site's internal structure sends no clear topical signal. In our audits, the majority of brands arrive with no internal linking strategy at all. Links were added page by page as content was published, with no architecture behind them.

Our ContextualJourney™ platform maps exactly how AI systems navigate a site before we recommend a single change. What it surfaces most often is a structure where high-value pages are either orphaned or weakly connected. The fix is almost always structural, not creative.

## Why internal linking for SEO and AI search matters

Internal linking connects pages on the same domain, distributes link equity from strong pages to weaker ones, and signals to search engines which content is most important. For AI search its role goes further. Large language models use a site's internal link structure to map content relationships, understand topical depth, and determine which pages are authoritative sources on specific subjects.

AI models also track user behaviour signals, and strong internal linking keeps visitors engaged longer, reducing bounce rates and producing the engagement signals AI search models use to evaluate content quality.

Natural language processing is how AI systems interpret the relationships between web pages they find through internal links. When AI-driven search models analyse content to understand relationships between topics, they use the link structure, anchor text, and surrounding copy to infer topical associations. This makes internal linking important for both crawlability and the semantic signals that determine citation probability.

### Internal linking for SEO: the foundational signals

John Mueller of Google has described internal linking as "super critical for SEO" and "one of the biggest things you can do on a website". Good internal linking shapes search engine rankings by ensuring link equity flows to key pages, keeping important content within crawling range, and building the topical cluster signals both traditional search and AI systems use to identify expertise. Strategic internal links from high-authority pages pass the most ranking power to the pages that need it most.

We've seen this play out repeatedly across client sites. Fixing internal link structure on key pages produces ranking improvements within weeks, before a single new piece of content is published.

### How AI models use internal links to evaluate content quality

AI models evaluate every page in the context of what surrounds it. A page with contextually relevant links to related topics earns a stronger topical authority signal than an identical page sitting in isolation. Internal links carry both context and authority between pages, telling AI systems which pages belong to the same knowledge domain and helping them understand site structure at the topical level.

## How search engines and AI systems use internal links

Search engine crawlers follow internal links to discover new pages across a site. A page with no internal links pointing to it receives no link equity and performs poorly in both organic rankings and AI-generated answers. A JetOctopus large-site case study found only 40% of pages were crawled by Googlebot before a revised internal linking scheme was implemented, rising to 70% after.

We see similar patterns in our own audits. Significant proportions of site content sit uncrawled because no internal links point to it, making those pages invisible to both search engines and AI platforms.

### Indexing, crawlability and why every page on your site needs internal links

Search engines primarily discover new content by following internal links, not sitemaps alone. When search engines crawl a well-linked site, they encounter key pages on every pass, building the indexing confidence that underpins citation probability.

Every page on your site needs internal links pointing to it:

- Every web page should have at least one contextual internal link from a related page
- Important pages including pillar content, service pages, and high-converting landing pages should have multiple contextual links from across the site
- Any page sitting outside the link network is effectively invisible to search engines and AI crawlers

### Orphan pages and the cost of poor internal linking

Orphan pages are pages on your site with no internal links pointing to them. Search engines have no path to reach them and AI systems can't reliably locate or cite them regardless of content quality. Fixing orphan pages is as simple as finding one page that covers a related topic and adding a contextual link from it. That single connection restores link equity flow and puts the page back in the crawl path.

## Internal links and external links: how both users and search engines follow them

Internal links connect pages on the same domain, distribute link equity, and help search engines understand site structure. External links point to other domains and contribute to the entity corroboration AI systems factor into citation decisions. Both users and search engines follow these link types differently, and understanding the distinction matters for on page SEO strategy.

Clear navigation built on strong internal linking keeps visitors on your site longer, lowers bounce rates, and produces the engagement signals AI models use to assess whether a page is worth citing. For B2B software brands, internal links are the more controllable lever. Adding links across an entire site produces measurable improvements in search engine rankings without any external dependency.

### Link equity, topical authority and AI citations

Link equity flows through internal links from pages with strong external backlinks to pages that need authority. A high-traffic pillar page can pass measurable ranking power to cluster pages and service pages through well-placed contextual links, connecting external authority to every page on the site.

### High value pages and link equity distribution

Your most valuable pages, the ones with the strongest referring domains and highest organic traffic, are your primary link equity donors. Strategic internal links from these pages to related pages that need authority pass ranking power without any additional off-site work:

- Pillar content pages with strong referring domains are the strongest donors
- Product and service pages benefit most from links originating on high-traffic blog content
- Cluster pages addressing buyer decision criteria earn the most from links on pillar and category pages
- Links from any high-value page to cluster content lift search engine rankings across the entire site

### How internal linking builds topical authority for AI search

Zyppy's 23 million link study across 1,800 websites found that pages with 40 to 44 incoming internal links received four times more Google Search clicks than pages with only zero to four. The most likely explanation is that pages with more varied internal links carry stronger topical association signals, exactly the kind that AI systems use to form citation preferences.

### Internal linking strategy: building topic clusters for AI search

The dominant internal linking architecture for AI search in 2026 is the pillar-cluster model. A broad pillar page covers a topic comprehensively. Supporting cluster pages each cover a specific subtopic and link back to the pillar, while the pillar links forward to every cluster page. This bidirectional pattern concentrates topical authority on the pillar and signals to AI systems that the cluster covers a coherent body of work.

### Building a strong internal linking strategy around pillar pages

A strong internal linking strategy starts by mapping core topics to pillar pages, then auditing all existing content for subtopics that belong under each pillar. Every piece of content covering a subtopic should link back to the relevant pillar using descriptive anchor text. Service pages and blog posts addressing buyer decision criteria should form the strongest cluster connections. New content fills gaps where subtopics have no dedicated page, giving AI systems a navigable content graph they can map and cite with confidence.

### Cluster pages, blog posts and connecting related pages

Each cluster page and blog post should link back to its pillar and sideways to two or three sibling pages on relevant content. Updating older articles with new internal links to related pages is one of the fastest ways to build this network on sites with existing content. Adding links from established pages to newer ones gives new pages immediate link equity and reduces orphan page count across the entire site in one pass.

## How to add internal links and add links that build topical signal

The right number depends on content length and connection quality. Zyppy's data shows the traffic benefit peaks between 40 and 44 incoming contextual links. A practical target for most B2B content is two to five contextual links per 1,000 words. The goal when you add internal links is connection quality over volume: each link should move a reader to a page that genuinely answers their next question.

More isn't always better: links to weakly related pages dilute topical signal rather than build it.

### Contextual links versus sidebar links

Contextual links placed inside body content carry a stronger semantic signal than sidebar links or navigational links. Adding contextually relevant links at the exact point where a reader would naturally want the next answer produces the editorial relevance signal AI systems read. Sidebar links and navigational links are structural. For AI search signal building, contextual placement is what moves citation rates.

### How to add new internal links to existing content

Start with your highest-traffic pages and add links wherever a topic is mentioned that has its own dedicated page elsewhere on the site. Use descriptive anchor text at each point. Then move to your highest-priority key pages, adding more internal links from related pages until every important linked page has several contextual links pointing to it from genuinely relevant content.

### Anchor text: why it matters for AI systems and search engines

Descriptive anchor text is the fastest single improvement in most internal linking programmes. Zyppy's analysis found that pages with at least one exact-match anchor text had at least five times more traffic than pages without. AI systems interpret anchor text as a description of the linked page before they follow a link. Descriptive anchors that match the destination page's primary topic give AI systems a direct signal reinforcing the topical association the link is building.

### Consistent terminology and why it makes linking coherent

Consistent terminology across a site reinforces topical clarity and makes linking more coherent for both users and search engines. When every page discussing a topic uses the same phrase rather than synonyms, AI systems encounter a consistent signal each time they crawl the cluster. That consistency strengthens the topical association between anchor text, linking page, and destination page, reducing the ambiguity that suppresses citation confidence.

### Fixing internal linking problems: orphan pages, broken links, and link distribution

The three most common internal linking problems each damage AI citation rates in a different way:

| Problem | What it does | Fix |
|---------|-------------|-----|
| **Orphan pages** | Receive no link equity, invisible to AI crawlers | Find one related page and add a contextual link |
| **Broken links** | Waste crawl budget on dead URLs, strand equity | Audit quarterly, fix or redirect all 4xx links |
| **Uneven distribution** | Key pages underlinked, authority concentrated in few pages | Map link equity from high-value donor pages to priority targets |

### Using the AI search revolution to find internal linking opportunities

The [AI search revolution](https://firstmotion.com/insights/from-keywords-to-conversations-the-ai-search-revolution-in-b2b-saas) changed what internal linking needs to achieve: not just search engine rankings but AI citation probability. Google Search Console provides a Links report showing the internal links pointing to each page. Pages with zero or few internal links are your orphan page candidates and most urgent internal linking opportunities. Sorting by incoming internal links reveals which valuable pages are receiving less link equity than they should, and cross-referencing against your pillar and cluster architecture shows the structural gaps suppressing AI citation rates.

### AI powered internal linking tools for B2B software brands

| Tool | What it does |
|------|-------------|
| **Ahrefs Link Opportunities** | Scans crawled pages for keyword mentions matching pages that rank for those terms elsewhere on the site |
| **Semrush Site Audit** | Flags internal linking issues including orphan pages, broken links, and underlinked key pages |
| **LinkWhisper** | Suggests contextual internal link placements inside content as you write or edit |
| **Inlinks** | Builds entity-based internal linking maps across a full content library |

For brands with large content libraries, these tools dramatically reduce the time required to find and add links across hundreds of pages.

### Effective internal linking in practice: good internal linking across your site

Effective internal linking requires consistent attention, not a one-off fix. The brands we work with that maintain a consistent internal linking cadence consistently outperform those that treat it as a launch-day task. Good internal linking across your site means every key page is connected, every new piece of content is linked on publish day, and the pillar-cluster structure stays coherent as the site scales.

Run through this before publishing and monthly after:

- Every page is linked to from at least one genuinely relevant page
- Every pillar page links to every cluster page, and every cluster page links back to its pillar
- Anchor text on all key internal links is descriptive and matches the destination page's primary topic
- No broken internal links exist anywhere on the site. Run a crawl audit quarterly
- New content gets linked from at least two existing relevant pages on publish day
- The Google Search Console Links report is reviewed monthly to catch orphan pages and underlinked key pages
- Blog posts and cluster pages link sideways to two to three sibling pages on related topics

## If your internal linking structure isn't supporting AI citations, here's where to start

Most B2B software sites we audit aren't missing good content. They're missing the structural signal that tells AI systems how that content relates to everything else on the site. Orphan pages, generic anchor text, and disconnected topic clusters are fixable problems that produce results faster than most content programmes once addressed.

Most of what we find in these audits is fixable quickly. If that sounds familiar, talk to the FirstMotion team and we'll show you exactly where the gaps are before recommending anything.

Most brands we audit have strong content and weak link architecture. Our ContextualJourney™ platform maps exactly how AI systems navigate your site and shows you the structural gaps before we recommend anything.
</content>

---

# Does Schema Markup Increase Generative Search Visibility?

Source: https://firstmotion.com/insights/does-schema-markup-increase-generative-search-visibility

Schema markup helps AI systems understand your content, but the Ahrefs study published in May 2026 found it doesn't directly increase AI citations. This guide explains what schema actually does for AI Overview visibility, why 53% of AI-cited pages include structured data without that causing the citations, and where to focus GEO investment instead.

Schema markup helps AI systems understand your content, but the Ahrefs study published in May 2026 found it doesn't directly increase AI citations. That finding surprised a lot of SEO teams who had been told schema was the unlock for AI Overview visibility. The evidence tells a more useful story.

## Key takeaways

- Ahrefs tracked 1,885 pages adding JSON-LD schema and found no meaningful citation uplift across Google AI Overviews, AI Mode, or ChatGPT
- 53% of AI-cited pages already include structured data, making schema a floor condition rather than a citation driver
- Schema markup is necessary groundwork for entity recognition and AI understanding, even when it doesn't directly produce citation gains
- Organisation schema and entity linking, not page-level schema alone, produce measurable improvements in AI Overview visibility

Schema is one of those topics where the industry consensus ran ahead of the evidence. The teams we work with at FirstMotion had often already implemented schema across their sites before coming to us, and still had near-zero AI citations for their most important queries. Our ContextualJourney™ platform maps this gap at the entity level, showing where AI systems lose confidence in a brand's identity before they ever evaluate the content. Schema is part of the foundation, but it's a long way from the whole story.

## How schema markup helps AI search engines understand your content

Schema markup is a specific code vocabulary added to a website's HTML, typically as a JSON-LD code snippet, that describes content to search engines and AI systems in machine-readable terms. JSON-LD is the recommended implementation format according to Google Search Central, and it's what AI engines including OAI-SearchBot and PerplexityBot process at crawl time. Rather than leaving AI models to infer meaning from unstructured text, schema markup defines entities, relationships, and context explicitly.

In March 2025, both Google and Microsoft [confirmed publicly](https://www.schemaapp.com/schema-markup/what-2025-revealed-about-ai-search-and-the-future-of-schema-markup/) that they use schema markup for their generative AI features. Krishna Madhaven from Microsoft described schema as a "steering" mechanism that builds AI confidence in the correct answer for a user's query. Schema markup also supports voice assistants and semantic search by clarifying query nuances and removing ambiguity at the ingestion stage.

There are over 800 schema types available covering various types of content, from articles and businesses to products, events, and how-to written guides. The schema types that matter most for AI search are covered in the section below.

## What the Ahrefs study found: schema markup and AI Overviews

[Ahrefs published a controlled study in May 2026](https://ahrefs.com/blog/schema-ai-citations/) tracking 1,885 pages that added JSON-LD schema between August 2025 and March 2026, matched against 4,000 control pages with similar AI citation histories. Citation changes were measured 30 days before and after schema addition across Google AI Overviews, Google AI Mode, and ChatGPT using a matched difference-in-differences methodology that strips out platform-wide trends.

| Platform | Citation change | Verdict |
|----------|-----------------|---------|
| **Google AI Mode** | +2.4% | Statistically indistinguishable from noise |
| **ChatGPT** | +2.2% | Statistically indistinguishable from noise |
| **Google AI Overviews** | -4.6% | Small but statistically significant; not confidently attributable to schema |

Four separate statistical tests all pointed the same way. Adding schema markup produced no major uplift in citations on any AI platform.

The study's scope constraint matters. Every page in the sample already had a meaningful AI Overview citation baseline before schema was added. The finding is that adding schema to a page already on the AI citation track doesn't move the needle. It says nothing about how schema performs for pages with no citation baseline at all. In our entity audits, we consistently find brands in exactly that position. For those brands, schema is still the right first step.

## Why 53% of AI cited pages use structured data

According to Ahrefs' analysis of 6 million URLs, 53% of AI-cited pages include structured data, and pages with structured data are almost three times more likely to appear in AI Overviews than pages without it. Both facts are accurate, and neither contradicts the other. Well-maintained sites with high quality content, strong domain authority, and genuine topical expertise tend to implement schema. The correlation is a byproduct of those sites, not caused by the schema itself.

In practice, the brands we audit with strong AI citation rates almost always have schema in place alongside strong entity presence. The schema didn't cause the citations, but its absence would have introduced friction the other signals couldn't fully compensate for. This is the correlation-causation gap the Ahrefs controlled study was designed to test.

When you strip out other factors by matching treated pages against equivalent control pages, schema's independent contribution disappears. For B2B software brands, schema is a baseline hygiene requirement rather than a citation lever. Implementing it removes unnecessary ambiguity for AI tools. Deploying it sitewide and expecting a step-change in generative results produces the same outcome the Ahrefs study found.

## Schema types for AI search: Article, Person schema and FAQ schema

Not all schema types carry equal weight for AI search. The types that matter most are the ones that build entity clarity and content extractability for AI users, not the ones that produce rich results in traditional search.

| Schema type | What it communicates to AI | Why it matters for generative results |
|-------------|---------------------------|---------------------------------------|
| **Organisation** | Brand identity, category, service areas, sameAs URLs | Resolves entity disambiguation across AI platforms |
| **Person** | Author credibility, affiliations, published work | Establishes author bio signals AI models evaluate for E-E-A-T |
| **Article** | Content type, publication date, authorship | Produces accurate AI generated answers by giving AI models structured metadata |
| **FAQ** | Question and answer pairs in extractable format | Improves content extractability for AI Overviews even without FAQ rich results |
| **Product / SoftwareApplication** | Features, pricing, availability | Describes product capabilities accurately in AI generated answers |

[Google restricted FAQ rich results](https://developers.google.com/search/docs/appearance/structured-data/faqpage) to authoritative government and health websites in August 2023, with full deprecation completed in May 2026. FAQ schema still improves content extractability for AI systems. Keeping schema markup updated to match visible page content is increasingly important: AI models compare structured data against rendered HTML, and mismatches reduce citation confidence rather than building it.

## Organisation schema and entity recognition for AI systems

Organisation schema is the single most strategically important schema type for B2B software brands focused on AI search visibility. It connects all digital signals associated with a business into a single, unambiguous entity that AI systems can identify, verify, and trust as a source. The sameAs property is where the real work happens: it links your website entity to your Wikipedia page, Wikidata entry, LinkedIn profile, and other authoritative URLs that AI platforms use as reference points for the same entity.

In our audits, incomplete or missing sameAs properties in Organisation schema are one of the most common fixable entity gaps we find, and one of the fastest to resolve. [Schema App's entity linking study](https://www.schemaapp.com/schema-markup/case-study-entity-linking-increases-aio-visibility/) showed a 19.72% increase in AI Overview visibility after implementing entity linking that connected on-page entities to authoritative external knowledge bases including Wikipedia, Wikidata, and Google's Knowledge Graph. That result came from connected schema with entity linking across authoritative references, rather than from adding basic JSON-LD schema types to existing pages.

## Linked data, knowledge graph and AI visibility

Most of the apparent contradiction in the research comes down to one distinction: schema markup versus connected schema with entity linking. Adding JSON-LD to a page tells AI systems what that page is about. Connecting page entities to external reference databases via sameAs references tells AI systems that the entity on this page is the same entity they already know from Wikipedia and Wikidata. AI models can then resolve the entity to a known identity rather than treating it as an ambiguous text string.

Google's Knowledge Graph acts as the reference layer AI platforms draw from when forming their understanding of entities. A brand with a verified Knowledge Graph entry linked to its schema markup enters generative results with significantly higher confidence than a brand relying solely on page content. Building this connection takes longer than deploying JSON-LD. It's also what the evidence shows actually moves AI Overview visibility.

## Schema markup strategy for AI search in 2026

A practical schema strategy treats markup as entity infrastructure rather than a citation shortcut. Four priorities in sequence:

1. **Deploy Organisation schema sitewide** with complete sameAs references to Wikipedia, Wikidata, and LinkedIn. This produces the strongest entity recognition gains across all major AI platforms
2. **Implement Article schema on all published content** with accurate authorship, publication dates, and category. Keep it updated to match visible page content; mismatches reduce AI confidence
3. **Add Person schema for named authors** with sameAs references to LinkedIn profiles and published work. Author bio signals are increasingly important to AI models evaluating source credibility
4. **Use FAQ schema on question and answer content** even without FAQ rich results. Run all schema through [Google's Rich Results Test](https://search.google.com/test/rich-results) to confirm accuracy before publishing

For B2B software brands, SoftwareApplication schema is also worth implementing for product pages. It gives AI models the structured product data they need to describe your product accurately in generative answers.

## Rich results and what schema still delivers for AI search

Schema markup's direct value for traditional search results remains real. Rich snippets including star ratings, pricing, and review counts still appear for correctly implemented schema and still produce higher click-through rates than standard links. For B2B software brands, this traditional search value alone justifies schema investment.

Earned authority, consistent entity presence, and content that answers users' queries at the passage level drive AI citations. Schema supports all three but doesn't replace any of them. Redirect GEO investment beyond schema into [earned media and entity signals](https://firstmotion.com/insights/geo-vs-seo-whats-the-difference), the factors the evidence shows actually determine whether AI platforms cite your brand.
</content>

---

# Why Entity Authority Is the Foundation of AI Search Visibility

Source: https://firstmotion.com/insights/why-entity-authority-is-the-foundation-of-ai-search-visibility

AI systems don't rank pages, they recognise entities. This guide explains what entity authority is, why inconsistent brand information across platforms is the most common reason strong content fails to earn AI citations, and how to build the clarity, consistency, and corroboration signals AI systems need to cite your brand with confidence.

AI systems don't rank pages. They recognise entities. A brand that AI systems can clearly identify, consistently verify, and confidently associate with specific topics earns citations. A brand that exists as disconnected web pages, inconsistent profiles, and unclear positioning doesn't, regardless of how well it ranks in Google.

## Key takeaways

- Entity authority is the degree to which AI systems recognise a brand as a distinct, trustworthy source on specific topics
- Inconsistent information across platforms is the most common reason strong content fails to earn AI citations despite solid keyword rankings
- Entity authority compounds over time, making early investment in consistent signals more valuable than late-stage remediation
- Reddit and Wikipedia dominate AI citations not because of superior SEO but because they've built clear, consistent entity authority that AI systems trust

We've run a lot of entity audits at FirstMotion, and the same pattern keeps showing up. Strong content, solid organic rankings, and almost no presence in AI generated answers for the queries buyers are actually asking.

AI systems filter brands out before they reach the content. The problem is always upstream: unclear entity signals, inconsistent platform presence, or no corroboration from credible independent sources. Our ContextualJourney™ platform maps exactly where that happens before we recommend anything else.

## Why entity authority matters in AI search

Entity authority is the degree to which AI systems and search engines recognise a brand as a distinct, trustworthy source on specific topics. It builds through consistent information across platforms, citation from credible sources, and clear topical associations that AI systems encounter repeatedly across multiple independent references. Modern search engines prioritise entity authority over keyword optimisation, which means the phrases and link signals that drove traditional SEO results are now secondary to entity recognition.

AI systems don't retrieve pages by keyword match. They retrieve entities by recognition, then pull content from sources those entities are associated with. A brand AI systems can't clearly identify gets filtered out before content quality is evaluated at all. Entity authority is becoming the new PageRank: traditional SEO built authority through links, while AI systems build it through entity recognition and citation patterns. Stable entity authority also protects visibility during algorithm updates in ways that keyword-based strategies never could.

Reddit and Wikipedia dominate ChatGPT citations not because they have the best SEO, but because they've established clear entity authority. Reddit provides human-validated answers. Wikipedia offers structured, fact-checked information. Both are entities AI systems trust. For B2B software brands, entity authority can build faster than backlink profiles when the right signals are in place.

## How AI systems select sources for AI generated answers

When an AI system encounters a query, it starts with entity disambiguation: identifying which specific business, person, or organisation the query refers to and what the AI model knows about that entity from its training data and real-time retrieval sources. The content it surfaces in AI generated answers comes from entities it has already verified and associated with the relevant topic.

AI systems evaluate sources across three dimensions before deciding whether to cite them:

| Dimension | What it means | Common failure point |
|-----------|---------------|---------------------|
| **Clarity** | The AI system can identify the brand as a distinct entity with a clear name, category, and set of associations | Inconsistent naming conventions, ambiguous positioning, or no structured data create disambiguation problems |
| **Consistency** | The information AI systems encounter about the brand agrees across multiple independent sources | Different company descriptions, conflicting service lists, or varying founding dates erode trust and reduce citation probability |
| **Corroboration** | Credible, independent sources confirm what the brand claims about itself | A brand that only describes its own expertise earns lower entity authority than one confirmed by industry publications, reviews, and analyst coverage |

Clarity without consistency produces a recognisable entity AI systems don't trust. Consistency without corroboration produces a brand AI systems can identify but can't verify independently.

## Entity authority built on consistent information across platforms

Inconsistent information is the most common entity authority problem and the easiest to fix once identified. When a brand's name, description, service list, or founding details vary across its website, LinkedIn profile, G2 listing, Crunchbase entry, and third-party publications, AI systems encounter fragmented signals that reduce citation confidence. That fragmentation directly costs revenue by removing the brand from AI generated answers at the moment buyers are forming their shortlists.

Consistent NAP information (name, address, phone) is the baseline for entity recognition. For B2B software brands the requirement extends far beyond contact details. The company description, service categories, and target customer definition all need to stay stable across every platform and maintained as the brand evolves. Any significant contradiction reduces the confidence score that determines citation probability.

Practical steps to fix consistency issues:

- Audit every platform where your brand has a presence: website, LinkedIn, G2, Capterra, Crunchbase, Trustpilot, industry directories, and any publication that has covered the company
- Identify every instance where the brand description, service definition, or company details differ from the canonical version on your own website
- Update each listing systematically, prioritising platforms AI systems draw from most heavily: G2, LinkedIn, Wikipedia or Wikidata, and major industry publications
- Build a canonical brand description document and maintain it as the single source of truth for every external listing. Google's 2025 Knowledge Graph cleanup is a useful reference point for any audit: Google removed approximately 3 billion ambiguous or outdated entities that June

## Topical authority and entity authority: why both are required

Topical authority tells AI systems what a brand knows about. Entity authority tells AI systems whether they can trust that brand as a source. Both are necessary because AI systems evaluate source credibility before content relevance. A well-defined entity associated with a specific topic earns citation opportunities that topical content alone never produces.

A B2B software company with excellent product comparison content but inconsistent brand identity across platforms will lose citation opportunities to a competitor whose entity signals are clearer, even when the content is weaker. Clear entity signals combined with deep topical coverage produces consistent AI citation rates and the revenue that follows.

Topical consistency is itself an entity signal. A brand that publishes consistently on a narrow set of topics over a sustained period builds a stronger association between its entity and those topics than one that publishes broadly. AI systems encounter the topically consistent brand as the go-to source for a specific subject area repeatedly, which compounds the entity-topic association driving citation recommendations in AI generated answers.

## The role of structured data in building entity authority

Structured data is the most direct mechanism for communicating entity signals to AI systems. JSON-LD schema markup describes your brand as a machine-readable entity: its name, type, founding date, address, service areas, and relationships to other entities. AI systems use this important information to build a precise entity identity before they process any content on the page.

Organisation schema, Person schema for named founders, and Article schema for published content all contribute to entity clarity. Complete structured data reduces disambiguation errors and increases citation attribution accuracy. Inconsistent or missing schema creates the same fragmentation problems as inconsistent NAP data. Schema-based entity signals protect visibility during algorithm updates, and modern search engines prioritise these structured definitions over keyword-based content signals.

Wikipedia and Wikidata entries serve as foundational entity signals for brands that qualify. Both platforms feed directly into the knowledge graphs AI systems reference when resolving entity queries. A brand with a Wikipedia entry that consistently describes its category, founding, and expertise has a meaningful entity authority advantage over one that relies solely on owned content and structured data.

## How corroboration from credible sources builds entity authority

Self-described expertise carries far less weight than expertise confirmed by independent, credible sources. [The AI search revolution](https://firstmotion.com/insights/from-keywords-to-conversations-the-ai-search-revolution-in-b2b-saas) shifted the weight of brand authority from owned signals to earned ones, and entity authority is where that shift shows up most directly in AI citations. AI systems look for corroboration: the same claims about a brand's expertise and trustworthiness appearing across multiple independent sources the AI already recognises as credible.

The corroboration sources that carry the highest weight are:

- **Industry publications and trade press:** editorial coverage in sector-specific publications AI systems have indexed as authoritative in your category
- **G2, Capterra, and Trustpilot:** review platforms that provide third-party validation of product capabilities. AI systems draw heavily from these when forming brand assessments
- **LinkedIn articles and company pages:** LinkedIn's domain authority and structured professional content make it one of the strongest corroboration sources for B2B brands. Named partnerships and client relationships referenced on LinkedIn also strengthen entity associations
- **Reddit and specialist forums:** community validation from platforms AI systems treat as human-verified expertise. A brand mentioned positively in relevant technical conversations earns entity authority that owned content can't replicate
- **Analyst briefings and industry reports:** Gartner, Forrester, or IDC coverage signals to AI systems that an independent expert has evaluated and verified the brand's expertise

For B2B software startups, entity authority can build faster than backlink profiles. Consistent, authoritative content on specific topics combined with structured profiles across key platforms and early earned media creates a foundation AI systems begin recognising quickly once the signals are coherent and consistent.

## Entity disambiguation: making sure AI systems know which brand you are

Entity disambiguation is the process by which AI systems distinguish one brand from all similar organisations or people with overlapping names or characteristics. A brand whose name matches a common phrase, or one operating in a competitive category with similar-sounding competitors, faces disambiguation challenges unless entity signals are explicit and consistent.

Practical steps to improve disambiguation:

- Use structured data to explicitly define your brand type, founding date, location, and primary service category. The more specific the entity identity definition, the less room for disambiguation errors and the more accurately AI generated answers describe your brand
- Ensure your brand name appears consistently across all platforms in exactly the same format, including capitalisation, spacing, and any legal suffixes
- Build explicit associations between your brand and the specific topics and use cases you want AI systems to link to you. Content that names the entity alongside specific topic clusters, published consistently over time, compounds these associations
- Create or claim your Wikidata entry. Wikidata is a structured reference database that many AI systems use as a primary entity resolution source, and a verified entry significantly reduces disambiguation errors across multiple AI platforms

## Building entity authority: a practical framework

Entity authority builds from the outside in. The signals that matter most to AI systems come from independent, credible sources that confirm what your own website and structured data claim about your brand. Owned content is necessary but not sufficient on its own. The four workstreams below need to run in parallel to produce measurable gains.

| Workstream | What it involves | Priority actions |
|-----------|------------------|------------------|
| **Entity foundation** | Establishing a clean, consistent entity identity across all platforms | Audit all listings, deploy complete JSON-LD schema, create or claim Wikidata entry, establish canonical brand description |
| **Topical authority signals** | Building consistent topic associations AI systems recognise over time | Publish on a focused topic set, name the brand entity explicitly alongside topic clusters in every piece of content |
| **Third-party corroboration** | Earning independent confirmation of expertise from credible sources | Earned media in industry publications, analyst briefings, G2 review growth, LinkedIn presence for named experts |
| **Consistency monitoring** | Maintaining signal accuracy as the brand evolves | Quarterly listing audits, track AI platform descriptions for inaccuracies, monitor citation rates across platforms |

## If your brand's entity authority is unclear to AI systems, here's where to start

The most common entity authority problem we find at FirstMotion isn't that brands are unknown. It's that they're inconsistently known. Different descriptions on different platforms, schema markup that contradicts the website, and no structured reference presence in Wikidata or industry databases all mean AI systems encounter the brand, can't confidently resolve the entity, and skip it in favour of competitors they can verify without ambiguity.

[Our GEO approach](https://firstmotion.com/insights/geo-vs-seo-whats-the-difference) starts with a full entity audit before any content or earned media work begins. Comparing your brand's presence against competitors in early audits consistently reveals the entity authority gaps driving the citation rate difference.

Most brands we audit are inconsistently known across platforms, not unknown. Our ContextualJourney™ platform runs a full entity audit and shows you exactly where AI systems lose confidence in your brand before we recommend anything.
</content>

---

# How to Track Brand Visibility Across Multiple AI Platforms

Source: https://firstmotion.com/insights/how-to-track-brand-visibility-across-multiple-ai-platforms

37% of product discovery queries now start in AI interfaces, yet most brands track AI visibility on one platform with no consistent framework. This guide covers the four core visibility metrics, the tools that measure them across ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Gemini, and Meta AI, and the tracking cadence that turns citation data into competitive intelligence.

Most brands tracking AI visibility are doing it wrong. They check one platform occasionally, run manual prompts with no consistency, and draw conclusions from data that shifts week to week without a framework to interpret it. Cross-platform AI visibility tracking requires a systematic approach across every major AI engine your buyers actually use.

## Key takeaways

- AI visibility tracking requires separate measurement across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, and Meta AI
- 37% of product discovery queries now start in AI interfaces, making cross-platform visibility a commercial priority not just an SEO metric
- Citation rate, share of voice, sentiment score, and prompt coverage are the four core visibility metrics every tracking setup needs
- Traditional SEO tools and rank trackers don't capture AI visibility at all, requiring a dedicated AI visibility tracker or structured manual tracking framework

Tracking brand visibility in the AI era is a problem most teams haven't solved. The platforms are different, the citation patterns are inconsistent, and the data doesn't sit in any tool your team already uses. Our ContextualJourney™ platform maps brand presence across every major AI engine at the prompt level, surfacing the gaps that standard analytics miss entirely. This guide covers the metrics, tools, and tracking framework you need.

## Why cross platform visibility tracking is different from traditional SEO

Traditional SEO tracks one search engine through one interface: Google Search Console gives you impressions, clicks, and position for every tracked keyword. Cross-platform AI tracking covers six distinct platforms, each operating on different retrieval architectures, drawing from different source pools, and producing different citation patterns for identical queries.

Ahrefs' AI visibility study confirms AI Mode and AI Overviews share only 13.7% URL overlap. A brand tracking well on one surface can be entirely invisible on the other. Traditional rank tracking tells you nothing about what ChatGPT says about your brand, how Perplexity describes your competitors, or whether Meta AI recommends your product when a buyer asks for options in your category.

AI search visibility tracking also requires a different unit of measurement. Traditional SEO measures positions and clicks. AI tracking measures citation rates, share of voice, and sentiment across a consistent prompt set run repeatedly on each platform. That data doesn't flow into Google Analytics or Search Console automatically. It requires either a dedicated AI visibility tracker or a structured manual process on a fixed cadence.

## Google AI, AI Mode, Meta AI and the major platforms to track

Effective cross-platform tracking starts with knowing which platforms your buyers actually use. Each major platform has a distinct user base, citation behaviour, and content preference that makes it a separate tracking priority.

| Platform | Active users | Best for tracking |
|----------|--------------|------------------|
| **ChatGPT** | 900M weekly (Feb 2026) | Brand recommendations, product comparisons, vendor shortlisting |
| **Google AI Overviews** | 2B+ monthly | Informational queries, category-level brand visibility |
| **Google AI Mode** | 1B+ monthly (May 2026) | Complex multi-part queries, B2B research queries |
| **Perplexity** | 100M+ monthly | Research-led queries, cited source tracking |
| **Gemini** | 900M+ monthly (May 2026) | Google ecosystem integration, mobile AI queries |
| **Meta AI** | 1B+ monthly | Consumer brand queries, social discovery contexts |

A brand can appear consistently in ChatGPT recommendations while being entirely absent from Google AI Mode for the same category queries. Each platform uses different AI models, draws from different source pools, and weights authority signals differently. Each one needs its own tracking setup, prompt set, and baseline before any cross-platform comparison is meaningful.

## The four core AI search visibility metrics

Four metrics form the foundation of every tracking setup. Without them, you can't compare platforms, benchmark competitors, or tell whether GEO efforts are actually working.

- **Citation rate:** the percentage of relevant prompts where your brand appears in AI generated answers on a given platform. Track it weekly per platform from a consistent prompt set. It's the primary AI visibility metric and the direct equivalent of keyword ranking in traditional SEO
- **AI share of voice:** your brand's citations as a percentage of all brand citations across your tracked prompt set. A brand appearing in 12 out of 50 prompts where four competitors also appear has a share of voice figure that reveals competitive position, not just absolute visibility
- **Brand position:** where your brand first appears in an AI response. First-position mentions drive significantly more buyer consideration than trailing references. Tracking position change over time shows whether GEO activity is moving your brand up or down
- **Sentiment score:** how AI systems describe your brand. When AI describes you with language such as "reportedly" or "though some users find it complex," that erodes buyer confidence before they reach your site. Sentiment analysis across platforms reveals whether your description varies by engine and where corrections are needed

Most dedicated AI visibility tools calculate a combined AI visibility score from these four metrics automatically. Manual tracking requires logging each one per platform per prompt run.

## AI visibility tracker tools: the best AI visibility platforms compared

A few tools now dominate the category for tracking AI mentions, monitoring share of voice, and running sentiment analysis across all major AI platforms. The right choice depends on team size, budget, brands tracked, and depth of competitive analysis needed.

| Tool | Best for | Platforms tracked | Pricing |
|------|----------|------------------|---------|
| **Profound** | Enterprise teams needing maximum depth | ChatGPT, Perplexity, Claude, Gemini, Grok, Copilot, Meta AI, DeepSeek, AI Overviews + | Enterprise |
| **Peec AI** | Agencies tracking multiple brands | ChatGPT, Perplexity, Gemini, AI Overviews | From $49/mo |
| **Otterly AI** | SMB teams and GEO audits | ChatGPT, AI Overviews, AI Mode, Perplexity, Gemini, Copilot | Free tier available |
| **Ahrefs Brand Radar** | Teams already using Ahrefs | AI Mode, ChatGPT, Perplexity, AI Overviews | Included in Ahrefs plans |
| **SE Ranking AI Toolkit** | SMBs combining traditional SEO and AI tracking | AI Overviews, ChatGPT, Perplexity, Gemini | From $65/mo |

Profound draws on 1.5 billion real user AI conversations across 10+ AI engines, updated daily. Its Answer Engine Insights product tracks which AI generated answers mention your brand, in what context, and with what sentiment across the broadest platform coverage in the category. For enterprise teams managing multiple brands across multiple markets, that depth justifies the price.

Otterly AI is the clearest entry point for SEO teams exploring AI visibility for the first time. Its free tier covers six platforms and includes a GEO audit checking 25+ on-page factors for AI readiness. For teams that want cross-platform monitoring without enterprise pricing, it's the natural first stop.

## How to build a cross platform AI visibility tracking framework

Most teams that struggle with tracking aren't using the wrong tools. They run prompts inconsistently, compare platforms with different prompt sets, and draw conclusions from data that reflects methodology differences rather than genuine visibility changes. A consistent framework fixes all three.

A practical cross-platform tracking setup requires four components:

- **A consistent prompt set:** 30 to 50 prompts covering buyer questions, category queries, comparison queries, and problem-led queries. The same set runs on every platform at every interval. Changing the prompt set resets your baseline
- **Platform-specific tracking:** each platform tracked separately with its own citation rate, share of voice, and sentiment log. Cross-platform aggregation only makes sense after each platform's data is individually clean
- **A fixed cadence:** weekly prompt runs for citation rate and share of voice, monthly sentiment reviews, quarterly competitive audits. Standardised UTM tagging on key pages keeps AI referral traffic data in GA4 consistent with visibility monitoring data from tracking tools
- **Privacy-aware data handling:** cross-platform tracking covers user interactions across web and mobile. Businesses should pay attention to privacy requirements when collecting and storing visibility data, particularly enterprise teams operating across multiple jurisdictions

## Custom prompt tracking: building the right prompt set for your brand

The prompts you run determine what your data actually measures. Generic prompts produce generic data. Prompts built around your buyers' specific questions and your category's decision criteria produce data that drives real GEO decisions.

An effective custom prompt set covers four query types:

- **Category queries:** "what is the best [product type] for [use case]" tests brand mentions in the widest awareness-level searches and reveals which brands AI systems recommend as category defaults
- **Comparison queries:** "[your brand] vs [competitor]" tests how AI platforms frame your competitive positioning. Sentiment analysis matters as much as citation rate because inaccurate framings directly affect buyer decisions
- **Problem-led queries:** "how do I solve [specific pain point]" tests whether your content earns AI citations for the problems your product addresses. These often surface content gaps that category queries hide
- **Recommendation queries:** "which [product type] should I use for [specific context]" tests AI platform recommendation behaviour at the moment of active vendor evaluation

Running the same custom prompt in ChatGPT, Perplexity, Google AI Overviews, and Gemini simultaneously reveals which AI models favour your content and which require different authority signals to earn citations.

## Tracking AI competitor research, content gaps and share of voice

Competitive AI visibility tracking reveals the gaps that internal citation rate data can't surface alone. A brand can improve its citation rate consistently while losing competitive ground if competitors are improving faster. Share of voice is the only metric that shows relative competitive position in AI generated answers.

Effective AI competitor research covers three dimensions:

- **Citation rate comparison:** your citation rate versus each competitor's on the same prompt set, same platform, same time. This controls for methodology differences and produces the cleanest competitive signal
- **Platform-specific gaps:** which platforms each competitor outperforms you on and by how much. A competitor dominating ChatGPT but absent from AI Overviews has a different vulnerability profile from one with balanced platform coverage
- **Content gap analysis:** which specific prompt types each competitor earns citations for that your brand doesn't. These gaps map directly to the content and authority work your GEO strategy needs to prioritise

Citation tracking also identifies high-performing internal pages by revealing which URLs earn AI citations across platforms. Pages cited consistently carry strong authority signals worth strengthening, updating, and building topical clusters around.

## AI search performance: tracking visibility and reporting progress

The metrics that matter for AI visibility monitoring don't appear in Search Console, Google Analytics, or any traditional rank tracker. You need a separate reporting layer.

A practical AI visibility reporting framework includes:

- **Weekly citation rate trend:** citation rate per platform over a rolling 12-week window, showing direction and velocity of improvement or decline on each engine
- **Cross-platform share of voice:** your brand's citation percentage across all tracked platforms combined, giving competitive AI presence in a single number
- **Sentiment tracking:** positive, neutral, and negative scores per platform, tracked monthly. Sentiment shifts faster than citation rate in response to earned media activity, making it an early indicator of AI description quality
- **Prompt coverage:** the percentage of your tracked prompt set surfacing your brand at least once, showing how broad your AI visibility footprint is across your buyers' full question set
- **Referral traffic from AI sources:** AI-referred sessions in GA4 alongside visibility data, connecting citation rate changes to commercial outcomes. Identity resolution techniques connecting anonymous AI referral activity to authenticated CRM behaviour reveal AI's influence on pipeline beyond direct referral clicks
- **Visibility gaps:** prompts where competitors appear and your brand doesn't, updated quarterly

Monitor visibility weekly. Citation rates can fall suddenly when a competitor earns a new authoritative list appearance or when an AI platform updates its retrieval behaviour. Weekly monitoring is the only way to catch these drops before they compound.

## How structured data improves cross platform AI visibility tracking

Pages with complete JSON-LD schema markup are more extractable at the AI ingestion stage, improving citation probability and producing more accurate brand descriptions when AI systems retrieve and summarise them. This reduces the risk of inaccurate brand mentions that damage buyer confidence before anyone reaches your site.

From a tracking perspective, structured data helps citation tools identify which specific page types earn AI citations. Pages with complete schema coverage consistently earn citations at higher rates than equivalent pages without it, and that gap is measurable. For enterprise teams tracking AI visibility across multiple brands and markets, schema consistency also reduces the platform-to-platform sentiment variation that makes cross-platform data harder to interpret.

## Answer engines and AI search engines: why the AI era requires a different mindset

A brand ranking position one in Google can earn zero citations in ChatGPT for the same query. A brand earning strong AI citations may see minimal referral traffic from those citations because most AI conversations produce no click. Success on one channel doesn't predict success on the other.

The commercial impact of AI citations runs through influence rather than traffic. A brand recommended by an AI answer engine builds buyer consideration before that buyer runs a Google search, visits a website, or enters a CRM. Traditional attribution models miss this entirely, systematically undercounting AI's contribution to pipeline and revenue.

AI search engines also behave differently from traditional search engines on consistency. Traditional search results for a given keyword are relatively stable week to week. AI answers for the same prompt vary significantly across runs, platforms, and time as AI models update. Visibility monitoring therefore needs to run more frequently than traditional rank tracking to produce reliable data.

## If your brand doesn't know where it stands across AI platforms, here's where to start

The most common finding in our FirstMotion cross-platform audits is that a brand's performance varies dramatically by platform. Strong ChatGPT citations sit alongside near-zero Google AI Mode visibility. High citation rates on informational queries hide complete absence from comparison and recommendation queries. Our ContextualJourney™ platform runs your full prompt set across every major AI engine and shows you exactly where the gaps are before we recommend anything.

Talk to the FirstMotion team to get a cross-platform AI visibility baseline for your brand. We'll map your citation footprint, benchmark it against your primary competitors, and identify the specific gaps driving the difference.
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# How to Prove the Business Impact of AI Search Visibility

Source: https://firstmotion.com/insights/how-to-prove-the-business-impact-of-ai-search-visibility

AI-referred traffic converts at 14.2% versus Google organic's 2.8%, yet only 16% of Fortune 500 companies currently track AI search performance. This guide covers the three-track commercial proof framework for GEO: AI visibility data, downstream commercial signals, and controlled testing, and shows how to connect citation rates to pipeline, revenue, and leadership-ready reporting.

Most GEO campaigns stall before the team can prove they worked. AI visibility is real, AI referral traffic is real, and the commercial impact is measurable. The measurement framework just requires a different set of tools from anything traditional SEO reporting provides.

## Key takeaways

- AI-referred traffic converts at 14.2% versus Google organic's 2.8%, making each AI citation worth roughly five times a traditional organic click
- 85.5% of AI citations come from earned media sources, not brand-owned websites, shifting where GEO investment produces the highest return
- Only 16% of Fortune 500 companies currently track AI search performance, creating a significant first-mover measurement advantage
- AI-referred leads convert 32 to 68% higher than other traffic sources because AI recommendations pre-qualify buyers before they click

The hardest conversation in GEO happens with the finance director who wants to know what the channel is actually worth. We've sat in that room a lot at FirstMotion. The question is always the same: show me the revenue, not the citations. Our ContextualJourney™ platform was built to close that gap, connecting AI citation data to pipeline metrics in a single view. This guide covers every layer of the commercial proof stack we use to make that case.

## Why proving GEO business impact is harder than traditional SEO

Unlike traditional SEO, GEO doesn't produce a clean attribution story where a keyword ranks, a user clicks, a session records, and a conversion fires. A brand cited in a ChatGPT conversation may never produce a trackable click. A buyer who read an AI summary on Tuesday and visited the site directly on Thursday shows as direct traffic in GA4.

Gartner's 2026 search prediction puts traditional search volume down 25% by 2026. G2's April 2026 research confirms 51% of B2B software buyers now start their research with an AI chatbot more often than with Google, up from 29% just eleven months earlier. [The AI search revolution](https://firstmotion.com/insights/from-keywords-to-conversations-the-ai-search-revolution-in-b2b-saas) has moved faster than most analytics stacks have adapted, and the buyers your SEO reporting was built to track are increasingly doing their research in a channel your tools can't see.

Proving GEO business impact requires three parallel proof tracks:

- **AI visibility data:** citation rates, share of voice, and sentiment scores across AI platforms
- **Downstream commercial signals:** AI referral sessions, conversion rates, and pipeline influence in the CRM
- **Controlled testing:** A/B location comparisons, pre and post content analysis, and geo-fencing measurement that isolates the causal impact of GEO activity from background noise

## The commercial case for generative engine optimization in 2026

The numbers that make the business case for GEO come from tracked cohorts of AI-referred visitors measured against organic benchmarks. Involve Digital's 2026 data shows AI-referred leads converting 32 to 68% higher than traditional organic traffic. The behavioural difference shows up immediately: fewer objections, better-informed questions, and clearer problem definitions because the AI recommendation has already done the qualification work.

AI-referred visitors also spend 48% more time on site and view 13% more pages per visit than non-AI traffic, according to Adobe's Q1 2026 analysis of over one trillion retail visits. A brand earning 500 AI-referred sessions per month at a 14.2% conversion rate generates 71 conversions from that channel alone. The same 500 sessions arriving as Google organic traffic at a 2.8% conversion rate generates 14. That's a 5x difference in commercial output from identical visit volume.

Only 16% of Fortune 500 companies currently track AI search performance, which means early movers aren't competing against the full market. They're competing against 16% of it. The window to build a first-mover measurement advantage is still wide open.

## GEO metrics: the three proof tracks for measuring success

Proving GEO's business impact requires three distinct measurement tracks running in parallel. Each answers a different question and produces a different type of evidence. Combining all three produces the commercial proof stack that survives scrutiny from finance and leadership teams.

| Proof track | What it answers | Primary tools |
|---|---|---|
| **AI visibility data** | Is our brand appearing in AI responses and with what frequency, position, and sentiment? | Profound, Peec AI, Otterly AI, Ahrefs Brand Radar |
| **Downstream commercial signals** | Is AI visibility producing sessions, leads, and revenue? | Google Analytics 4, CRM pipeline tracking, UTM parameters |
| **Controlled testing** | Is GEO activity causing the commercial outcomes, not just correlating with them? | A/B location comparisons, pre/post content analysis, geo-fencing measurement |

Running all three tracks together matters because visibility data without commercial signals becomes a vanity metric, and commercial signals without visibility context can't attribute outcomes to GEO. The controlled testing track is what converts correlation into causation and produces the evidence that justifies sustained investment.

## Real world impact: tracking AI citations and GEO performance

Citation frequency is the primary GEO metric for visibility measurement: how often your brand appears in AI responses to prompts relevant to your category, across which platforms, and in what position. Ahrefs' AI visibility study confirms that 26% of brands have zero mentions in AI Overviews, which means establishing a citation baseline comes before any other GEO metric has meaning.

The citation frequency metrics that connect most directly to real world impact are:

- **Citation frequency:** how often your brand appears in AI responses across your target prompt set, measured weekly. A brand discovering zero citations across 50 relevant prompts has the most important fix in its GEO practice identified immediately
- **Share of voice:** your brand's citations as a percentage of all brand citations in your category, giving the competitive context that raw citation counts miss
- **Brand position:** the position at which your brand appears in each AI response. First-position mentions drive disproportionately more buyer consideration than trailing references and matter to partners evaluating brand credibility
- **Sentiment score:** how AI platforms describe your brand. Positive descriptions accelerate buyer confidence; qualifying language such as "reportedly" or "some users say" erodes it before the user reaches your site

Smarter decision-making starts with consistent prompt tracking. Run 30 to 50 prompts across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, and Gemini weekly. The pattern across four to six weeks reveals which platforms, query types, and competitors require the most focused GEO investment.

## Downstream commercial signals: connecting AI citations to revenue

AI visibility metrics confirm your brand is appearing in AI responses. Downstream commercial signals confirm that appearance is producing revenue. Connecting these two layers efficiently turns GEO from a marketing exercise into a business case most finance teams can follow.

AI referral traffic arrives in GA4 via several sources: chat.openai.com for ChatGPT, perplexity.ai for Perplexity, and gemini.google.com for Gemini. Building a dedicated GA4 channel group for these sources isolates AI driven visits from generic referral and direct traffic buckets, giving teams access to data that was previously loading into the wrong bucket and obscuring GEO's contribution entirely.

The commercial signals to track alongside citation frequency are:

- **Assisted conversions:** deals where an AI-referred session appeared in the conversion path before the final converting touch. These reveal GEO's influence on deals it didn't close directly and matter most when making the case to leadership
- **Close rate by source:** the percentage of AI-referred leads that progress to closed deal, compared to organic and paid benchmarks. Because AI recommendations pre-qualify buyers before they click, close rates for AI-referred leads consistently outperform other channels
- **Revenue per location:** comparing sales performance by geography alongside AI citation rates by region reveals where GEO investment produces the highest commercial return and surfaces regional performance gaps early
- **Branded search uplift:** increases in branded search volume correlating with periods of high AI citation activity, capturing zero-click AI exposure that never produces a direct referral session

## The attribution challenge and how to solve it

Attribution is the hardest problem in GEO measurement because the most common AI-influenced buyer journey doesn't produce a trackable AI referral session. A buyer asks ChatGPT for vendor recommendations on Monday, sees your brand cited, researches your website directly on Wednesday, and converts through paid retargeting on Friday. Standard last-click and multi-touch attribution models weren't designed for a channel where the most influential touchpoint produces no trackable click.

Solving the attribution challenge requires layering three approaches. First, build a custom GA4 channel group capturing all known AI referral sources including ChatGPT, Perplexity, Gemini, and Claude as a single trackable segment. Second, tag every AI-referred session in the CRM before it converts so that closed deals carry AI attribution data regardless of which channel produced the final click. Third, run controlled pre/post analysis: measure commercial metrics in the 90 days before and after a GEO campaign launch, and track sales velocity, branded search volume, and direct traffic trends that move alongside citation rate changes.

Cost per visit adds another dimension to this analysis. Dividing GEO programme investment by AI-referred sessions produces a cost per AI visit that benchmarks against paid and organic channel equivalents. For most B2B software brands running a structured GEO programme, cost per AI visit runs significantly lower than paid search cost per visit while producing significantly higher downstream conversion rates.

## Why 85% of AI citations come from earned media

The single most strategically important finding in GEO measurement changes where the investment case gets made. 5W PR's earned media study, based on analysis of over one million AI prompts, found that 85.5% of AI citations reference earned media sources, not brand-owned websites. Every founder profile, press cycle, analyst briefing, and review platform listing forms critical infrastructure for the channel that now intercepts buyers before any other touchpoint.

Brands appearing on four or more third-party platforms are 2.8x more likely to be cited in ChatGPT responses than single-platform brands, according to 5W's research. G2 review management, industry publication coverage, analyst briefings, and digital PR programmes are direct GEO investment, not brand overhead. The ROI calculation for earned media changes entirely when each piece of coverage contributes to an AI citation rate converting at 14.2%.

The conference presentation, the industry award, and the community forum post your team deprioritised as soft brand activity are all loading into the earned media base that AI systems draw citations from. Organisations that efficiently build earned media presence across multiple authoritative sources earn disproportionate AI citation share in their categories.

## Measure GEO success: building the business case for leadership

The GEO reporting framework that earns budget approval combines visibility metrics with commercial outcomes in a single view. A GEO business impact report for leadership should include:

- **Citation rate trend:** weekly citation rate across the target prompt set over the reporting period, showing direction and velocity of improvement
- **AI share of voice vs key competitors:** your brand's citation percentage relative to named competitors, demonstrating competitive progress rather than just absolute growth
- **AI-referred sessions and conversion rate:** total sessions from AI platforms in GA4 against organic benchmark, with conversion rate comparison showing the commercial quality gap
- **Assisted conversions:** deals in the CRM where an AI-referred session appeared in the conversion path, capturing influence on deals GEO didn't close directly
- **Branded search uplift:** branded query volume trend in Google Search Console, correlated against citation rate changes to reveal zero-click influence
- **Revenue attribution estimate:** AI-referred conversion volume multiplied by average deal value, producing a conservative lower-bound revenue estimate for the channel

Comparing your brand's AI presence against competitor citation rates in the same report converts a GEO update from an internal metric review into a competitive intelligence briefing. Leadership teams respond to competitive framing in ways they rarely respond to channel-specific metrics alone.
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# The KPIs and Metrics That Actually Matter for a GEO Campaign

Source: https://firstmotion.com/insights/the-kpis-and-metrics-that-actually-matter-for-a-geo-campaign

The GEO KPIs B2B software brands need to track: citation rate, AI share of voice, referral traffic conversion and sentiment scoring explained.

Most GEO campaigns fail measurement before they fail strategy. Teams track the wrong signals, confuse AI visibility with AI traffic, and report on metrics that feel familiar rather than metrics that reflect what generative engine optimization actually does.

## Key takeaways

- Citation rate is the primary GEO KPI: the percentage of relevant prompts where your brand appears in AI generated answers
- 26% of brands have zero mentions in AI Overviews, making baseline measurement the first step before any optimisation
- AI referral traffic converts at 4.4x the rate of traditional organic traffic, making it the highest-value acquisition channel most teams aren't measuring
- Share of voice in AI responses is the GEO equivalent of ranking position, and it varies significantly across AI platforms for the same query

When we start measuring GEO performance properly with a FirstMotion client, the same thing happens almost every time. Their AI citation footprint looks completely different from their Google rankings. Pages that rank well get zero AI citations. Pages that barely rank get cited repeatedly. Our ContextualJourney™ platform maps that gap in the first session, and this guide explains every metric it uses to do it.

## Generative engine optimization GEO: why organic search metrics fail

Unlike SEO, generative engine optimization GEO doesn't produce rankings, impressions, or click-through rates. A brand can appear in thousands of AI generated answers without generating a single trackable session, and a brand can rank position one in organic search while being entirely absent from every AI platform your buyers actually use.

Gartner's 2026 search prediction puts traditional search volume down 25% by 2026 as users shift to AI answer engines. G2's April 2026 research found 51% of B2B software buyers now start their research with an AI chatbot more often than with Google, up from 29% just eleven months earlier. The buyers your organic search strategy was built to reach are increasingly not there to be reached by it.

Traditional metrics fail in the AI era for three structural reasons:

- **Zero-click search:** 58.5% of US Google searches now end without a click to any website. AI summaries answer the query before the user reaches your content, meaning organic search traffic figures systematically undercount the role your content plays in buyer decision-making
- **Invisible citations:** large language models and generative AI models cite content without producing a referral session. A brand mentioned in a ChatGPT or Perplexity response earns influence that never shows up in Google Analytics or Google Search Console
- **Platform fragmentation:** traditional search engines give you one set of rankings to track. GEO requires tracking brand visibility across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, and Gemini, each of which draws from different sources and weights different signals differently

## The core GEO KPIs and metrics: what to track

GEO KPIs and metrics organise into three tiers. The first tier measures AI visibility: the raw fact of appearing in AI generated answers. The second tier measures AI traffic: the sessions and conversions that AI visibility produces. The third tier measures brand authority signals: the external evidence that drives citation rates over time.

No single metric tells the full story. A brand with high citation rates but zero AI referral traffic may have strong AI visibility but weak clickthrough prompts. A brand with strong AI traffic but low share of voice may be capturing a niche but missing the broader category queries where buyers first form their shortlists. Tracking all three tiers together is what separates a GEO measurement framework from a collection of disconnected numbers.

Setting the right GEO KPIs starts with benchmarking current performance across all three tiers before attempting optimisation. Ahrefs' AI visibility study found that 26% of brands have zero mentions in AI Overviews, which means for many brands the baseline is zero. Any positive citation rate is progress in the right direction and the foundation for tracking progress over time.

## Tier one: visibility metrics in AI responses

AI visibility metrics measure the fact of appearing in AI generated answers, not the traffic those appearances produce. These are the leading indicators of GEO success: they move before traffic does, and they reveal where content and authority gaps exist before they become revenue gaps. AI visibility tools including Profound, Peec AI, Otterly AI, and Ahrefs Brand Radar measure these signals at scale across all major AI platforms.

| Metric | What it measures | Why it matters |
|---|---|---|
| **Citation rate** | Percentage of relevant prompts where your brand appears in AI generated answers | The primary GEO KPI: directly measures whether GEO efforts are working |
| **AI share of voice** | Your brand's citation count as a percentage of all brand citations in your category | Reveals competitive positioning in AI responses that organic search rankings can't show |
| **Brand position** | The position at which your brand first appears in an AI generated response | First-position mentions drive significantly more buyer consideration than trailing references |
| **Prompt coverage** | The percentage of your target query set where your brand earns at least one citation | Reveals query gaps where competitors earn citations your brand doesn't |
| **Sentiment score** | Whether AI systems describe your brand in positive context or with qualifying language | Negative sentiment reduces citation rates over time as AI models reinforce negative associations |

Citation rate is the GEO equivalent of keyword ranking. Run a consistent set of 30 to 50 prompts across your primary AI platforms, record how often your brand appears, and track the change week on week. A steady increase confirms effective GEO efforts. A sudden drop typically signals a competitor has earned new authoritative coverage that shifted the evidence base generative AI models draw from.

## Tier two: AI traffic and engagement metrics

AI traffic metrics connect visibility to business outcomes. They're the layer where GEO becomes legible to finance and leadership teams, translating citation rates into website visits, pipeline, and revenue. Track AI traffic in GA4 by building a dedicated channel grouping for AI referral sources so AI driven visits don't merge into generic referral buckets.

AI referral traffic converts at 4.4x the rate of traditional organic search traffic, according to Semrush's 2026 analysis. Visitors from AI platforms arrive pre-qualified because the AI has already synthesised a recommendation before the click. They arrive with higher intent, clearer expectations, and stronger purchase readiness than a user who clicked a blue link in traditional organic search.

The key AI traffic metrics to track are:

- **AI-referred sessions:** total sessions arriving from AI platforms, segmented by platform in GA4. Tracking AI traffic separately from organic prevents AI driven visits from being absorbed into broader referral or direct buckets
- **AI referral conversion rate:** the percentage of AI-referred sessions that convert, compared to organic and paid benchmarks. The 4.4x conversion premium means even small AI referral volumes produce outsized commercial value
- **Revenue per AI-referred visit:** Adobe's Q1 2026 analysis of over one trillion retail visits shows AI-referred visitors generate 37% more revenue per visit than non-AI traffic, making this the clearest signal of AI traffic quality in digital marketing reporting
- **Direct traffic uplift:** brands cited frequently in AI answers see corresponding increases in direct traffic as users navigate to the site after an AI conversation. Monitoring direct traffic trends alongside referral data captures zero-click AI interactions
- **Branded search uplift:** increases in branded search volume correlating with periods of high AI citation activity give a proxy metric for AI reach across zero-click interactions

Track engagement metrics for AI-referred sessions separately from organic search sessions. AI driven visits tend to show fewer pages per session but significantly higher conversion rates because visitors arrive further along in their research process. Comparing engagement metrics between AI and organic traffic reveals the pre-qualification effect that makes AI referral traffic disproportionately valuable.

## Tier three: brand visibility and authority signals

The third tier sits outside owned analytics entirely. It covers the external signals AI systems use to form their understanding of a brand's authority, accuracy, and relevance when assembling AI driven answers. These signals don't produce traffic data directly but they determine citation rates at every other tier. Comparing your brand's presence against competitor citation rates reveals which specific authority signals drive the difference.

Brand authority in generative engines builds from five categories of external signals:

- **Third-party list appearances:** how often your brand appears in "best of" lists, industry rankings, and expert roundups across publications AI systems treat as authoritative
- **Earned media coverage:** mentions in trade press, major news outlets, and sector-specific publications with high domain authority
- **Review platform presence:** review volume, recency, and sentiment on G2, Capterra, and Trustpilot that AI systems actively draw from when forming brand assessments
- **Brand mentions:** Ahrefs' brand visibility analysis found that brand web mentions correlate with AI citation rates at 0.664, approximately three times stronger than the backlink correlation of 0.218
- **Structured data:** pages with complete JSON-LD schema markup are more extractable at the ingestion stage, improving the probability of appearing in AI generated answers for relevant prompts

Tracking brand visibility signals requires a combination of brand monitoring tools, manual prompt audits, and regular competitor analysis. Brand credibility in AI systems builds from the weight of consistent, accurate third-party evidence across multiple sources. A brand with strong credibility in traditional search but thin third-party coverage will see this gap reflected directly in lower AI citation rates.

## AI share of voice: the GEO metric most brands miss

Share of voice in AI responses is the single most strategically useful GEO metric most brands don't track. Citation rate tells you how often you appear. Share of voice tells you how often you appear relative to key competitors, which is what determines whether buyers include your brand in their shortlist when they query generative AI models for vendor recommendations.

Measuring AI share of voice requires running the same set of prompts across AI platforms weekly, recording every brand cited across all responses, and calculating your brand's citations as a percentage of the total. A share of voice figure below 20% in a category with three or four major competitors suggests significant gaps in the authority signals AI systems draw from. A share of voice figure growing week on week but not reflected in AI referral traffic points to a landing page or clickthrough issue rather than a citation problem.

Share of voice also reveals platform-specific gaps that aggregate citation rates hide. AI Mode and AI Overviews share only 13.7% URL overlap, which means strong performance on one platform tells you almost nothing about performance on another. A brand can have strong share of voice in Perplexity and near-zero presence in Google AI Overviews for identical query sets, requiring a different content and authority strategy to close.

## Query gap analysis: the GEO KPI that reveals content strategy

Query gap analysis identifies the specific prompts your target buyers use where competitors earn citations and your brand doesn't. It's the GEO equivalent of a keyword gap analysis, and it produces the most directly actionable output of any GEO measurement activity. Unlike SEO keyword gap analysis, query gap analysis operates at the question level rather than the term level, which reflects how users actually interact with large language models and generative AI models.

Running a query gap analysis requires a prompt set covering category queries, comparison queries, and problem-led queries at every buyer journey stage. Execute across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, and Gemini. Record which brands appear for each prompt on each platform. The gaps where competitors consistently appear and your brand doesn't map directly to content opportunities.

The geographic dimension matters here too. GEO performance varies significantly across markets because AI platforms personalise responses based on user location. Monitoring localised performance acts as an early warning system against regional risks: a brand with strong AI visibility in the UK but weak citation rates in the US may be losing consideration with North American buyers before any sales interaction occurs. Geospatial analysis of citation patterns reveals where to prioritise regional content and earned media investment.

## AI generated sentiment: the GEO metric traditional tools can't measure

AI generated sentiment is a GEO KPI with no equivalent in traditional SEO metrics. It measures how AI systems describe your brand, not just whether they mention it. A brand appearing frequently in AI responses but consistently described with negative sentiment or qualifying language is worse off than a brand that doesn't appear at all, because negative descriptions reach buyers at scale before any sales interaction.

Sentiment is measured across three dimensions:

- **Descriptive accuracy:** whether AI systems describe your product capabilities, pricing, and positioning correctly. Inaccurate descriptions from large language models actively damage brand credibility at scale
- **Competitive framing:** whether AI responses position your brand favourably relative to named competitors when buyers ask for vendor recommendations
- **Trust language:** whether AI generated descriptions include qualifying phrases such as "reportedly," "some users say," or "though reviews are mixed" that introduce doubt before a user visits your site

Correcting negative AI sentiment requires sustained publishing of accurate, detailed content across owned and earned channels. AI sentiment shifts gradually as the weight of evidence across multiple sources changes. Dataset completeness matters here: AI systems form assessments from the breadth of available evidence, so brands with incomplete or outdated information across web sources see this reflected in their AI sentiment scores.

## Connecting GEO KPIs to business goals

The metrics that earn credibility with leadership teams are the ones that connect to revenue, pipeline, and brand preference. GEO KPIs that live only in an AI visibility dashboard don't survive budget conversations. Connecting the right GEO KPIs to business outcomes is what turns a GEO campaign from a visibility exercise into a growth channel.

| GEO KPI | Business outcome it connects to | How to measure it |
|---|---|---|
| **AI-referred conversion rate** | Revenue: sessions from AI platforms converting to leads or sales | GA4 channel grouping for AI referral sources |
| **Branded search uplift** | Brand awareness: AI exposure building recognition surfacing as branded searches | Google Search Console branded query volume trends |
| **Pipeline influence** | Revenue attribution: deals where AI was a touchpoint in the buyer journey | CRM tagging of AI-referred sessions before conversion |
| **AI share of voice change** | Competitive positioning: GEO efforts building category dominance | Weekly prompt set tracking across all major AI platforms |
| **Direct traffic correlation** | Zero-click influence: AI citations producing navigation visits | Direct traffic trend comparison against citation rate changes |

Regional performance adds a further dimension to GEO metrics. Customer acquisition cost by location measures the marketing cost required to acquire a new customer in a specific region, and applying that framework to AI-referred sessions reveals which geographic markets deliver the highest GEO return on investment.

## Setting realistic targets and measuring success

GEO targets need to reflect current AI search infrastructure. Setting a citation rate target of 80% in the first quarter is unrealistic for a brand starting from zero. Setting a target of 20% prompt coverage across primary AI platforms within 90 days is a measurable, achievable baseline for most B2B software brands.

A practical GEO target framework looks like this:

- **30 days:** establish baseline citation rate, share of voice, and sentiment scores across the target prompt set on all major platforms. No optimisation targets yet because you can't set realistic targets without knowing where you start
- **60 days:** target 10 to 15 percentage point improvement in citation rate on the specific prompts identified as highest-priority gaps. Track branded search volume as a leading indicator of AI exposure
- **90 days:** target measurable AI-referred sessions in GA4 with conversion rate benchmarked against organic. If AI referral conversion rate is below organic, the issue is landing page alignment rather than citation rate
- **Six months:** target share of voice parity with the primary competitor outperforming you in AI responses. Achievable through consistent content and earned media activity focused on the specific query gaps the audit reveals

47% of B2B buyers already use AI for market research and vendor vetting, according to Forrester's 2024 research. Brands setting GEO targets now compound an advantage over brands that begin optimising when AI search is as saturated as traditional organic search already is.

## The GEO measurement cadence: metrics matter most when they're consistent

GEO performance changes faster than organic rankings. 30% of brands stay visible across back-to-back AI responses for the same prompt, and 40 to 60% of cited domains change monthly across major AI platforms. A measurement cadence that matches this rate of change is essential for tracking progress effectively.

A practical GEO measurement cadence for B2B software brands:

- **Weekly:** run the core prompt set across primary AI platforms. Log citation rates, share of voice, sentiment changes, and any shifts in brand description. A steady increase confirms GEO efforts are working. Flag drops immediately for investigation before they compound
- **Monthly:** review AI referral traffic in GA4. Compare session volume, conversion rates, and revenue per visit against organic search benchmarks. Cross-reference against GEO changes made in the period to build cause-and-effect understanding
- **Quarterly:** run a full competitive GEO audit. Map your citation footprint and share of voice against key competitors across all AI platforms. Identify authority gaps and query gaps, and update your GEO strategy accordingly

## Today's digital landscape: what the right GEO KPIs reveal

Traditional SEO measurement tells you how visible you are to users who query a traditional search engine and click a result. GEO measurement tells you how visible you are to users who ask generative AI models for recommendations, and how those models describe your brand in their AI driven answers.

An industry leader in traditional organic search can be entirely invisible in AI generated answers if their content doesn't match the passage-level extractability and topical depth that AI systems reward. Positional accuracy matters in this context: a brand appearing in AI answers but in the wrong context, associated with the wrong use cases, or described with inaccurate product details has a positional error that damages brand credibility even at high citation volumes. Structured data plays a direct role in correcting this, helping AI systems identify content types, entity relationships, and positioning accurately at the ingestion stage.

GEO measurement in today's digital landscape connects AI visibility to the business outcomes that digital marketing teams are accountable for. Data-driven insights from consistent prompt testing, citation source analysis, and AI referral traffic tracking together produce the picture that organic search dashboards will never surface on their own.
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# How to Measure the Performance of GEO-Optimised Pages

Source: https://firstmotion.com/insights/how-to-measure-the-performance-of-geo-optimised-pages

GEO performance can't be measured with traditional SEO tools. AI citations don't appear in Search Console, citation frequency isn't tracked by rank trackers, and a brand can earn hundreds of AI mentions without a single click. This guide covers the three-layer measurement framework: AI visibility, AI referral traffic, and brand authority signals, and the tools and cadence needed to connect GEO activity to commercial outcomes.

Measuring GEO performance requires a fundamentally different approach from traditional SEO metrics. AI generated responses don't appear in Google Search Console, citation frequency isn't tracked by rank trackers, and a brand can earn hundreds of AI mentions without generating a single click.

## Key takeaways

- GEO performance measurement covers three layers: AI visibility, AI referral traffic, and brand authority signals in generative engines
- Traditional SEO tools miss the majority of GEO performance because they weren't built to track AI generated answers
- Only 30% of brands remain visible across back-to-back AI responses for the same prompt, making continuous monitoring non-negotiable
- AI referral traffic converts 31% better than non-AI traffic, making it a high-value channel regardless of current volume

The brands that measure GEO performance well share one habit: they stopped treating AI citations as a byproduct of SEO and started tracking them as a primary channel metric. We've seen this shift produce clearer, faster decisions at FirstMotion client organisations than any other single change in how they report on search. [The AI search revolution](https://firstmotion.com/insights/from-keywords-to-conversations-the-ai-search-revolution-in-b2b-saas) created a measurement problem before it created a strategy problem, and this guide solves the measurement layer first.

## What is GEO and why does measurement matter?

Generative engine optimization, or GEO, is the practice of making your content citation-worthy inside AI generated answers across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, and Google Gemini. Gartner predicts a 25% search volume drop by 2026 as AI answer engines replace traditional search queries. McKinsey confirms more than 70% of organisations now regularly use generative AI in at least one business function.

6sense's 2025 buyer research found 94% of B2B buyers used generative AI tools during their most recent purchase process. Google's own data confirms AI Overviews now appear in roughly 50% of all searches globally. A brand appearing consistently in AI generated search results but not ranking in traditional Google search shows zero impressions in Search Console, producing a false picture of invisibility.

GEO investment without measurement is invisible by definition. Closing that gap makes the AI layer of discovery visible, actionable, and connected to the business goals that justify the investment.

## Why traditional SEO metrics don't capture GEO performance

Unlike traditional SEO, GEO operates on three different measurement units. Visibility is measured in mentions rather than rankings. Authority is measured in citation frequency across AI platforms rather than backlinks. Success includes zero-click interactions where a brand earns influence in an AI generated answer without producing a session in Google Analytics 4.

Rankings, impressions, and click-through rates all assume visibility produces traffic. GEO breaks that assumption: AI responses frequently produce zero clicks even when a brand appears prominently, and that visibility doesn't register in any standard analytics tool. Traditional keywords, impressions, and sessions all undercount GEO's commercial contribution in ways that compound over time.

Paid media campaigns running during periods of strong AI citation activity also tend to see higher branded click-through rates, suggesting brands that are AI cited convert better across every channel. Building a parallel GEO measurement framework isn't an alternative to traditional SEO reporting. It's an addition that reveals the data-driven insights organic dashboards will never surface on their own.

## The GEO measurement framework: three layers

GEO performance sits across three distinct layers. No single layer tells the full story, and all three need monitoring in parallel to build an accurate picture of GEO success.

| Layer | What it measures | Primary tools |
|-------|-----------------|---------------|
| **AI visibility** | Brand mentions, citation frequency, share of voice, sentiment | Profound, Peec AI, Otterly AI, SE Ranking AI Toolkit |
| **AI referral traffic** | Sessions, conversions, engagement from AI-referred visits | Google Analytics 4, UTM parameters, referral source segmentation |
| **Brand authority signals** | Third-party mentions, earned media, review platform presence | Brand monitoring tools, manual prompt audits, competitor analysis |

A brand can score well on AI visibility metrics while generating almost no AI referral traffic. A brand can drive meaningful referral sessions without appearing in any GEO tool's citation tracking because traffic arrives via direct navigation after an AI conversation. Tracking all three layers together is the only way to build an accurate picture of GEO success.

## Layer one: AI visibility and brand visibility metrics

AI visibility measures how often a brand appears in AI generated responses across AI platforms, in what position, and in what context. GEO performance varies significantly across multiple AI platforms and geographic markets, so tracking each separately is essential. The core AI visibility metrics to track are:

- **Citation frequency:** how often your brand appears in AI responses to prompts in your category, measured across a consistent prompt set on each platform. A steady increase in citation rates indicates effective content promotion and growing brand authority in generative search
- **AI share of voice:** the percentage of AI responses in your category mentioning your brand versus competitors, giving competitive positioning data that traditional SEO never surfaced
- **Brand position:** the position at which your brand appears in a response. First-position mentions carry significantly more weight with your target audience than trailing references
- **Sentiment score:** how AI powered search systems describe your brand and whether that description appears in positive context or with qualifying language that reduces buyer confidence
- **Accuracy:** whether AI generated descriptions of your product, pricing, and positioning are factually correct

Localised messaging and personalisation tailored to specific regions improves the probability of appearing in AI generated answers for that market. A brand with strong UK coverage but thin US media presence will see materially different AI visibility across those markets, which directly affects reach with the intended target audience.

## Layer two: tracking AI generated referral traffic

Adobe Digital Insights analysed over one trillion visits to US retail sites during the 2025 holiday season and found AI referrals converted 31% better than non-AI sources. Visitors from AI platforms spent 45% more time on site and viewed 13% more pages per visit. A steady increase in AI referral traffic in GA4 is one of the clearest indicators of effective GEO efforts and improving content authority in generative search.

AI referral traffic arrives in GA4 via three routes:

- **Direct referral links:** when an AI platform provides a clickable link and the user visits your own site, the session appears in GA4 with the AI platform as referrer. ChatGPT referrals show as chat.openai.com, Perplexity as perplexity.ai, and Gemini as gemini.google.com
- **UTM-tagged links:** adding UTM parameters to key pages isolates AI driven traffic even when referral source data is inconsistent across enterprise platforms. Tagging links with an AI search source and referral medium lets you segment AI sessions cleanly in GA4 regardless of how each platform passes referral data
- **Direct traffic uplift:** brands cited frequently in AI answers see corresponding increases in direct traffic as users navigate to the site after encountering the brand in an AI conversation. Monitoring direct traffic trends alongside referral data captures the full commercial impact of AI citations, including zero-click interactions that never produce a referral session

Regional ROI adds a further dimension to AI referral analysis. Comparing AI referral conversion rates by geography reveals which markets produce the highest return on geo investment. Effective geographic measurement also helps optimise sales territory coverage and staffing by revealing where AI driven demand is growing fastest.

## Layer three: brand authority signals in generative search

The third layer sits outside owned analytics entirely. It covers the signals AI systems use to form their understanding of a brand's authority, accuracy, and reputation. These signals don't produce direct traffic data but determine citation rates at every other layer. Comparing your AI visibility against competitors reveals which specific authority signals they've built that you haven't.

Brand authority in generative engines builds from five categories of external signals:

- **Third-party list appearances:** industry rankings, expert roundups, and "best of" compilations across publications AI systems treat as authoritative
- **Earned media coverage:** mentions in trade press, major news outlets, and sector-specific publications with high domain authority
- **Review platform presence:** review volume, recency, and sentiment on G2, Capterra, and Trustpilot that AI systems actively draw from when forming brand assessments
- **Community mentions:** brand references in Reddit threads and LinkedIn posts that AI systems index as social proof signals
- **Accuracy of brand information:** whether the information AI systems surface about your brand is current, correct, and consistent with your actual positioning

Geographic Information Systems and regional data sources also feed into the authority signals AI systems draw from for localised queries. Brands with strong regional press coverage, local review presence, and geographically relevant case studies consistently outperform generic competitors in AI answers for location-specific informational queries.

## AI generated sentiment: measuring how AI describes your brand

Sentiment analysis reveals whether AI systems describe a brand in positive context or with qualifying language that reduces buyer confidence. Understanding AI perception of your brand helps adjust content strategies before inaccurate or negative descriptions reach your target audience at scale.

Sentiment is scored across three dimensions in dedicated GEO tools:

- **Descriptive accuracy:** whether AI systems describe your product capabilities, use cases, and positioning correctly
- **Competitive framing:** whether AI responses position your brand favourably relative to named competitors when users ask for recommendations
- **Tone and trust signals:** whether AI generated descriptions include language such as "reportedly" or "some users say" that introduces doubt

Correcting negative AI sentiment requires sustained publishing of accurate, detailed content across owned and earned channels. AI sentiment shifts gradually as the weight of evidence across multiple sources changes, so paid media campaigns running alongside strong earned media coverage compound GEO authority signals more effectively than paid-only strategies.

## GEO measurement tools: visibility metrics in practice

| Tool | Best for | What it tracks |
|------|----------|-----------------|
| Profound | Enterprise brands | Citation frequency, sentiment, share of voice across 10+ AI engines |
| Peec AI | Agencies and multi-brand teams | Brand mentions, position, sentiment across ChatGPT, Perplexity, Gemini |
| Otterly AI | GEO audits | Citations, schema audits, crawlability issues, prompt-level visibility |
| Ahrefs Brand Radar | Teams already using Ahrefs | AI Mode and ChatGPT citation tracking alongside existing SEO data |
| SE Ranking AI Toolkit | SMBs and agencies | AI Overview citations, ChatGPT and Perplexity visibility in one view |

Traditional analytics tools including GA4 and Google Search Console remain essential for tracking the organic traffic and technical health that feeds GEO citation rates. The most effective measurement stacks combine one dedicated AI visibility platform with GA4 for referral traffic and a brand monitoring tool for earned media coverage.

## What GEO metrics matter for business goals

The metrics that matter most connect to commercial outcomes, not just visibility dashboards. The GEO metrics that earn credibility with leadership teams are the ones that connect to revenue, pipeline, and brand preference.

| Metric | What it measures | Why it matters |
|--------|-----------------|-----------------|
| **AI-referred conversion rate** | Sessions from AI platforms divided by conversions | Directly connects AI citations to revenue |
| **Branded search uplift** | Branded search query increases correlating with AI citation growth | Captures zero-click AI exposure as branded awareness |
| **Direct traffic trends** | Sustained direct traffic increases correlating with AI citation growth | Reveals commercial impact of zero-click AI interactions |
| **Pipeline influence** | CRM data showing converted prospects had prior AI-referred sessions | Maps AI citations to the B2B buyer journey |
| **AI share of voice change** | Week-on-week brand appearance change across category prompts | Leading indicator of GEO strategy effectiveness |

Regional ROI measures the cost required to acquire a customer versus revenue generated in a specific location. Applying that framework to AI-referred sessions reveals which geographic markets deliver the highest return on geo investment. Brands that track this dimension allocate paid media and earned media budgets with significantly more precision than brands reporting on AI visibility at aggregate level only.

## Today's digital landscape: what GEO measurement reveals

Traditional SEO measurement tells you how visible you are to users who search in a traditional search engine and click a result. GEO measurement tells you how visible you are to users who ask AI systems for recommendations, and how those systems describe your brand in response.

An industry leader in traditional search can be entirely invisible in AI generated answers if their content doesn't match the passage-level extractability and topical depth that AI systems reward. Natural language processing is the mechanism behind this shift: AI systems interpret queries, retrieve relevant passages, and generate answers grounded in the sources they find most credible. User behaviour in AI search is fundamentally different from keyword-driven search because users provide more context, ask follow-up questions, and engage in multi-turn conversations.

GEO measurement makes this new layer of discovery visible, actionable, and connected to business goals. Data-driven insights from consistent prompt testing, citation source analysis, and referral traffic tracking together produce the picture that organic dashboards will never surface on their own.

## Building a GEO measurement cadence for measuring success

30% of brands remain visible across back-to-back AI responses for the same prompt, and 40 to 60% of cited domains change monthly across major AI platforms. Continuous monitoring is the only reliable way to detect citation gains and losses before they translate into competitive position changes.

A practical GEO measurement cadence looks like this:

- **Weekly:** run the core prompt set across primary AI platforms. Log citation rates, share of voice, and sentiment changes. A steady increase in citation rates week on week confirms GEO efforts are working
- **Monthly:** review AI referral traffic in GA4. Compare session volume, engagement, and conversion rates against the prior month and prior year. Cross-reference against GEO changes made in the period to build cause-and-effect understanding
- **Quarterly:** run a full competitive GEO audit. Map your citation footprint against named competitors. Identify authority gaps and content gaps explaining share of voice differences, and update your GEO strategy accordingly

Investing in GEO measurement infrastructure now builds the data history that makes future optimisation decisions faster. Brands that start measuring AI visibility today will have twelve months of baseline data before most of their competitors begin tracking it.
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# How Google AI Mode and AI Overviews Select Sources

Source: https://firstmotion.com/insights/how-google-ai-mode-and-ai-overviews-select-sources

Google AI Mode and AI Overviews select sources using fundamentally different criteria from traditional organic rankings. Only 14% of AI Mode citations overlap with the Google top 10, 62% of AI Overview citations now come from outside the top 10, and the two surfaces share just 13.7% URL overlap. Understanding how each platform selects sources is the foundation of any AI visibility strategy in 2026.

Google's AI search experiences, AI Mode and AI Overviews, select sources using fundamentally different criteria from traditional organic rankings. Understanding each one is the foundation of any AI visibility strategy in 2026.

## Key takeaways

- Only 14% of URLs cited in AI Mode also rank in the top 10 of traditional Google search results
- AI Overviews now appear in approximately 48% of all tracked queries, up from 30% a year ago
- 62% of AI Overview citations come from pages outside the organic top 10 as of early 2026
- AI Mode uses a query fan-out technique that selects sources at a granular level traditional SEO never needed to address

Most of the B2B software brands we work with at FirstMotion assume their Google rankings carry over to AI Mode and AI Overviews. The data says otherwise. The gap between organic and AI is now large enough to demand a separate strategy, and this guide explains exactly what drives citation selection on each platform.

## What is Google AI Mode and how does it work in Google Search?

Google AI Mode is a dedicated tab within Google Search powered by Gemini 2.5. It generates synthesised, conversational responses to complex queries using more advanced reasoning than traditional search, interprets queries through text, images, or voice, and retrieves real-time information from the live web rather than a static index.

AI Mode reduces the need to reformulate searches and visit multiple websites, because it handles multi-part questions and performs multiple background searches simultaneously. Google confirmed in its August 2025 announcement that AI Mode now reaches 180 countries and territories in English, making it the most powerful AI search experience Google has ever deployed globally.

AI Mode also uses multimodal capabilities that go beyond text. Through Search Live, it lets users point their camera at real-world objects and ask questions about what they see, using computer vision to analyse environments in real time. Google's Agentic Vision within Gemini 3 Flash takes this further, using computer vision to improve image recognition accuracy, automating visual analysis tasks that previously required manual processes and delivering superior accuracy compared to manual inspection methods.

## What are Google AI Overviews and how do AI Overview citations work?

Google AI Overviews is a separate product from AI Mode. It appears directly on the main Google search results page as an AI generated summary above traditional organic listings, without any tab switch required. AI Overviews launched officially on May 14, 2024, focusing specifically on increasing visibility in AI generated search summaries for informational queries.

A critical distinction: AI Overviews cite passages, not entire pages. The citation unit is a specific extractable answer within a page, not the overall authority of the domain. BrightEdge's year-over-year analysis confirms AI Overviews now trigger on approximately 48% of tracked queries, up from 30% a year ago.

For local businesses, this prevalence matters significantly. Queries about local services, healthcare providers, and professional services increasingly surface AI Overviews rather than traditional organic listings. Brands that earn an AI Overview citation see a 35% increase in organic clicks compared to competitors that don't appear in the overview, according to Seer Interactive's analysis of queries across 42 organisations.

## How AI Mode works: query fan-out and AI generated responses

AI Mode's source selection starts before it retrieves a single page. Ahrefs' AI Mode guide confirms that AI Mode uses a query fan-out technique that takes the original query, divides it into multiple sub-queries, and sends each to Google's index independently. A single question in the AI Mode search bar can trigger dozens of parallel searches across different facets of the same topic.

This architecture produces a very different AI response from what traditional search generates. A page ranking position one for the primary query can lose citation slots to candidate pages that answer sub-queries well, even when those exact URLs don't rank for the original question. AI Mode queries tend to be significantly longer and more conversational than traditional search queries, which means AI Mode selects content at a much more granular and intent-specific level.

AI Mode also provides a more detailed analysis of complex topics than any AI generated answer or featured snippet. When users want to dive deeper, they ask follow-up questions within the same session, and AI Mode performs additional query fan-out rounds to retrieve more specific context. This extended session behaviour means multiple brands can earn citations across a single conversation, creating citation opportunities that don't exist in any other Google search format.

## How AI Mode selects sources: what the data shows

SE Ranking's August 2025 study analysed AI Mode responses across a large keyword set and produced three findings that fundamentally change how AI visibility needs to be measured.

| Signal | Figure | What it means for your strategy |
|--------|--------|--------------------------------|
| Average links per AI Mode answer | 12.6 | AI Mode cites significantly more sources than a featured snippet |
| URL overlap with organic top 10 | 14% | Ranking in Google doesn't reliably predict AI Mode citation |
| URL consistency across three repeated tests | 9.2% | AI Mode results are highly volatile; no single page gets cited reliably |

The 14% URL overlap is the most strategically significant figure. It confirms that AI Mode rarely references the pages Google ranks highest in traditional search results, and operates on a fundamentally different approach to content relevance. For brands tracking AI visibility through organic rankings alone, these figures confirm that organic search results are almost entirely missing what AI Mode actually does with their content. User feedback signals, including follow-up question patterns and session dwell time, also influence which specific pages get selected over time.

## Google's AI Overviews: AI Overview visibility data and citation patterns

AI Overviews and AI Mode share the same Google infrastructure but select sources differently. AI Overviews focus on informational queries, cite passages rather than entire pages, and correlate more strongly with organic rankings than AI Mode, though that correlation has weakened significantly in 2026.

Digital Applied's post-I/O 2026 analysis shows that in July 2025, 76% of AI Overview citations came from pages ranking in the organic top 10. By March 2026, that figure had fallen to 38%, a 50% relative decline in eight months. Ahrefs' March 2026 analysis confirms that 62% of AI Overview citations now come from pages outside the top 10 organic results, as top-10 citation rates fell from 76% to 38% in eight months.

AI Overviews also push traditional organic listings further down the page. The average overview now exceeds 1,200 pixels in height, displacing organic search results, blue links, and featured snippets significantly below the fold on AI Overview-triggered queries. Ahrefs' updated December 2025 study found that the presence of an AI Overview now correlates with a 58% lower average clickthrough rate for the top-ranking page, updated from their initial 34.5% finding in April 2025.

## Generative AI in Google Search: AI Mode vs AI Overviews vs traditional search

The clearest way to understand how generative AI has changed source selection is to compare all three surfaces directly. Each operates on different signals, rewards different content properties, and delivers a different user experience.

| Signal | Traditional organic search | Google AI Overviews | Google AI Mode |
|--------|---------------------------|-------------------|----------------|
| Where it appears | Main SERP | Above organic results on main SERP | Dedicated generative AI tab |
| Query type | All query types | Primarily informational | Complex, multi-part, exploratory |
| Source selection | Ranking algorithm | Passage-level citation, correlated with top 10 | Query fan-out, 14% overlap with top 10 |
| Citation unit | Full page ranking | Cited passages, not entire pages | 12.6 links per response on average |
| Personalisation | Limited | Limited | Deep, via Search, Maps, Google apps |
| Result volatility | Relatively stable | Moderate | Very high (9.2% URL consistency) |
| Follow-up questions | No | No | Yes, within the same session |

The most important distinction is the citation unit. AI Overviews cite passages; AI Mode selects at the sub-query level. Both systems evaluate specific content within a page, not the overall authority of the page itself. That's why candidate pages outside the top 10 regularly earn AI citations when they contain the most directly answerable passage for a specific sub-topic.

## How AI Overview visibility differs from AI Mode visibility

AI Mode visibility and AI Overview visibility are distinct metrics that require separate tracking strategies. Ahrefs' analysis of 540,000 query pairs found that AI Mode and AI Overviews cite the same URLs only 13.7% of the time. A brand can earn strong AI Overview citations without appearing in AI Mode responses at all, and vice versa.

AI Overview visibility aligns more closely with traditional organic rankings, topical authority, and content quality. Pages that rank well for informational queries, carry schema markup including Article schema and HowTo schema, and cover topics with genuine contextual understanding earn AI Overview citations at higher rates. AIO focuses specifically on synthesising helpful links and cited pages for the user's initial query, meaning content that directly and clearly answers common questions performs best.

AI Mode visibility requires a different approach because of the query fan-out architecture. AI Mode visibility depends on covering the full range of sub-topics a complex query generates, not just the primary keyword. A brand that answers one aspect of a query well but leaves adjacent sub-queries uncovered will see inconsistent AI Mode citation patterns, regardless of domain authority or traditional search results performance.

## What drives AI Overview citations and AI generated answers across both platforms

Both AI Mode and AI Overviews reward the same underlying content properties, though they weight them differently. These signals consistently improve citation likelihood across both platforms:

- **Direct answers first:** content that answers the specific query in the opening paragraph gets extracted more reliably. An AI generated answer draws from the most immediately relevant passage, not the most comprehensive page
- **Topical depth:** covering all the sub-topics a query fan-out generates means more sub-queries find a citable passage within the same domain, keeping multiple brands from occupying citation slots your content should fill
- **Schema markup:** Article schema, HowTo schema, and FAQ schema all improve passage-level extractability for specific pages. Google Search Central confirms JSON-LD is the recommended implementation
- **Content freshness:** AI systems favour recently updated content with current statistics and contemporary references on cited pages
- **Entity clarity:** naming the brand, topic, and use case explicitly in titles, headings, and opening paragraphs helps Google's AI systems anchor AI citations accurately
- **Technical SEO foundations:** pages that load quickly and render correctly for AI crawlers pass eligibility requirements before any relevance evaluation begins
- **Topical authority:** domains that cover a topic area comprehensively build the citation trust AI Mode's query fan-out needs to return to the same domain repeatedly across multiple searches

Content quality has become the dividing line between brands that appear consistently in AI generated answers and brands that don't. AI systems automate relevance evaluation at scale, delivering superior accuracy compared to any manual content audit process.

## How AI Mode personalisation affects source selection and where brand appears

AI Mode's personalisation layer adds a dimension to source selection with no direct equivalent in traditional SEO or AI Overview optimisation. When users opt in, AI Mode references past searches, location data, and activity from the Google app and Google Maps to generate an AI powered response tailored to their personal context.

The same query from two different users can produce entirely different cited sources and different AI response content. Content that speaks to specific use cases, buyer stages, and geographic contexts, including local businesses and region-specific solutions, earns more citations in personalised responses for those segments. A brand that only publishes generic category-level content won't appear in personalised AI Mode responses, even when it ranks well in traditional organic search results.

For B2B brands, topical depth across the full buyer journey is essential. AI Mode needs enough relevant content across an entire topic area to construct personalised responses. Brands that publish at multiple depth levels, from overview articles to detailed technical guides, give AI Mode more citation options across different user contexts.

## How to measure AI generated visibility and AI Mode citations

Measuring AI visibility requires different tools from traditional rank tracking. Organic rankings are a necessary but insufficient proxy for AI citation performance, and the gap between the two continues to widen across all search engines incorporating generative AI.

Platforms that now track AI visibility directly include:

- **SE Ranking AI Search Toolkit:** tracks AI Overview citations and AI Mode citations at keyword level, with volatility monitoring across multiple searches of the same query
- **Ahrefs Brand Radar:** indexes AI Mode responses and lets brands check citation frequency for exact URLs across a growing query dataset
- **BrightEdge Generative Parser:** monitors AI Overview presence and overview citations with year-over-year trends across industry verticals
- **Semrush AI Toolkit:** tracks AI Overview visibility alongside traditional organic results for comparison across SEO platforms

FirstMotion's AI search audit starts by mapping a brand's citation footprint across AI Mode and AI Overviews, comparing it against competitor citation rates, and identifying the specific content and technical gaps that explain the difference. Continuous monitoring of AI citation rates is the only reliable signal of AI search performance because organic visibility no longer predicts it.

## Featured snippets, blue links, and what AI search replaces

AI Mode and AI Overviews don't just complement traditional search. For informational queries, they're actively replacing featured snippets and blue links as the primary way users receive answers. Understanding this displacement helps brands prioritise where to focus their SEO strategy and content investment.

Featured snippets were the first step in Google's transition from returning links to returning answers directly. AI Overviews took that further by synthesising answers from multiple sources. AI Mode goes further still, replacing the entire traditional search results experience with a conversational AI response that handles the full research session without requiring multiple clicks to individual websites.

The brands earning consistent AI citations treat this as a content architecture problem, not a keyword problem. Topical depth, structured data coverage, and passage-level clarity determine AI citation outcomes. The AI search revolution in B2B SaaS has already made these signals the primary competitive differentiator in organic visibility for informational queries.

## If your brand isn't appearing in AI Mode or AI Overviews, here's where to start

Most of the brands we audit at FirstMotion aren't invisible in AI search because their content is low quality. They're invisible because their content strategy was built for a different citation system. A targeted audit of citation gaps, schema markup coverage, and topical depth usually reveals fixable issues within the first session.

If you want to understand exactly why your brand isn't being cited and what to prioritise first, talk to the FirstMotion team. We'll map your AI citation footprint and show you the fastest path to AI search visibility.
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# Best UK AI Search & GEO Agencies in 2026: A Founder's View

Source: https://firstmotion.com/insights/best-geo-agencies-uk

Our curated guide to UK GEO agencies: what each one does, who they suit, and how to tell genuine AI search capability from rebranded SEO services.

## Summary

The UK's generative engine optimisation scene has grown fast. There are now dedicated AI search specialists, established full-service shops with genuine GEO practices, and everything in between. Which GEO agency fits depends on your sector, your growth stage, and whether AI search visibility needs to stand alone or sit inside a wider programme.

Before FirstMotion, Tom Batting built and exited two platforms, Obby and Baluu, and earned a [Forbes 30 Under 30](https://www.forbes.com/pictures/5c60a96431358e2a162edba7/obby-tom-batting--max-kuf/). Those years in founder circles gave a close-up view of how badly search and AI discovery can be handled, even by companies with genuinely strong products.

When AI started reshaping how B2B buyers build shortlists, FirstMotion launched with [Alex Price](https://alexprice.co.uk/), an exited agency founder and investor. We kept seeing the same problem: strong B2B software brands being underserved by agencies that hadn't adapted. So we built ContextualJourney™, combining audience intelligence, buyer journey mapping, and prompt mining into a single platform.

What follows covers 10 agencies in detail, the criteria we used to evaluate them, and a stage-by-stage framework to help you match your brief to the right type of partner.

## Top GEO agencies in the UK: quick overview

| Agency | Best for | Notable for | Pricing |
|--------|----------|-------------|---------|
| FirstMotion | B2B SaaS and software, Series A-B | ContextualJourney™ platform, investor due diligence | On request |
| Rank4AI | AI-only visibility, no traditional SEO needed | Structured audit methodology, tests 6 AI platforms | From £800/mo |
| Found | Larger brands in a full performance programme | Luminr platform, Everysearch™ methodology | On request |
| Impression | B2B and SaaS, GEO integrated with digital PR | B Corp, Digital Agency of the Year | On request |
| Passion Digital | GEO alongside paid and content strategy | Google Premier Partner 2026, Pixis.ai backing | On request |

## What AI search optimisation means in 2026

The terminology is genuinely confusing. GEO, AEO, AI SEO, LLMO: agencies use these interchangeably, and some use all four simultaneously. Here's a quick breakdown:

### AI Search Terminology

| Term | What it means | Where it applies |
|------|---------------|-----------------|
| GEO (generative engine optimisation) | Getting your content cited inside AI-generated answers by large language models across ChatGPT, Perplexity, Google AI Overviews, and Google Gemini | Any brand that needs to appear when AI systems answer buyer queries |
| AEO (answer engine optimisation) | Optimising for direct-answer features: featured snippets, voice search, and zero-click boxes | Brands targeting featured snippet positions alongside AI visibility |
| AI SEO | A broad label covering anything from basic schema work to fully integrated GEO programmes | Ask any agency using this term exactly what they track and how |

Large language models select which sources to cite based on entity clarity, content structure, and third-party authority signals. Unlike ranking web pages in traditional search, generative AI platforms assess how well a source directly answers the query.

### What separates a real GEO programme from rebadged SEO

A genuine AI search programme measures citation as a primary metric, runs real prompts through ChatGPT, Perplexity, and Google Gemini, and connects results to pipeline outcomes. GEO strategy can't be measured by organic traffic or search performance in traditional search engines alone.

### The commercial case

- AI referral traffic to retail sites [grew 693% year on year](https://www.adobe.com/content/dam/dx/us/en/experience-cloud/digital-insights/pdfs/adobe_analytics_holiday_shopping_2025.pdf) during the 2025 holiday season (Adobe Digital Insights, January 2026)
- AI referral sessions [grew 700% across the full year 2025](https://thestacc.com/blog/ai-search-referral-traffic-stats/), the fastest-growing traffic source on the web (Previsible, 400+ tracked websites)
- AI referral traffic [grew 340% year on year](https://pressonify.ai/blog/conductor-aeo-geo-benchmark-2026-ai-referral-traffic/) as of January 2026, based on Conductor's analysis of 21.9 million queries

Unlike traditional SEO, GEO focuses on how pages are retrieved and synthesised by generative engines, not just indexed and ranked. Our [GEO vs SEO guide](https://firstmotion.com/insights/geo-vs-seo-whats-the-difference) covers the full distinction.

### How we selected the best generative engine optimisation agencies

No agency paid to appear. Every entry was assessed against three criteria. The right GEO agency depends on fit: your sector, your stage, and whether AI search visibility needs to stand alone or sit inside a broader programme.

#### Named methodology and prompt-level tracking

Structured data, entity optimisation, and content architecture for AI extraction are the baseline. Prompt-level tracking and citation reporting across ChatGPT, Perplexity, and AI Mode are the differentiators. Agencies without a named methodology are rebranding existing SEO services.

#### Citation outcomes, not traffic

Can they show citation results for clients, not just traffic improvements? Digital PR and GEO need to work as one: agencies that treat them as separate service lines consistently deliver weaker results in both.

#### B2B sector understanding

Consideration-stage queries like "best [category] software for [use case]" are where AI search is reshaping B2B pipeline. Agencies without B2B experience miss the nuances of multi-stakeholder buying cycles.

## The 10 best UK agencies for AI search and GEO in 2026

### 1. FirstMotion

**Best for:** B2B SaaS and software companies at Series A-B stage with long sales cycles, complex buying committees, and pipeline goals.

FirstMotion's [ContextualJourney™ platform](https://firstmotion.com/) was built around a gap most software companies don't know they have: their buyers are building shortlists through ChatGPT and Perplexity before ever visiting a website, and those shortlists often don't include them.

GEO for B2B software is not a category where a standard agency model holds up. Buying cycles are long, buying committees are senior, and the way a CISO or Head of RevOps uses AI tools to evaluate vendors is specific to the category, the moment, and the competitive set. The same senior people who set the strategy are in each FirstMotion engagement week to week, which means understanding of the client's buyers, category, and competitive position builds continuously rather than being interpreted by layers of the account team.

ContextualJourney™ is how FirstMotion structures that work. The team maps where clients appear across AI search platforms, using prompt data, ICPs and sales transcripts to build a precise picture of how buyers research and shortlist. Engagements are built around that: entity and schema audits, AI search monitoring, structured content development, and digital PR for citation authority, sequenced around the actual buying cycle. Reporting ties to pipeline from day one, with one question driving everything: is AI visibility generating opportunities?

In one B2B SaaS engagement, FirstMotion delivered a 200% improvement in AI visibility and shifted 40% of inbound enquiries to organic and AI search combined.

FirstMotion also runs [digital due diligence for investors and PE firms](https://firstmotion.com/pricing/investors), assessing how visible portfolio targets are across generative platforms before acquisition or growth investment. No other agency on this list offers that.

FirstMotion works with a focused number of clients at any one time. It's worth confirming availability before investing time in the process.

### 2. Rank4AI

**Best for:** Businesses that want AI search visibility as a standalone programme, separate from traditional SEO or paid media.

One thing and one thing only is what [Rank4AI](https://www.rank4ai.co.uk/what-we-do/) does: dedicated AI search visibility. No traditional SEO retainer, no paid media, nothing else. Every engagement starts with an audit across six AI platforms, using a 17-section assessment that covers entity signals, content architecture, ecosystem presence, and cross-platform consistency. The methodology draws on data from over 1,400 UK business audits, which gives it a practical evidence base rather than theoretical frameworks.

Three service paths are available: Ecosystem (building AI presence outside your website, from £800/month), Full Agency (includes direct site work, from £1,500/month), and Advisory for teams that want to future proof their AI search strategy without full outsourcing. Founded by Adam Parker, the approach is systematic and the pricing is unusually transparent for a specialist generative engine optimisation agency.

Rank4AI's exclusive AI search focus is its clearest strength and its natural constraint. If your brief includes integrated SEO, content production, or digital PR, you'll need additional partners.

### 3. Found

**Best for:** Larger brands that need AI search visibility tracked and reported as part of a broader performance marketing programme.

Everysearch™ is [Found](https://www.found.co.uk/services/seo/ai-seo/)'s trademarked framework for tracking brand visibility across generative AI platforms, social search, and traditional search engines in one place. The engine behind it is Luminr, their proprietary AI-powered platform, which maps how a brand appears wherever buyers are searching. As a full-service digital marketing agency, Found's SEO, digital PR, data, and paid media teams operate as a connected system rather than separate service lines, which is where they perform best: when AI visibility needs to sit inside a broader performance marketing agency brief. Clients include Puma, Toolstation, Fender, and House of Marley.

GEO work covers entity optimisation, schema and structured data implementation, metadata strategy, and content built for AI extraction. The infrastructure Found has built is genuinely substantial, and it's better suited to brands with the scale and budget to use it fully.

Found's model is built for scale. Brands with more focused briefs or tighter budgets will get more specialist attention from smaller partners.

### 4. Impression

**Best for:** B2B and SaaS brands that want GEO integrated with digital PR, technical SEO, and genuine senior engagement across the team.

B Corp certified and independently owned since its founding in 2012 by Aaron Dicks and Tom Craig, [Impression](https://www.impressiondigital.com/generative-engine-optimisation-agency/) operates across Nottingham and London with dedicated sector teams for B2B, SaaS, and fintech. That vertical depth shapes how GEO gets done: knowing how buyers in those sectors research and shortlist is what determines which prompts to target and which content formats earn AI citations. Their 2024 Digital Agency of the Year win at the Global Agency Awards and a 4.5-day working week both point to an agency that's thought carefully about how it operates.

GEO services are built around earning citations through authority: digital PR and brand mention outreach sit alongside entity optimisation, schema implementation, and authoritative content structured for AI extraction. The combination of strong technical SEO and earned media capability gives them a genuinely joined-up approach to the two things AI systems assess: content quality and source credibility.

Impression is multi-channel by design. If you need a GEO-only brief or a boutique engagement model, this isn't the natural fit.

### 5. Passion Digital

**Best for:** Brands wanting GEO alongside paid media, content, and cross-channel performance, particularly B2B and professional services.

Four consecutive years as a Google Premier Partner (2023 to 2026) puts [Passion Digital](https://passion.digital/services/ai-search/) in the top 3% of Google's agency partners globally. The 2025 acquisition by [Pixis.ai](http://pixis.ai/), a US AI technology firm, accelerated their AI capability: they now operate as part of Stellar, an AI-native global agency network, with access to AI forecasting tools and real-time optimisation infrastructure most independent agencies can't replicate. Named clients include Nutanix, OneTrust, Octopus Investments, Knight Frank, and Moore Kingston Smith.

The GEO offering covers entity optimisation, AI Overview optimisation, LLM performance tracking via their proprietary Deep Research methodology, semantic enhancement, and cross-platform AI search monitoring. Separating those workstreams rather than bundling them makes reporting more honest and makes it easier to see what's moving across AI search platforms and traditional search.

Passion Digital's broad service range works well for brands that want everything handled in one place. For focused GEO specialist work, you may find more depth elsewhere.

### 6. Blue Array

**Best for:** Established brands and scale-ups that want the depth of a specialist organic search consultancy with a growing GEO capability built on top.

Simon Schnieders built [Blue Array](https://www.bluearray.co.uk/services/generative-engine-optimisation/) in 2015 after leading SEO at Zoopla, MailOnline, and Yell. What he created is deliberately different from a standard SEO agency: the Consulgency® model (trademarked) blends senior consultancy strategy with agency-scale execution. Clients include RAC, Simply Business, Funding Circle, and GoCardless. Schnieders runs the LondonSEO Meetup and authored the In-House SEO book series, which Amazon lists as a bestseller. The agency is B Corp certified, has strong technical SEO expertise, and operates from Reading and London.

Generative engine optimisation services cover AI sentiment analysis, citation gap analysis, and structured reporting across major AI models. Their technical expertise in organic search strategy underpins the GEO delivery. The Ignite package for startups gives Blue Array a broader entry point than most at this level.

Blue Array's model is strongest for brands that want senior strategic direction alongside delivery. It's less suited to a narrow AI-search-only brief.

### 7. Tilio

**Best for:** Brands that already have SEO covered and need specialist AI search measurement, tracking, and practical optimisation as a distinct programme.

A UK AI search agency based in Exeter, [Tilio](https://www.tilio.co.uk/ai-search-agency) starts where most GEO agencies finish: measurement. Work begins by building a prompt set around your services, buyers, competitors, and decision-stage searches, then tracking how your brand appears across the major AI search platforms. Profound is the primary AI visibility data source, with Peec AI, Ahrefs, and Semrush feeding into a client dashboard that shows citation signals, competitor movement, and content recommendations in one place. Pricing is published from £499/month.

The focus is understanding whether your brand is being mentioned, cited, accurately described, and fairly compared in AI-generated responses, then improving the specific signals most likely to influence each of those factors. It's a future-proof approach for brands that want AI search visibility to compound over time.

Tilio isn't a full-service agency. Content production, link building, and technical SEO at scale are outside what they're built for.

### 8. Varn

**Best for:** In-house SEO teams and technically minded marketers with complex websites who need GEO built on solid information architecture.

Where most GEO agencies lead with content strategy, [Varn](https://varn.co.uk/services/generative-engine-optimisation-geo-agency/) starts with structure. A Bristol-based Google Premier Partner, the approach to generative engine optimisation (GEO) treats it as an architectural problem first: auditing how AI systems interpret a site, then rebuilding the foundations so AI crawlers can accurately parse and cite the brand. That sequencing, structural work before content, is what separates GEO that compounds from GEO that stalls.

Services cover entity modelling, schema markup, content structuring for AI clarity, digital PR for citation authority, and AI visibility tracking across AI-powered search engines and generative search environments. Varn publishes a free guide to AI visibility that reflects a transparent, education-led approach to the discipline.

Varn's strength is technical depth. Brands that also need high-volume content production alongside structural work may need a broader partner.

### 9. Buried Agency

**Best for:** Scale-ups and growth-stage brands wanting an ROI-led approach that treats GEO and traditional organic search as a single integrated programme.

Among the first UK agencies to position explicitly around generative engine optimisation as a core organic search strategy rather than an add-on, [Buried](https://www.buriedagency.com/) is a Bristol-based agency covering GEO, SEO, digital PR, and link building under one roof. The founding conviction is that AI search visibility and traditional organic performance aren't separate problems: brands need visibility across both traditional search and AI driven search engines to future-proof their discovery. GEO services focus on entity clarity, structured data, and content architecture for AI extraction, while digital PR and link building build the third-party citation footprint that AI systems use to assess credibility.

Small by design, which means direct access to senior practitioners rather than account management layers. A free GEO audit is available before committing to a retainer.

Being a smaller agency is a genuine advantage for some clients and a real constraint for others. Capacity during busy periods is worth discussing early.

### 10. ClickSlice

**Best for:** Ecommerce and retail brands wanting a well-established London agency that has built GEO, AEO, and LLM optimisation into its core search offering.

Joshua George's [ClickSlice](https://www.clickslice.co.uk/generative-engine-optimisation-geo-services/) is a London-based SEO agency with unusually public credentials: a UK government commission to deliver SEO training to digital teams, a Udemy SEO course with over 100,000 students, and coverage in Forbes and Entrepreneur. Search marketing services are published from £2,500/month, making ClickSlice one of the top GEO agencies at this profile level to be transparent about pricing. GEO, AEO, and LLM optimisation are offered alongside traditional SEO, combining structured data implementation, AI-aligned content workflows, and entity optimisation.

ClickSlice appears consistently in ChatGPT and Perplexity responses when buyers search for GEO agencies in the UK, which is a proof point worth noting: they've applied the discipline to themselves. Their generative engine optimisation (GEO) and AEO capability is built on top of a heritage of strong technical SEO. Their strongest documented results are in ecommerce SEO.

B2B SaaS buyers with long sales cycles and complex buying committees should ask specifically for sector-relevant case studies before committing.

## Four questions to ask any GEO agency before signing

More than simply process questions, these separate agencies that genuinely work in AI search from those that have added "GEO" to a service list.

### 1. Can you show us a brand appearing in ChatGPT or Perplexity for a query they don't rank for on Google?

This is the most direct test of genuine GEO capability. Organic rankings and AI citations use different signals. An agency with real GEO expertise should be able to show a client appearing in AI-generated answers for a prompt where their Google rankings wouldn't explain the citation. If they can't, the programme is likely traditional SEO with updated language.

### 2. How do you measure share of voice in AI answers, and which tools do you use?

The honest answer involves named tools. [Peec.ai](http://peec.ai/) and Profound are the primary platforms in 2026 for tracking how often a brand appears in AI-generated responses across a defined prompt set. Vague references to "monitoring AI search" without specifying how are a red flag. AI search visibility is now a distinct reporting category from Google Search Console data and needs to be treated as such.

### 3. What's your approach to building citation authority through third-party sources?

Authority signals significantly impact AI citation selection. Brands appearing consistently in authoritative third-party publications, directories, and review platforms earn far more AI citations than brands optimising only their own content. Ask whether digital PR and citation building is part of the programme or sold separately, and ask to see examples of the third-party placements they've secured for clients.

### 4. Have you worked with companies in our specific vertical, and what did success look like?

GEO for a B2B SaaS company with a nine-month sales cycle is different from GEO for an ecommerce brand. The prompts buyers use, the buying committee structure, and the AI platforms they rely on all vary. Generic case studies showing traffic improvements without connecting to pipeline or revenue aren't sufficient evidence for a business-critical investment.

## What separates GEO-native agencies from SEO shops with a new name?

There are now dozens of UK agencies offering AI search optimisation services. Most are applying traditional SEO thinking to a different surface, rebranding existing SEO services as GEO, and calling it generative engine optimisation. Three tests separate the genuine ones.

### 1. They report on AI citations as a primary metric

Not as a derivative of organic rankings. A genuinely GEO-native agency can tell you a brand's share of citations in ChatGPT for a specific prompt cluster, how that share has changed over 90 days, and which structural changes drove the movement. A digital marketing agency that's rebranded its existing SEO services can't.

### 2. They understand digital PR differently

In traditional SEO, digital PR builds backlinks that influence ranking web pages in Google. In generative search, it builds brand mentions in authoritative content that AI systems retrieve from and are trained on. The mechanism is different. GEO agencies that haven't made that distinction in their thinking haven't made it in their delivery either.

### 3. They can produce an AI visibility report

Not a screenshot of a ChatGPT response. A structured document showing which prompts were tested, which AI search platforms were checked, where the brand appeared and where it didn't, and what changed between reporting periods. That's the clearest evidence a GEO agency is running a genuine AI search programme across both AI-powered platforms and traditional search.

## How to match your growth stage to the right agency

Company stage is the most reliable guide to which type of GEO agency will deliver best. Generative engine optimisation services vary significantly by scope, from foundational audit work through to full programmes covering content strategy, digital PR, and technical infrastructure.

| Growth stage | Primary need | Right agency type |
|---|---|---|
| Pre-Series A / seed | Entity building, foundational AI visibility | Specialist or advisory model |
| Series A | Consideration-stage citability, B2B buyer journey mapping | GEO-native with B2B depth |
| Series B | Share of voice across the funnel, integrated SEO and GEO | GEO with digital PR and technical capability |
| Scale-up and enterprise | Multi-platform visibility, performance integration | Full-service agency with a dedicated GEO practice |

Our lane is Series A to B, B2B SaaS and software, UK and European markets. If your brief falls here and pipeline depends on AI-mediated research, that's the context the ContextualJourney™ platform was built for.

For benchmarks on what good AI search visibility looks like at each stage, our [AI search benchmarks for B2B SaaS](https://firstmotion.com/insights/ai-search-benchmarks-for-b2b-saas-what-good-actually-looks-like-in-2026) sets out what to measure and what to aim for.

## Ready to build your AI search strategy?

If your B2B software brand isn't showing up when buyers run shortlisting prompts in ChatGPT or Perplexity, you're losing pipeline at the earliest stage of the AI-driven search research cycle, before a competitor's website has even been visited.

Every FirstMotion engagement starts with a ContextualJourney™ audit: mapping the prompts your buyers actually use across AI search engines and AI-driven search, identifying where you appear and where you don't, and building a prioritised organic search strategy to close the gap. Measurable from day one and tied to pipeline from the outset.
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---

# How AI Search Engines Rank and Retrieve Websites

Source: https://firstmotion.com/insights/how-ai-search-engines-rank-and-retrieve-websites

The AI retrieval ranking pipeline explained: learn how keyword search, vector search, hybrid retrieval and reranking determine which websites AI search engines surface.

## Key takeaways

- 96.55% of web pages receive zero organic traffic, making retrieval eligibility the first barrier to address
- Hybrid retrieval combining keyword precision and vector recall consistently outperforms either method alone
- Rerankers assign relevance scores after initial retrieval to surface the most relevant passages for answer generation
- RAG architectures transform queries before retrieval to improve match quality across all pipeline stages

We've run retrieval audits on B2B software brands that rank on page one of Google but don't appear in a single AI-generated answer. The content is strong. The problem is structural: their pages fail retrieval eligibility before any relevance scoring even starts. We built our GEO practice around fixing exactly that, and this guide covers every stage of the pipeline we work through.

## What is an AI retrieval ranking pipeline?

An AI retrieval ranking pipeline is a multi-stage process designed to find relevant information from a large corpus of documents and surface the best answers to a user query. According to IBM Research, retrieval augmented generation (RAG) combines a retrieval phase, where relevant documents are identified from an external knowledge base, with a generation phase, where a large language model synthesises an answer from the retrieved context.

The pipeline exists because large language models have a finite context window. They can't process every document on the internet before answering a question, so retrieval systems do the heavy lifting first, narrowing billions of potential sources down to the handful of relevant chunks that fit inside the LLM's context window and carry enough relevant context for grounded answer generation.

Ahrefs' study of 14 billion pages found that 96.55% of all indexed pages receive zero organic traffic from Google. The same dynamic applies to AI retrieval: the vast majority of published content never enters a retrieval pipeline's candidate set because it fails basic eligibility requirements before any relevance scoring begins.

## The stages of an AI retrieval ranking pipeline

According to NVIDIA's RAG documentation, a retrieval augmented generation pipeline operates across two main phases: an offline ingestion phase where documents are processed and indexed, and an online query processing phase where retrieval and generation happen in response to a user query.

Each stage acts as a filter. Content that fails eligibility at stage one never reaches the reranker. Content that passes every stage but lacks clear entity anchoring may still be deprioritised at the answer generation stage.

| Stage | What happens | Key signals evaluated |
|-------|--------------|----------------------|
| Data ingestion | Source documents are broken into chunks and converted into vector embeddings | Chunk size, metadata, document structure |
| Query understanding | The user query is analysed, transformed, and encoded into a query vector | User intent, entity recognition, query rewriting |
| Initial retrieval | Keyword search and vector search run in parallel across the index | BM25 scores, semantic similarity, vector distance |
| Hybrid fusion | Results from keyword and vector searches are merged via Reciprocal Rank Fusion | Rank positions from both retrieval methods |
| Reranking | A cross-encoder scores each retrieved chunk against the query | Contextual relevance, groundedness, answer quality |
| Answer generation | The top-ranked chunks are passed to the language model as retrieved context | Context window fit, source attribution |

## How large language models and AI systems use the retrieval ranking pipeline

As IBM Research explains, RAG combines LLM generation with external knowledge retrieval to ground model responses in verifiable, up-to-date information rather than static training data. This architecture powers AI search engines, enterprise chatbots, and tools like Perplexity and ChatGPT's web search mode. Knowledge graphs also play a role in enterprise retrieval systems, providing structured entity relationships that help AI systems interpret query intent and connect relevant context across multiple documents.

AI systems across sectors including healthcare and finance use retrieval pipelines for improved decision-making, because retrieval grounds model outputs in external knowledge rather than probabilistic prediction. A senior data scientist building a RAG system for root cause analysis in a financial services environment relies on the retrieval step to pull retrieved evidence from multiple documents simultaneously, delivering relevant context that no single document contains on its own.

### Stage one: data ingestion and the embedding model

Retrieval begins offline, before any user query is processed. Source documents are broken into smaller, manageable chunks, each encoded into a high-dimensional vector representation by an embedding model. Weaviate's hybrid search guide explains that these vector embeddings capture the semantic meaning of content by converting text into mathematical representations that position similar concepts near each other in vector space.

Chunk quality at ingestion directly determines retrieval accuracy downstream. Chunks that are too large dilute the semantic signal; chunks that are too small lose the context needed for grounded answer generation. The embedding model translates both the content and the user query into the same vector space, which is what enables semantic similarity search to match relevant documents even when exact keywords don't appear in both.

For content publishers, the ingestion stage has a direct implication: structured content with clear headings, explicit entity naming, and logical paragraph boundaries produces cleaner chunks. Unstructured content, JavaScript-rendered pages, and pages with poor TTFB that AI crawlers abandon before ingestion never reach the vector database and fail the retrieval process entirely.

### Stage two: query understanding and query transformation

Query understanding is the stage where AI systems interpret user intent, not just the words a user typed. ZipTie.dev's pipeline breakdown confirms that query transformation enhances retrieval quality by modifying the original query before it enters the initial search, producing multiple queries that broaden the retrieval net and improve the probability of matching relevant documents.

Common query transformation techniques include:

- **Query rewriting:** rephrasing the original query to match vocabulary used in source documents
- **Query fan-out:** generating multiple queries from the same user query to capture different phrasings of the same intent
- **Query decomposition:** breaking complex queries into sub-queries, each sent to the retrieval system independently
- **HyDE:** generating a hypothetical answer and using its embedding for retrieval rather than the original query vector

The same document can fail retrieval for one query formulation and succeed for another. Content that explicitly addresses the entities and terminology users actually use in their prompts scores better across all query transformation variants, which is why entity clarity is a stronger retrieval signal than keyword density.

### Stage three: keyword search and information retrieval

Keyword search, also called lexical retrieval or sparse retrieval, is a core component of information retrieval systems. It matches query terms against an inverted index of document terms to produce an initial set of search results. BM25's probabilistic scoring model, which emerged from information retrieval research in the 1970s and 1980s, scores documents based on term frequency, inverse document frequency, and document length normalisation to rank how relevant each document is to the exact keywords in the query.

BM25 excels at exact-match retrieval: product codes, named entities, rare technical terms, and specific jargon that must appear verbatim to be relevant. Its core limitation is vocabulary mismatch: a document about "machine learning model training" won't match a query for "how to build an AI" even if both cover the same concept. Semantic search addresses this gap directly by operating on meaning rather than exact keywords.

Google's 400 billion page index is narrowed to a small candidate set per query before any ranking begins. Traditional search and AI retrieval both use this two-stage architecture: broad candidate retrieval first, precise relevance ranking second.

### Stage four: vector search and semantic search

Vector search, also called dense retrieval or semantic search, converts both the user query and source documents into numerical vector embeddings and retrieves documents based on semantic similarity rather than exact keyword match. Pinecone's search guide confirms that vector retrieval finds relevant results even when queries and documents share no exact terms, capturing the semantic meaning behind user intent.

The semantic similarity calculation measures the cosine distance between the query vector and each document vector in the database. Documents positioned close to the query in vector space are retrieved as semantically relevant even when they share no exact keywords with the original query. This is what allows AI search engines to correctly retrieve a document about "cloud infrastructure optimisation" in response to a query about "reducing server costs."

For content publishers, writing about a topic using natural language that covers the concept thoroughly produces better vector embeddings than content that optimises solely for keyword density. Deep learning models produce these embeddings, and the same model encodes both documents at ingestion and the user query at retrieval time, ensuring the semantic space is consistent across both.

### Stage five: hybrid search, hybrid retrieval and Reciprocal Rank Fusion

Hybrid search combines keyword precision with vector recall by running both BM25 and vector search in parallel and merging search results into a single ranked list. Weaviate's RRF knowledge card explains that Reciprocal Rank Fusion calculates a combined score for each document by summing the reciprocal of its rank position across both result lists, without requiring incompatible raw scores to be directly compared.

RRF works because it operates on rank positions rather than raw scores, solving the problem of combining BM25's term frequency outputs with vector search's cosine similarity outputs. Digital Applied's 2026 benchmark data confirmed that basic RRF (NDCG 0.7068) outperforms both BM25 alone (0.6983) and pure vector search alone (0.6953) on the WANDS e-commerce benchmark, with well-tuned hybrid variants reaching 0.7497.

Hybrid retrieval enhances retrieval quality in enterprise environments because real-world queries mix both retrieval needs. Access control requirements in enterprise systems add another layer: the retrieval pipeline must filter results based on user permissions before surfacing retrieved evidence to the user interface, ensuring relevant context reaches only those with the correct authorisation.

### Stage six: reranking, answer generation and the context window

Initial retrieval optimises for recall: retrieving a broad set of potentially relevant documents. Reranking optimises for precision: ordering those documents by exact relevance to the specific query before passing the most relevant chunks to the language model. ZipTie.dev's pipeline breakdown confirms that rerankers assign relevance scores after initial retrieval to prioritise the best content, directly determining which passages make it into the LLM's context window.

Cross-encoder rerankers evaluate the query and each retrieved document together as a pair, producing a precise relevance score. This is more computationally expensive than the bi-encoder approach used in initial retrieval, which is why reranking operates on a shortlist of 50 to 100 candidates rather than the full index. The trade-off is significantly higher answer quality: rerankers surface relevant passages that first-stage retrieval ranked too low to reach the context window.

Answer generation is the final retrieval step. The top-ranked chunks are assembled as retrieved context and passed to the language model, which synthesises a response grounded in that evidence. User interactions with the generated answer, including follow-up queries, dwell time, and feedback signals, feed back into iterative improvements to the pipeline's ranking systems over time.

## How to optimise content for AI retrieval ranking pipelines

Understanding the pipeline is the first step. The second is building a content operation that passes every stage. Most content optimisation advice targets the answer generation stage when the more critical barriers are earlier in the pipeline.

| Optimisation area | Pipeline stage affected | Primary action |
|-------------------|------------------------|-----------------|
| Technical accessibility | Retrieval eligibility | TTFB under 800ms per Google's TTFB guidance, LCP under 2.5 seconds |
| Structured data | Ingestion quality | JSON-LD schema markup improves chunk boundary recognition and entity identification |
| Entity clarity | Query transformation match | Name entities explicitly in titles, headings, and opening paragraphs |
| Content structure | Chunk quality | Clear H2 and H3 headings, short focused paragraphs, one concept per section |
| Keyword coverage | BM25 retrieval | Include the exact terminology users query, not just synonyms |
| Semantic depth | Vector retrieval | Cover the topic thoroughly using natural language across multiple related concepts |
| Direct answers | Reranking score | Answer the query in the first paragraph and include verifiable claims throughout |
| Content freshness | Training data inclusion | Update date_modified fields and refresh statistics regularly |

According to Google's structured data guide, implementing JSON-LD is the recommended approach for helping AI systems understand content types, entity relationships, and document metadata across all retrieval contexts.

## Traditional search vs AI ranking systems

Traditional search and AI retrieval share architectural roots but diverge significantly in what they prioritise. Understanding the differences helps brands allocate optimisation effort across both surfaces rather than assuming one strategy covers both.

| Signal | Traditional search | AI retrieval |
|--------|-------------------|----------------|
| Primary ranking driver | Link-based authority | Semantic relevance and information gain |
| Vocabulary matching | Keyword density | Semantic meaning via vector embeddings |
| Document evaluation | Full page evaluation | Chunk-level relevance scoring |
| Authority signals | Domain authority and backlinks | Citation frequency across training data |
| Freshness | Crawl recency | date_modified structured data signals |
| Result format | Ranked list of links | Synthesised answer with inline citations |
| Indexing requirement | Googlebot | PerplexityBot, GPTBot, and platform-specific crawlers |

As FirstMotion's GEO analysis explains, GEO requires a fundamentally different discipline from traditional SEO, demanding structured content, entity clarity, and LLM-ready formatting rather than ranking signals and backlinks.

## How to evaluate retrieval pipeline performance with a golden dataset

A golden dataset is a curated set of queries with known correct answers, used to benchmark retrieval accuracy across all pipeline stages. TruLens's RAG triad framework defines three primary evaluation metrics: context relevance, which measures whether retrieved chunks match the query; groundedness, which measures whether the generated answer is supported by the retrieved context; and answer relevance, which measures whether the answer addresses what the user actually asked.

For content publishers without access to pipeline internals, a practical evaluation approach is proxy testing:

1. Query AI search engines with the exact questions your target buyers ask
2. Observe which sources get cited and at which position
3. Audit those sources against the optimisation criteria in each pipeline stage
4. Track user interactions and web analytics for AI-referred traffic patterns
5. Iterate based on citation rate changes after each content update

User interactions and behaviour patterns in web analytics also reveal which content is generating AI-referred traffic and which isn't reaching the candidate set at all.

## Making AI retrieval visibility work for your brand

Getting consistently cited in AI-generated answers means building content that passes every stage of the retrieval pipeline, not just producing high-quality writing. The technical accessibility requirements, entity clarity demands, and direct-answer structure that AI retrieval rewards are different from what traditional SEO rewards, and the gap between the two explains why strong Google rankings don't automatically transfer to AI search visibility.

The brands that earn consistent AI citations combine three disciplines: technical infrastructure that makes content accessible to AI crawlers, content architecture that produces clean, well-bounded chunks at ingestion, and writing that delivers direct, verifiable answers at the reranking stage.

The AI search revolution in B2B SaaS doesn't reward one optimised page. It rewards a content operation that treats retrieval pipeline eligibility as a standard requirement across every page it publishes.

## If your content isn't reaching the AI retrieval candidate set, here's where to start

Most of the B2B software brands we audit at FirstMotion aren't failing AI retrieval because their content is poor quality. They're failing because their content was built for a different retrieval architecture. Fixing the structural issues, not rewriting the content, is usually where the fastest gains come from.

If you want to know exactly where your pages are failing the retrieval pipeline and what to fix first, talk to the FirstMotion team. We'll map your content against every pipeline stage and show you where the gaps are.
</content>

---

# How ChatGPT Decides Which Brands to Recommend

Source: https://firstmotion.com/insights/how-chatgpt-decides-which-brands-to-recommend

ChatGPT recommends brands based on three primary factors: entity recognition from training data, authoritative list mentions, and third-party credibility signals including media coverage and customer reviews.

## Key takeaways

- Authoritative list mentions account for 41% of ChatGPT brand recommendation signals
- 71% of ChatGPT citations reference content published in the last two to three years
- ChatGPT surfaces only 3 to 4 brands per response, creating winner-take-all dynamics
- Traditional SEO signals like backlinks have near-zero direct influence on AI training data recommendations

Most of the brands we audit at FirstMotion have strong Google rankings and clean backlink profiles. Neither of those things transfers to ChatGPT. The brands getting recommended are building a completely different kind of visibility, and this guide breaks down exactly how it works.

## What is ChatGPT and how does it work in AI search?

[ChatGPT](https://openai.com/index/introducing-chatgpt-search/) is a large language model developed by OpenAI that provides quick answers to questions, generates images, writes code, and searches the internet in real time. Free and paid tiers give hundreds of millions of users access to it daily, and it's become the tool most diligent buyers turn to when they want a direct answer rather than a list of links to evaluate.

[According to Attest's 2025 Consumer Adoption of AI Report](https://www.askattest.com/blog/articles/2025-consumer-adoption-of-ai-report), based on a survey of 5,000 consumers, nearly 41% of consumers trust generative AI search results more than paid search results. That's the core reason brand visibility inside ChatGPT answers matters: the model is doing something closer to endorsement than matchmaking.

[As Ahrefs confirmed in their analysis](https://ahrefs.com/blog/chatgpt-has-12-percent-of-googles-search-volume/), ChatGPT processed 2.5 billion prompts per day as of July 2025, representing 18% of Google's daily search volume. By September 2025, OpenAI CEO Sam Altman confirmed the platform had surpassed 800 million weekly active users, roughly 10% of the world's adult population.

## How ChatGPT builds its brand knowledge

ChatGPT doesn't consult a single ranked list of brands. [According to Foglift's analysis](https://foglift.io/blog/chatgpt-brand-recommendations), its knowledge is assembled from three distinct layers, each with different update cycles and different implications for how you build visibility:

- **Training data:** the massive corpus of web pages, articles, forums, documentation, and reviews that ChatGPT was trained on. Brands mentioned frequently, positively, and in authoritative contexts across the internet have a structural advantage that compounds over time
- **Real-time web browsing:** when web search is enabled, [ChatGPT uses Bing's index](https://www.thekeyword.co/news/openai-s-chatgpt-search-relies-on-bing-s-index) to retrieve live results, meaning Bing indexing is a technical prerequisite for appearing in real-time ChatGPT answers regardless of where you rank pages on Google
- **Search grounding:** ChatGPT verifies and augments responses with live search results, drawing on authority signals that overlap with traditional SEO but weight them differently

Understanding which layer drives a given recommendation tells you where to focus your effort. Both reward the same underlying asset: a strong trust footprint across the web.

## The three categories of trust signals ChatGPT evaluates

Writing in [Entrepreneur](https://www.entrepreneur.com/growing-a-business/how-chatgpt-really-decides-which-brands-to-recommend/503887), Scott Baradell, author of Trust Signals: Brand Building in a Post-Truth World, describes the parallel between how careful buyers evaluate brands and how AI models replicate human behavior at scale. The most diligent buyers look for media coverage, check review sites, and notice how a website presents itself. Each signal answers the same question: can I trust this brand?

Most of the advice floating around on how to get recommended by ChatGPT focuses on technical tactics: content structure, FAQ formatting, freshness signals. That framing addresses the wrong place in the priority order. The signals that move the needle most aren't on your website.

| Category | What it includes | Why it matters to ChatGPT |
|----------|------------------|--------------------------|
| Website trust signals | Design quality, testimonials, customer logos, messaging clarity | Signals credibility to crawlers and to the humans ChatGPT learned from |
| Inbound trust signals | Media coverage, review sites, analyst mentions, PR, third-party citations | The most heavily weighted category; reflects external validation |
| SEO trust signals | Google rankings, structured data, technical health | Influences what gets crawled and included in training data |

[According to Onely's analysis](https://www.onely.com/blog/how-chatgpt-decides-which-brands-to-recommend/) of ChatGPT recommendation patterns, authoritative list mentions account for 41% of influence factors, awards and accreditations 18%, and online reviews 16%.

## Why authoritative list mentions are the single most important signal

Most brands optimising for AI visibility focus on their own content: structured FAQs, schema markup, published case studies. Those things matter, but they don't drive ChatGPT brand recommendations. The single biggest lever is appearing in third-party lists and rankings that exist on other sites, not your own.

[Onely's brand recommendation analysis](https://www.onely.com/blog/how-chatgpt-decides-which-brands-to-recommend/) confirms that authoritative list mentions drive 41% of ChatGPT recommendation signals. Industry rankings, expert roundups, and "best of" compilations tell ChatGPT that independent, credible sources have already evaluated your category and chosen to include your brand.

The practical implication: getting listed in industry publications, comparison platforms like G2 and Capterra, analyst reports, and "best of" roundups earns more AI recommendations than any amount of on-site optimisation. Media coverage significantly impacts AI recommendation outcomes because it generates the inbound trust signals that AI systems evaluate when deciding which brands to name.

## How training data shapes ChatGPT brand recommendations

[Foglift's analysis](https://foglift.io/blog/chatgpt-brand-recommendations) found that 71% of ChatGPT citations reference content from 2023 to 2025. Content freshness directly influences which training data patterns are most active in ChatGPT's recommendation behaviour, and it's a signal you can act on immediately by updating existing pages rather than creating new ones.

AI models favour authoritative, frequently-cited sources because those are the sources that generated the most agreement across the internet during training. Brands with strong historical digital presence, frequent mentions in credible publications, and consistent external validation gain AI visibility that newer brands are still competing to close.

The same dynamic applies to how ChatGPT answers questions about service quality and brand reputation. AI systems evaluate brands based on external validation signals, which means reviews, testimonials, and third-party coverage all flow constantly into the training data that shapes future recommendations.

## How real-time web search changes ChatGPT brand recommendations

When ChatGPT's web search is active, it queries Bing's index in real time before generating a response. This introduces a parallel pathway to brand recommendation that operates on a much shorter update cycle than training data, and it means existing Google rankings don't automatically carry over.

[Ahrefs' analysis](https://ahrefs.com/blog/chatgpt-has-12-percent-of-googles-search-volume/) found that ChatGPT results overlap only 12% with the Google SERP, confirming that Google-first SEO strategies systematically miss the signals that drive ChatGPT web search visibility. Pages with recent publication dates, updated statistics, and current-year references signal freshness to ChatGPT's search grounding process.

To signal freshness effectively, pages need to:

- Carry visible datePublished and dateModified structured data fields
- Reference current-year statistics and examples throughout the body
- Include a visible last updated date that users and crawlers can both read
- Update core claims whenever the underlying data changes, not just once a year

## How ChatGPT is already being used across industries

Buyers in every sector are asking ChatGPT the same questions they used to google, and getting direct brand recommendations back. The picture across industries is consistent: ChatGPT has moved from a writing tool to a primary discovery channel for both consumers and enterprise buyers.

| Industry | How ChatGPT is being used | Source |
|----------|--------------------------|--------|
| Enterprise sales | [Salesforce launched Agentforce in ChatGPT](https://www.salesforce.com/news/press-releases/2025/10/14/openai-partnership-expansion-announcement/), letting teams query sales records, review customer conversations, and build Tableau visualisations directly in ChatGPT | Salesforce / OpenAI press release, October 2025 |
| Customer service | [Klarna's OpenAI-powered assistant](https://openai.com/index/klarna/) handled two-thirds of all customer service chats in its first month of operation, conducting 2.3 million conversations | OpenAI Klarna case study, February 2024 |
| Healthcare | [OpenAI launched ChatGPT Health](https://www.healthcaredive.com/news/openai-launches-chatgpt-health/809094/) in January 2026, connecting medical records and wellness apps for 24/7 personalised health information, with over 230 million users submitting health questions weekly | Healthcare Dive, January 2026 |
| E-commerce | [OpenAI's ChatGPT Shopping Research](https://almcorp.com/blog/chatgpt-shopping-research-the-complete-guide-to-ai-powered-product-discovery-and-llm-optimization-for-e-commerce-2025/) delivers personalised product recommendations with images, pricing, and reviews, engaging users through a conversational discovery process | ALM Corp, December 2025 |
| Financial services | AI-powered assistants deployed for personalised customer support and automated sales processes have cut resolution times dramatically. [Klarna reduced average resolution time from 11 minutes to under 2 minutes](https://openai.com/index/klarna/) using its OpenAI-powered assistant | OpenAI Klarna case study, February 2024 |
| Energy sector | Energy companies use ChatGPT for virtual energy audits, equipment maintenance analysis, and expert customer advice, reducing reliance on specialist staffing | FasterCapital industry analysis |

[Zalando reported a 23% increase in product clicks and a 41% rise in wishlist additions](https://openai.com/index/zalando/) after deploying GPT-4o mini for its AI shopping assistant, a concrete example of what AI-driven product navigation delivers at scale. AI-referred visitors [convert at 4.4x the rate of standard organic traffic](https://foglift.io/blog/how-chatgpt-ranks-websites), meaning the quality of AI-referred visitors compounds the value of appearing in ChatGPT answers.

## The content strategy that gets brands cited by ChatGPT

Understanding the recommendation algorithm is the first step. The second is building the content operation that earns consistent citations. ChatGPT favours content that directly answers the exact questions buyers ask, across multiple sources, at a level of specificity that demonstrates genuine expertise.

[According to Foglift's seven-factor analysis](https://foglift.io/blog/chatgpt-brand-recommendations), the content signals that consistently influence ChatGPT brand recommendations include:

- **Exact question matching:** content built around the precise queries buyers type, not keyword variations. ChatGPT recommends brands that answer the question being asked, not the question you wish they were asking
- **Multi-source presence:** your brand answering the same question across your own site, review platforms, industry publications, and third-party guides signals consensus to AI models
- **Freshness signals:** updated publication dates, current-year statistics, and contemporary references that tell ChatGPT the content reflects current reality
- **Entity clarity:** your brand name, category, and use case stated unambiguously in titles, headings, and opening paragraphs so AI models can anchor the recommendation accurately
- **Authoritative citations:** content referencing primary sources, original data, and verifiable claims rather than recycled summaries of existing ones

Personalised learning also shapes which brands get recommended to specific users. A user who mentions running a 10-person remote team will receive different recommendations than an enterprise buyer. Content needs to speak to specific use cases and buyer contexts to show up as a recommendation for the right audience.

## How to build AI visibility across different platforms

ChatGPT isn't the only platform where brand recommendations matter. The same trust footprint that drives ChatGPT visibility also influences Google AI Overviews, Perplexity, and Gemini, though each platform weights signals differently. Gemini focuses more heavily on Google's own index and training data; Perplexity focuses almost entirely on real-time web retrieval; ChatGPT operates across both.

| Platform | Primary citation source | Freshness weight | Training data reliance |
|----------|------------------------|------------------|------------------------|
| ChatGPT | Training data and Bing index | High | Very high |
| Perplexity | Real-time web retrieval | Very high | Low |
| Google AI Overviews | Google index and training data | Moderate | Moderate |
| Gemini | Google index and training data | Moderate | High |

[According to HubSpot's analysis of ChatGPT product recommendations](https://blog.hubspot.com/marketing/chatgpt-product-recommendations), authority signals in AI work similarly to traditional SEO but extend to third-party platforms including established review sites, industry publications, analyst reports, and LinkedIn. Building visibility across that ecosystem is what creates the multi-source presence ChatGPT treats as consensus.

## What most brands get wrong about ChatGPT visibility

Most brands approach ChatGPT visibility the same way they approached Google SEO: by optimising their own website. That strategy addresses the wrong place in the signal hierarchy, and it misunderstands why AI-generated content about your brand matters far less than what independent sources say about you on other sites.

The most common mistakes we see:

- Investing in backlink campaigns that have near-zero influence on AI recommendations
- Publishing content only on their own site rather than earning coverage on third-party platforms
- Ignoring Bing indexing because Google rankings look healthy
- Treating review management as a customer service function rather than an AI visibility signal
- Writing content for keyword variations rather than the exact questions buyers ask ChatGPT
- Responding to AI visibility gaps by creating more AI-generated content rather than earning more external mentions

[13% of consumers already interpret the absence of a brand from AI results as a sign it's less established or less trustworthy](https://www.retaildive.com/press-release/20251029-sogolytics-2025-consumer-trust-study-finds-ai-rapidly-redefining-brand-rep/), according to Sogolytics' 2025 research of 1,198 US adults. The reputational cost of AI invisibility is no longer theoretical.

## Making ChatGPT brand visibility work for your business

Getting recommended by ChatGPT consistently means shifting your content strategy from publishing to earning. The signal hierarchy is clear: external validation beats internal content, third-party consensus beats self-promotion, and freshness beats authority in real-time search.

The brands that earn consistent ChatGPT recommendations share three traits: they're present on the platforms where buyers research, they're cited by the sources ChatGPT treats as authoritative, and they keep their content and external presence current enough to stay relevant inside ChatGPT's training data update cycle.

[AI visibility in B2B software](https://firstmotion.com/insights/geo-vs-seo-whats-the-difference) doesn't compound from one optimised page. It compounds from a brand that has built enough external consensus that any AI system querying the internet for your category arrives at the same answer.

## If ChatGPT isn't recommending your brand, here's where to start

Most of the B2B software brands we audit at FirstMotion aren't invisible to ChatGPT because their product is weak. They're invisible because their trust footprint is thin outside their own website. A few targeted changes to where and how your brand appears externally can shift that faster than any amount of on-site optimisation.

If you want to know exactly where your brand stands in ChatGPT's recommendation system and what to prioritise first, talk to the FirstMotion team. We'll show you exactly where the gaps are.
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# How Perplexity Decides Which Sources to Cite: Perplexity Citation Mechanics Explained

Source: https://firstmotion.com/insights/how-perplexity-decides-which-sources-to-cite-perplexity-citation-mechanics-explained

Perplexity selects sources through a three-layer reranking system that weighs content freshness, semantic relevance, entity clarity, and domain authority signals pulled from real-time web searches across multiple sources.

## Key takeaways

- Pages answering the query directly in the first paragraph get cited at higher rates
- Content updated within 30 days consistently beats older pages in citation selection
- Domain authority covers roughly 15% of Perplexity's ranking, drawn from three major indexes
- Schema markup makes pages structurally extractable and over-represented in Perplexity citations

We've watched B2B software brands with half the domain authority of their competitors consistently outrank them in Perplexity answers. The difference was never the content quality. It was always the structure. This guide breaks down exactly what they did differently.

We'll walk through every layer of Perplexity's citation mechanics, from real-time retrieval to structured data, so you can make your content the one Perplexity cites.

## What is Perplexity AI and how does it work in AI search?

The term perplexity carries two distinct meanings worth separating before going further. In its technical sense, perplexity refers to a statistical metric that measures a language model's prediction accuracy. Lower perplexity indicates text that's more predictable and characteristic of AI output, while human-written texts tend to produce higher scores; this property makes perplexity scores a tool for gauging authorship and detecting AI-generated manuscripts.

In the context of this guide, perplexity refers to the popular AI-powered search platform used for citation analysis and research. [Perplexity AI](https://www.perplexity.ai/help-center/en/articles/10352895-how-does-perplexity-work) is a retrieval augmented generation engine that dispatches real-time web searches and synthesises answers from multiple sources, attaching numbered inline citations to extracted sentences from the pages it retrieves.

As [IBM Research explains](https://research.ibm.com/blog/retrieval-augmented-generation-RAG), RAG gives models access to information beyond their training data by retrieving verifiable external facts before generating a response. That distinction is what makes citation selection an active, engineerable process rather than a training data lottery.

## How Perplexity retrieves and ranks sources in real time

According to Perplexity's official help documentation, every query triggers a fresh web retrieval with no static cached answer store. As documented in the AI crawlers field guide by Presence AI, PerplexityBot and other AI crawlers impose 1 to 5 second timeouts, meaning pages that render slowly get skipped before any content quality signal is evaluated.

Once pages are retrieved, Perplexity runs them through its three-layer reranking system, scoring each source across freshness, semantic relevance, and authority. The highest-scoring sources become the citations attached to the final generated answer.

The full six-stage pipeline, documented by ZipTie.dev in April 2026, details how domain authority, freshness signals, and structured data function as core inputs across each sequential retrieval and ranking stage.

## The three-layer reranking system explained

Perplexity's citation selection isn't a single score. It's a layered evaluation where each signal builds on the last. AuthorityTech's 2026 analysis of 602 controlled prompts documents each stage in detail.

| Layer | Signal | What it measures |
|-------|--------|------------------|
| Layer 1 | Relevance scoring | Initial semantic match against query intent |
| Layer 2 | Quality and freshness | Recency, content depth, and authority evaluation |
| Layer 3 | XGBoost quality gate | Entity clarity and authoritativeness threshold |

Each layer acts as a filter. A page can carry strong domain authority but still get deprioritised if the content is stale or doesn't match the query. All three layers need to hold up for a source to earn a citation, and citation density across your site compounds over time as Perplexity builds confidence in your domain.

## Why content freshness and freshness signals dominate citation selection

According to AuthorityTech's freshness research, roughly half of all AI-cited content is less than 13 weeks old, and content under 30 days old earns an estimated 3.2x more AI citations than older pages. Content freshness carries more weight in Perplexity's citation process than domain authority, which is a meaningful shift from traditional SEO.

Perplexity favours content updated within the last 30 days for fast-moving queries. For evolving topics, content older than 90 days enters a decay window where it starts losing retrieval priority to newer pages covering the same queries. Freshness signals include a recent `date_modified` field in your structured data, contemporary references in the body text, and an updated publication date on the page.

As NAV43's controlled test demonstrated, the same content updated with 2026 data was cited more frequently than the identical 2024 version, with the same domain authority and content depth. Regularly updating existing content consistently outperforms publishing new content infrequently.

## How to write a direct answer that passes semantic relevance

Perplexity doesn't retrieve pages that simply contain your keywords. It evaluates content relevance by assessing how precisely your content matches the specific intent behind each query, and whether it delivers a direct answer quickly enough to be worth extracting. According to ZipTie.dev's pipeline analysis, 90% of top-cited sources answered the core query within the first 100 words.

For a page to pass semantic relevance and reach citation selection, it needs to:

- Place the direct answer in the first paragraph, not after several sentences of preamble
- Use clear entity anchoring so Perplexity can identify exactly what the content covers
- Contain concise, quotable statements Perplexity can extract as 2 to 3 sentence snippets
- Structure content with clear headings so the extraction process can segment it accurately
- Demonstrate semantic quality throughout, not just in the introduction

Entity clarity is a particularly underrated strong signal. Pages with clear entity naming and unambiguous topic focus get cited more frequently than pages that cover multiple subjects loosely. Think of it as giving Perplexity a clean anchor point for extraction from your website.

## How domain authority and AI systems determine source credibility

Domain authority accounts for approximately 15% of Perplexity's ranking system. That's not negligible, but it's smaller than most SEOs assume and it shouldn't be your primary GEO lever.

Perplexity pulls authority signals from three sources: Google, Bing, and Brave Search. Pages with established credibility, strong backlink profiles, and consistent citation from authoritative sources all score higher on this layer. Original research, transparent methodology, and references from industry analysts reinforce authority signals further.

Domain authority functions more as a tiebreaker than a primary driver. As Onely's citation analysis confirms, 24% of Perplexity citations come from pages outside Google's top 10 organic positions, showing that structural extractability can compensate for lower authority across many query types.

## Entity clarity and original research: the signals most brands ignore

Most brands optimising for Perplexity overlook the two signals that carry disproportionate weight for emerging publishers: entity clarity and original research. Entity clarity means your page unambiguously declares what it's about, with the entity named explicitly in the title, the first paragraph, and at least one heading.

According to AuthorityTech's source selection research, the L3 XGBoost quality gate specifically evaluates whether a page clearly identifies the entity it covers. Pages that bury the subject under brand language or span multiple topics fail this gate entirely.

Original research is a compounding advantage. According to AuthorityTech's citation signals guide, content containing original data Perplexity can't find elsewhere gets cited at higher rates because it becomes the primary source. Case studies, proprietary surveys, and first-party data all strengthen citation quality and increase the probability that Perplexity returns to your domain repeatedly.

## How structured data and schema markup improve citation rates

According to Onely's research, schema-enabled pages achieve 47% top-3 citation rates compared to 28% for pages without schema, a 19 percentage point advantage. Perplexity uses structured data to identify content types, understand content relationships, and determine whether a page is structurally extractable.

Here's what structured data implementation looks like in practice for citation optimisation:

- **Organisation schema** establishes entity clarity at the brand level and connects your content to a verifiable source
- **Article schema** with `datePublished` and `dateModified` fields sends direct freshness signals; JSON-LD is the recommended format for structured data at scale
- **FAQ schema** makes question-and-answer content immediately parseable for direct answer extraction
- **HowTo schema** structures step-by-step content so Perplexity can extract individual steps as citable claims

It's worth noting that structured data primarily benefits Google AI Overviews most directly. For Perplexity, the benefit is largely indirect: clean schema improves crawlability and entity clarity, which feeds the signals Perplexity does actively score.

## Perplexity AI as a research tool: what publishers and users need to know

Beyond citation mechanics, Perplexity AI allows document analysis by uploading PDFs and asking questions directly, making it genuinely useful for synthesising complex research. The critical caveat: AI-generated citations must always be checked for accuracy against original sources.

In academic writing, the standard guidance is clear: don't cite Perplexity AI directly. The platform acts as a research assistant rather than a primary source, and citation standards require tracing claims back to their origin.

This matters for publishers too. The more your content reads like a primary, verifiable source with transparent methodology, the stronger a signal it sends to Perplexity's citation selection process, and the more consistently it returns to your domain.

## How Perplexity compares to other AI search citation systems

Perplexity's citation mechanics differ meaningfully from other AI search tools, and understanding those differences helps you prioritise which GEO tactics matter most on each platform.

| Platform | Citation approach | Freshness weight | Authority weight |
|----------|-------------------|------------------|------------------|
| Perplexity AI | Real-time retrieval and reranking | Very high | Moderate (15%) |
| Google AI Overviews | Blended training and live retrieval | Moderate | High |
| ChatGPT search | Live web search with source cards | Moderate | Moderate |
| Bing Copilot | Bing index with inline citations | Moderate | High |

Unlike ChatGPT, Perplexity's freshness bias actively deprioritises stale content in a way that authority signals can't compensate for. A high-authority page with content older than 90 days will consistently lose to a lower-authority page that's been recently updated and structured to directly answer the query.

## What publishers get wrong about brand visibility in AI search

Most publishers optimising for AI search focus almost entirely on traditional SEO signals: domain authority, keyword density, backlinks. Those signals matter, but they're not what drives Perplexity citation rates or long-term brand visibility in AI-generated answers.

The most common mistakes we see:

- Publishing new content without updating existing high-authority pages
- Writing for keyword inclusion rather than direct answer structure
- Ignoring structured data because it doesn't visibly affect page design
- Assuming high domain authority compensates for outdated content
- Writing introductions that delay the direct answer past the first paragraph

According to ZipTie.dev's citation research, cited content contains 32% more explicit concepts than uncited content, meaning conceptual completeness and entity relationship density matter far more than keyword frequency. Publishers who treat semantic quality as a page-level discipline consistently earn higher citation rates.

## Making Perplexity citation work for your brand

Getting cited by Perplexity consistently means treating [AI search visibility](https://firstmotion.com/insights/geo-vs-seo-whats-the-difference) as its own discipline, not an extension of traditional SEO. The signals are different, the freshness requirements are more demanding, and the structural requirements reward a different kind of writing.

The brands that earn the most Perplexity citations share three traits: they publish original research regularly, they maintain content freshness across their key pages, and they build structured data into every content template from the start.

Brand visibility in AI search doesn't come from one optimised article. It comes from a content operation that treats citation density, freshness signals, and entity clarity as standard practice across every page it publishes.

## If your content isn't being cited, here's where to start

Most of the B2B software brands we audit at FirstMotion aren't missing citations because their content is weak. They're missing citations because their best content is structured for human readers rather than machine extraction. A few targeted changes, consistently applied, tend to move the needle faster than anyone expects.

If you want a clear picture of where your pages are falling short and what to prioritise first, talk to the FirstMotion team. We'll show you exactly where the gaps are.
</content>

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# How Agentic AI Is Changing the B2B Buying Unit

Source: https://firstmotion.com/insights/how-agentic-ai-is-changing-the-b2b-buying-unit

**Summary**

Agentic AI is now an active member of the B2B buying committee, handling vendor discovery, RFP generation, and order submission with minimal human oversight. Gartner forecasts AI agents will intermediate over $15 trillion in B2B spend by 2028. This guide covers how agents are reshaping procurement and what B2B software brands need to do to stay visible and shortlisted.

Agentic AI is changing how B2B purchasing decisions get made, with autonomous agents handling vendor discovery, RFP generation, and order submission with minimal human oversight. The traditional buying unit hasn't disappeared, but AI has become one of its most active members.

## Key Takeaways

- Gartner forecasts AI agents will intermediate over $15 trillion in B2B spending by 2028
- 94% of B2B buyers now use AI in their purchase process, with generative AI their top research source
- 67% prefer a rep-free experience yet 69% still turn to reps to validate AI insights
- Brands that aren't machine-readable get filtered out before any human reviews them

*We started FirstMotion because we saw something most agencies were missing: AI tools weren't just changing how people search, they were changing who does the buying. We work exclusively with B2B software companies, and what we keep seeing is that the brands getting shortlisted are the ones that understood this early. If you're still building go-to-market for a human-only buying process, this article is for you.*

This article covers what agentic AI does inside a B2B buying unit, why it changes the rules of vendor discovery and procurement, and what software companies need to do to stay visible and shortlisted in an agent-led world.

## What Is Agentic AI in B2B Buying?

[Agentic commerce](https://www.bigcommerce.com/blog/b2b-agentic-commerce/) refers to autonomous AI agents acting on behalf of buyers and sellers to streamline complex purchasing decisions, improving both operational efficiency and customer experience. Unlike traditional chatbots that follow predefined scripts, agentic systems are context-aware, goal-driven, and capable of making decisions independently.

This transforms artificial intelligence from reactive to proactive in the buying process, shifting procurement from static workflows to smart orchestration. The shift to agentic commerce in B2B is driven by the need for more efficient procurement, where AI agents enforce contract compliance and match products to precise specifications automatically.

[Gartner's October 2025 strategic predictions](https://www.gartner.com/en/newsroom/press-releases/2025-10-21-gartner-unveils-top-predictions-for-it-organizations-and-users-in-2026-and-beyond) forecast that 90% of B2B buying will be AI-agent intermediated by 2028, pushing over $15 trillion through agent exchanges. Most companies haven't yet built the data quality, structured product data, or composable architecture needed to prepare today.

## How AI Agents Are Entering the Buying Unit

| Role in buying unit | What the AI agent does |
|---|---|
| Procurement manager | Scans workflows, drafts RFPs, monitors supplier risk |
| Technical evaluator | Runs simulated tests, generates unbiased feature matrices |
| Finance lead | Validates contract pricing, defines spend thresholds, flags anomalies |
| End user | Submits natural language queries, receives tailored recommendations |
| Compliance | Embeds ESG criteria, preferred supplier lists, and regulatory guidelines |

AI agents don't replace the B2B buying committee; they join it and lead its early-stage work across multiple stakeholders and internal teams. [Forrester's State of Business Buying 2026](https://www.businesswire.com/news/home/20260121478240/en/Forrester%E2%80%99s-2026-Buyer-Insights-GenAI-Is-Upending-B2B-Buying-As-Leaders-Face-Mounting-Pressure-To-Justify-Every-Dollar-Spent) found the typical buying decision now includes 13 internal stakeholders and 9 external influencers, with procurement professionals as decision-makers in 53% of buying cycles.

Software bots run simulated tests, analyse complex pricing tiers, and generate unbiased feature matrices without human bias. AI agents scan internal company workflows, identify operational gaps, and automatically draft technical RFPs before a procurement manager has been briefed.

## Agentic Commerce and the New Buyer Journey

Buyer behavior has shifted decisively. [Forrester's Buyers' Journey Survey 2025](https://www.forrester.com/blogs/b2b_buyers_make_zero_click_buying_number_one/) found that 94% of B2B buyers now use AI in their purchase process. The share naming generative AI as their most meaningful research source doubled year-on-year, surpassing vendor websites, product experts, and sales teams.

[67% of B2B buyers](https://www.gartner.com/en/newsroom/press-releases/2026-03-09-gartner-sales-survey-finds-67-percent-of-b2b-buyers-prefer-a-rep-free-experience) now prefer a rep-free buying experience, up from 61% the prior year, and 70% prefer a completely digital self-service process. According to [6sense's 2025 Buyer Experience Report](https://6sense.com/science-of-b2b/buyer-experience-report-2025/), buyers are now 61% of the way through their purchase journey before they contact a seller.

By that point, shortlists are formed and requirements defined, inside AI conversations the vendor never sees. Understanding [why AI traffic converts](https://firstmotion.com/insights/is-ai-traffic-higher-quality-more-likely-to-convert) at multiples of traditional organic makes the commercial stakes clear: this is a revenue shift, not just a discovery shift.

## How Autonomous Agents Are Reshaping Procurement

Autonomous agents evaluate thousands of global vendors simultaneously, bypassing traditional search engines to find exact technical matches against predefined criteria. They synthesise historical purchasing data, market trends, and vendor risk profiles to recommend optimal purchasing routes.

By automating routine and time-consuming administrative tasks, procurement teams execute purchases significantly faster and focus on high-level strategic sourcing. Ensure the AI has access to live market indices, inventory levels, logistics timelines, and dynamic vendor pricing feeds to make accurate, real-time decisions.

Here's what an agentic procurement workflow looks like end to end:

- Agents scan internal company workflows, identify operational gaps, and automatically draft technical RFPs
- Agents evaluate thousands of global vendors simultaneously, bypassing traditional search engines to find exact technical matches
- Verify the agent reads and writes data seamlessly across your ERP, CRM, and Supply Chain Management software before deploying in live workflows
- AI eliminates manual data entry errors and [negotiates better bulk rates](https://www.jadasquad.com/blog/ai-agents-in-procurement) by analysing datasets no human team could process at speed
- Test the agent's capacity to accurately read, extract, and compare complex terms hidden inside PDFs, master service agreements, and RFPs
- Organisations scale procurement operations without proportionally increasing headcount, opening new revenue streams that were previously unprofitable to serve

## AI Tools and AI Sales Agents in the Sales Process

| AI tool type | Primary function | Impact on sales process |
|---|---|---|
| AI assistant | Drafts emails, summarises calls, automates follow-ups | Frees reps from manual tasks |
| AI sales agent | Monitors buyer behavior, triggers outreach, manages lead engagement | Runs sequences autonomously |
| Agentic AI solution | Account planning, territory design, quota setting, deal management | Strategic-level decision support |
| Procurement AI agent | Vendor discovery, RFP generation, order submission | Removes humans from routine purchasing |

[Salesforce's State of Sales 2026](https://www.salesforce.com/news/stories/state-of-sales-report-announcement-2026/) found 87% of sales organisations now use some form of AI for tasks like prospecting, forecasting, lead scoring, or drafting emails. AI sales agents go further, improving response rates and gathering complex information in real time, acting as an AI assistant that enhances customer engagement and lead engagement across the buyer journey.

A concrete example: an AI sales agent monitors buyer behavior signals across a target account, drafts a personalised outreach sequence, and triggers follow-ups based on engagement without any human initiation. The AI outputs from these systems compound over time, making them a genuine competitive advantage for the sales teams that deploy them early.

[Forrester predicted](https://investor.forrester.com/news-releases/news-release-details/forresters-2026-b2b-marketing-sales-and-product-predictions-b2b/) that 1 in 5 B2B sellers would face agent-led quote negotiations in 2026, compelled to respond to AI-powered buyer agents with dynamically delivered counteroffers. The sales process is increasingly a negotiation between software systems, with humans setting the strategy.

## How Artificial Intelligence Is Transforming Product Discovery

Agentic commerce transforms B2B product discovery by allowing AI agents to autonomously navigate product catalogues, understand complex requirements, and complete procurement tasks with minimal human oversight. AI agents interpret natural language queries to find products meeting specific technical specifications, significantly improving efficiency and reducing friction across the customer journey.

The structured product data and product descriptions behind your catalogue determine whether agents surface your brand or a competitor's when they act autonomously on behalf of a buyer. If your product pages don't contain the right data in a machine-readable format, agents building shortlists will simply move on.

Agentic AI adoption is accelerating among organisations that have invested in digital transformation and data quality, because those are the prerequisites for agents to deliver tailored recommendations that profitably serve buyers in this new era.

## The AI Powered Marketing Shift

Agentic AI in B2B marketing enables autonomous decision-making and real-time adjustments, allowing for continuous optimisation of campaigns without constant human oversight. The integration of agentic AI shifts teams from traditional automation to smart orchestration, where AI-powered systems autonomously manage campaign execution across multiple channels.

Agentic AI systems continuously learn from campaign interactions, adjusting audience segments and creative variations based on real-time performance insights. Every cycle produces better AI outputs than the last, compounding the competitive advantage of early agentic AI adoption.

For a detailed look at [AI search statistics](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google) and how citation rates translate into pipeline, the data makes the case clearly. It's also worth reading [why a16z backs GEO](https://firstmotion.com/insights/why-a16z-is-betting-on-geo-and-what-it-means-for-b2b-marketers) to understand why the smartest capital in tech treats this as a structural shift.

## What Agent Ready Actually Means

| Requirement | What it means | Why it matters |
|---|---|---|
| Structured product data | Specs, pricing rules, technical details in machine-readable format | Agents can't evaluate what they can't extract |
| Answer-first content | Buyer questions answered directly in the first 100 words | Agents score pages that lead with the answer |
| Third-party validation | Brand mentions on authoritative external pages | AI cross-references these to establish credibility |
| Live data feeds | Current market indices, inventory, logistics, dynamic pricing | Agents need real-time data to make accurate decisions |
| Tech stack integration | Reads and writes across ERP, CRM, supply chain software | Enables end-to-end autonomous procurement |
| ESG and compliance logic | Corporate ESG criteria and regulatory guidelines in agent policy | Ensures compliant purchasing decisions at scale |
| Audit trail | Human-readable log of every vendor or purchase path decision | Builds operational trust in AI outputs |

Agent ready describes whether your brand and its data can be accurately found, evaluated, and cited by autonomous AI systems operating in procurement workflows. [Only 24% of B2B suppliers](https://www.deloittedigital.com/us/en/insights/research/b2b-commerce-trends.html) have deployed agentic AI, according to Deloitte Digital's February 2026 study of 1,060 suppliers and buyers, despite two-thirds of those not yet using it saying they plan to.

Implement hard coding parameters to prevent hallucinations in contract terms, pricing structures, or vendor selections. Embed corporate ESG criteria, preferred supplier lists, and strict regulatory compliance guidelines directly into the agent's core policy logic.

## The AI Driven Competitive Advantage

[Deloitte Digital's February 2026 research](https://www.deloittedigital.com/us/en/insights/research/b2b-commerce-trends.html) found that digitally mature B2B suppliers exceeded annual sales growth targets by a margin 110% greater than low-maturity peers, and were 5 times more likely to use agentic AI at all. The AI-driven gap is already visible in pipeline and revenue data, and it compounds every quarter.

Brands winning right now share a few characteristics:

- Structured product content that agents can read and evaluate without human help
- Third-party authority built through educational content, case studies, and industry press
- GEO strategy connected to pipeline metrics, not just visibility scores
- AI search treated as a performance channel, not a marketing experiment
- Agentic AI adoption treated as a digital transformation priority, not a future consideration

Every month a brand spends invisible in [AI procurement workflows](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google) is market share handed to a competitor who got there first.

## Security, Governance, and Human Oversight

Protecting negotiation strategies, volume requirements, and sensitive pricing histories from leaking into public LLM training datasets is non-negotiable. Secure communication channels between buying agents and supplier selling agents must prevent phishing, spoofing, and invoice fraud.

Key governance requirements before deploying agentic AI in live procurement:

- Define exact spend thresholds and transaction limits the AI can approve autonomously before requiring human sign-off
- Design interfaces where humans act as strategic supervisors, approving strategy prompts while AI manages execution
- Establish clear triggers for handoff to a procurement professional during high-value negotiation gridlocks
- Ensure the AI maintains a step-by-step, human-readable log explaining every vendor or purchase path decision
- Implement hard coding parameters to prevent hallucinations in contract terms, pricing structures, or vendor selections
- Secure negotiation strategies, volume requirements, and pricing histories from leaking into public LLM training datasets

Organisations must balance technological readiness with operational trust when implementing agentic AI in B2B purchasing decisions.

## Relationship Building in an Agentic World

Here's what most commentary on agentic AI gets wrong: it doesn't make relationships irrelevant. [Gartner's May 2026 research](https://www.gartner.com/en/newsroom/press-releases/2026-05-20-gartner-survey-finds-sixty-nine-percent-of-b-two-b-buyers-turn-to-sales-reps-to-validate-ai-generated-insights) found that 69% of B2B buyers still turn to sales reps to validate AI-generated insights, even as 70% prefer a completely digital self-service buying experience.

Buyers use AI to research independently, but they still need human judgment to confirm what they've found before they commit. [B2B starts with relationships](https://oroinc.com/b2b-ecommerce/blog/agentic-ai-in-commerce/), contracts, and approved supplier lists; AI's job is executing purchases efficiently within those existing agreements.

AI handles the complex tasks and manual tasks underneath, freeing sales teams to focus on the customer experiences and interactions that move the relationship forward. The brands that get this right treat AI as a coworker that handles execution, not a replacement for the human relationships that underpin every major deal.

## The GEO Connection: Your Content Is Evaluated by Software

Success in an agentic world depends on [answer engine optimisation](https://commercetools.com/blog/agentic-commerce-in-b2b-from-efficiency-to-autonomy): structuring product information, pricing rules, technical documentation, and compliance data so AI systems can interpret and trust it. Companies that master this gain preferential placement in AI-assisted procurement cycles and stay ahead of competitors who haven't made the shift.

At FirstMotion, our [PromptPath™ framework](https://firstmotion.com/services/ai-search-optimisation) maps the specific prompts B2B buyers use inside AI tools when evaluating a category, then builds a GEO strategy ensuring your brand is cited in the responses that matter. Our guide to [mapping prompts for AI](https://firstmotion.com/insights/enterprise-b2b-how-to-map-prompts-for-ai-search-generative-engine-optimisation) covers exactly how to understand which queries buyers enter into ChatGPT, Perplexity, and Google AI Mode when evaluating your category.

Brands that invest in educational content and third-party authority now are building the citation signals that agent-led procurement systems will rely on.

## How to Prepare Your Brand for Agentic Buying

The practical starting point is a structured audit of whether your brand can be accurately found, read, and cited by the AI agents your buyers already use. Here's where to focus first:

- **Audit machine-readability.** Can an agent extract your value proposition, pricing structure, and integration capabilities from your product pages without human help? Test this inside ChatGPT and Perplexity before assuming yes.
- **Structure for AEO.** Every page should answer a specific buyer question directly in the first 100 words; agents extract the opening answer and score pages poorly when it isn't there.
- **Build third-party citation signals.** Ensure your brand is referenced accurately on the external pages AI engines trust: review platforms, analyst content, and industry publications.
- **Fix your tech stack.** Verify your agent reads and writes data across your ERP, CRM, and supply chain software, and ensure it has access to live pricing feeds and inventory data.
- **Define human escalation logic.** Establish clear triggers for when agents must hand off to a procurement professional, and define exact spend thresholds they can approve autonomously.
- **Protect sensitive data.** Secure negotiation strategies, volume requirements, and pricing histories from leaking into public LLM training datasets.

## The Agentic Era Requires a New Go-To-Market Logic

The B2B buying unit hasn't shrunk; it's grown a new member that moves faster than any human, evaluates more vendors simultaneously than any team, and builds shortlists before your sales team knows a deal exists. The shift from static workflows to smart orchestration is happening now, whether vendors are ready or not.

The brands that structure content, product data, and digital presence for agent-led evaluation will profitably serve the shortlists of 2027 and beyond. The ones that wait will be filtered out of deals they didn't know existed.

**Find out if AI procurement agents can actually see your brand**

Shortlists are increasingly built inside AI conversations before a vendor gets the chance to respond. As part of our AI search visibility work, our PromptPath™ framework maps the exact prompts your buyers use when evaluating vendors like you, showing where your brand appears and where it's missing. From there, we plan and build the content, structure, and authority needed to close those gaps. [Book a discovery call](https://firstmotion.com/)

## Frequently Asked Questions

**What is agentic AI in B2B buying?**

Agentic AI in B2B buying refers to autonomous AI systems that complete procurement tasks independently on behalf of buyers. Unlike a traditional AI assistant or chatbot, an agentic system discovers vendors, issues RFQs, analyses bids, and submits purchase orders without human prompts at each step. These agentic systems are already deployed across enterprise procurement workflows in 2026.

**How does agentic AI change the B2B buying unit?**

Agentic AI becomes an active participant in the buying unit, handling early-stage research, vendor shortlisting, pricing analysis, and compliance verification before human stakeholders are involved. Forrester's State of Business Buying 2026 puts the typical decision at 13 internal stakeholders and 9 external influencers; agentic AI now compresses and accelerates the work every one of them used to do manually.

**Do B2B sales teams still matter in an agentic world?**

Yes, and recent Gartner research confirms why. 69% of B2B buyers still turn to sales reps to validate AI-generated insights, because buyers use AI to research independently but need human judgment at critical decision points. Human sales teams handle relationship building, strategic negotiation, and the stakeholder dynamics that AI can't replicate.

**What does it mean for a B2B brand to be agent ready?**

An agent ready brand has structured its digital presence so AI procurement systems can accurately find, read, evaluate, and recommend it. That means machine-readable structured product data, answer-first content architecture, third-party citations on authoritative sources, and up-to-date technical and pricing information that agents can extract without human interpretation.

**How does FirstMotion help B2B software brands navigate agentic buying?**

FirstMotion's [PromptPath™ framework](https://firstmotion.com/services/ai-search-optimisation) maps the prompts B2B buyers use inside AI tools when evaluating your category, then builds a GEO strategy ensuring your brand is cited at each stage of the buyer journey. We work exclusively with B2B software companies through VC partnerships, so our methodology is built around complex, multi-stakeholder buying journeys with long sales cycles.

**What's the commercial risk of ignoring agentic AI in B2B go-to-market?**

Deloitte Digital's February 2026 study found that digitally mature B2B suppliers exceeded annual sales growth targets by a margin 110% greater than low-maturity peers. Every month your brand spends invisible in [AI procurement workflows](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google) is pipeline your competitors are building instead. The brands that act now own the shortlists; the ones that wait are filtered out of deals they never knew existed.

---

# AI Search Readiness as a VC Due Diligence Criterion

Source: https://firstmotion.com/insights/ai-search-readiness-as-a-vc-due-diligence-criterion

AI search readiness is rapidly becoming a non-negotiable signal in VC due diligence: the B2B SaaS companies that can be found, cited, and recommended by AI search engines are building a compounding discovery advantage that directly impacts pipeline and valuation.

**Key takeaways**

- Only 22% of marketers are actively tracking AI visibility, leaving most portfolios flying blind
- Six core KPIs replace traditional rank tracking for measuring AI search performance
- Content not refreshed within 13 weeks shows measurable decline in AI citation frequency
- AI search visitors convert at 4.4x the rate of traditional organic visitors, per Semrush

At FirstMotion, we work exclusively with B2B software companies through VC partnerships, helping portfolio companies build systematic AI search visibility before it becomes a competitive liability. Our proprietary PromptPath™ maps the full prompt universe your buyers use and calculates your Brand Visibility Score and Share of Model Voice as baseline metrics any serious investor should want to see. Find out more about our AI search for investors service.

This article explains why AI search readiness belongs in every VC due diligence framework, what it actually measures, and what good looks like for growth-stage B2B SaaS companies in 2026.

## What is AI search readiness and why do AI search engines matter?

AI search readiness refers to the state of preparedness of an organisation's digital assets, content, and infrastructure to be accurately found by AI-driven search engines. It covers everything from how well large language models can parse your content to whether your brand is consistently cited when buyers query AI platforms like ChatGPT, Perplexity, or Google AI Overviews about your category.

It's a meaningfully different discipline from traditional SEO. Traditional search focuses on keyword matching, Google rankings, and ranking positions in search results. AI search readiness is about entity clarity, topical depth, structured data, and content that AI engines recognize and can confidently surface in AI-generated responses.

The shift matters because B2B buyers are now using AI platforms as the first stop in their research journeys. If a portfolio company isn't visible in those AI answers, it's invisible at the exact moment intent is highest.

## Why AI visibility is now a core VC diligence signal

VC due diligence has always evaluated market position and competitive advantage. AI visibility is simply the 2026 version of that question: where does this brand appear when buyers are actively researching a solution?

The gap is striking. Only 22% of marketers have set up LLM brand visibility or traffic monitoring, while brands that do optimise consistently earn significantly more citations than those that don't. That's not a marginal performance gap; it's a structural moat forming in real time across every B2B software category.

The conversion case is equally clear. According to Semrush research, "AI search visitors convert at 4.4x the rate of traditional organic visitors," a pipeline multiplier that shows up directly in CAC and LTV metrics any investor cares about.

## How AI search differs from traditional SEO as a diligence signal

This is where many investors get tripped up. AI search visibility can't be measured using standard keyword rankings, and treating it as an SEO proxy leads to a fundamentally misleading picture of a company's digital market position.

According to Ahrefs Brand Radar research, 28% of ChatGPT's most-cited pages have zero Google organic search visibility. A company could rank on page one in traditional search and be completely absent from the ChatGPT responses its buyers are reading every day. Google Search Console data tells you nothing about how often your brand appears across AI platforms or what AI models say about you versus most competitors.

Traditional SEO scorecards simply don't capture this. We're still in the early days of standardised AI search measurement, but the leading indicators are already clear enough to act on.

## The six KPIs that replace traditional rank tracking for AI search

Six core KPIs replace traditional rank tracking for measuring AI search performance. The shift is from click-through rates to citation rates, and from ranking positions to how often a brand appears in AI-driven answers.

| KPI | What it measures | Why it matters |
|-----|-----------------|-----------------|
| Brand Visibility Score | % of AI responses mentioning the brand | Baseline of AI search presence |
| Share of Model Voice | Citations vs. competitors across AI engines | Competitive positioning |
| Citation frequency | How often brand appears per query set | Consistency of AI recommendation |
| Prompt coverage | % of buyer journey prompts brand is cited in | Funnel-stage visibility |
| AI-referred session quality | Conversion rate from AI referral traffic | Revenue attribution |
| Brand mentions (third-party) | Citations in third-party content LLMs extract | Authority signal for AI engines |

Track brand mentions across AI platforms, not just Google. This is the measurement shift that separates companies building real AI search optimization from those still running a pre-AI playbook.

## AI answers and AI overviews: the new discovery surface

AI answers and AI overviews are now the primary discovery surfaces in B2B search. When a buyer asks ChatGPT about the best tool in a category, the AI-generated response they receive shapes their entire consideration set before they've visited a single vendor website.

The crawl-to-refer ratio tells you everything about how to measure this channel. Cloudflare data from June 2025 shows OpenAI's crawl-to-referral ratio reached 1,700:1, meaning AI platforms crawl content at a scale that dwarfs the referral traffic they send back. Referral traffic volumes from AI platforms are misleading in isolation; what matters is how often your brand appears in those AI answers and how it's framed against competitors.

LLM visibility is built over time through consistent citation signals, not quick wins. The tools and platforms earning the most AI citations have invested in structured data, third-party authority, and topical depth well ahead of their competitors.

## AI platforms and the five dimensions of AI search readiness

When assessing AI search readiness as part of due diligence, these five dimensions give the clearest picture of where a company stands across AI platforms.

### Content structure and the answer first content structure principle

AI engines prioritise well-organised, meaningful content that LLMs extract easily in response to conversational queries. The answer first content structure principle means the first 100 to 150 words of any page are evaluated disproportionately. Clear content structure with direct answers at the top is what determines whether your content enters the AI extraction pool or not.

### Content freshness: the 13-week citation decay threshold

Research from 5WPR identifies 13 weeks as the threshold beyond which content shows measurable decline in AI citation frequency without a refresh. For any B2B SaaS company in a fast-moving category, a stale content programme is an active suppressor of AI search visibility across key pages. ConvertMate's analysis of 80M+ citations found "content updated within 30 days earns 3.2x more AI citations than older pages," making a systematic refresh cycle non-negotiable. Brands that let key pages go stale are handing citation share to competitors who don't.

### Entity clarity, schema markup and structured data

AI systems prioritise entity clarity, topical depth, and structured data above most other signals. Clear entity signals, organisation schema, HowTo schema, and schema markup implemented consistently across the site are now baseline requirements for AI search optimization. Entity relationships between your brand, your category, and your competitors need to be unambiguous for AI engines to confidently cite you.

### Third-party authority and brand mentions

AI engines validate facts by cross-referencing third-party sources to establish brand authority. Review sites, forum discussions, comparison tables, analyst coverage, and external links all feed directly into how favourably a brand appears in AI-generated responses. A company with strong third-party content and active brand mentions is dramatically more likely to appear consistently across AI platforms.

### Data quality, LLM crawlers and technical infrastructure

AI search relies on data quality, requiring businesses to consolidate fragmented data and ensure comprehensive indexing across all sources. LLM crawlers need clean, accessible content across all web properties; server side rendering issues can actively block AI indexing even when content quality is high. AI workloads demand scalable architectures that often use advanced techniques such as Retrieval-Augmented Generation (RAG) and vector databases. Data cleansing is necessary to remove outdated, duplicated, or unstructured data that could cause inaccuracies in AI retrieval processes.

| Signal | Below average | Developing | Strong |
|--------|---------------|-----------|---------|
| Brand Visibility Score | Under 10% | 10 to 22% | 22%+ |
| Content freshness | 50%+ pages unrefreshed over 13 weeks | Mixed | Refreshed within 13 weeks |
| Schema markup | None | Partial | Full org and HowTo schema |
| Third-party citations | Minimal review coverage | Some G2/Capterra presence | Active across multiple platforms |
| Prompt coverage | Not mapped | Partial mapping | Full buyer journey mapped |
| Data infrastructure | Fragmented, no RAG | Partial consolidation | Clean, indexed, RAG-ready |

A Brand Visibility Score above 22% is the strong benchmark for growth-stage B2B SaaS, based on FirstMotion's observed performance across competitive software categories. Staying ahead of most competitors on this metric requires systematic AI optimization as a dedicated programme, not a side project bolted onto an existing SEO workstream.

## How to assess AI search optimization during VC due diligence

The practical challenge for investors is that AI search readiness isn't captured in the standard data room. Here's a structured approach to closing that gap and making informed decisions before close.

### Prompt the AI platforms directly

The fastest diligence step is also the most revealing. Query ChatGPT, Perplexity, and Google AI with the core buyer intent questions in the company's category. Does the brand appear in AI-generated outputs? How is it framed relative to competitors? This takes 20 minutes and surfaces more about real-world AI visibility than any analytics report.

### Ask for a Brand Visibility Score baseline

Any B2B SaaS company serious about AI search should be able to show a Brand Visibility Score across ChatGPT, Perplexity, and Google AI Mode for their core buyer intent queries. If they can't, that's a gap to quantify before close.

### Review the content programme

A quick content audit tells you a lot. Ask these questions:

- What percentage of core pages haven't been meaningfully updated in the last 13 weeks? Content not refreshed within that window shows measurable AI citation decline per 5WPR research.
- Is there an answer first content structure with clear entity signals across key pages?
- Are there original research assets and first-party data for AI engines to cite as a primary source?
- Is schema markup implemented consistently across the site?

### Check third-party presence and brand mentions

Review the company's footprint on G2, Capterra, Trustpilot, and relevant community platforms. AI engines cross-reference these sources constantly, and brands appearing in comparison tables and forum discussions earn significantly more citations. A company with sparse third-party presence is leaving a major citation surface unmaintained.

### Assess data governance and compliance

Organisations should ensure compliance with strict data protection regulations to safeguard data in AI applications. For enterprise-focused SaaS companies, fragmented or non-compliant data infrastructure suppresses AI search performance and creates downstream liability. It's both a visibility issue and a risk flag.

### Assess the team's AI SEO awareness

Ask the marketing or growth lead: how do you measure your AI search performance? If the answer defaults to organic traffic, keyword research, or Google Search Console alone, the team hasn't yet made the shift to AI SEO. Resources like the Gartner Enterprise AI Search Guide can provide structured implementation frameworks for teams at the start of that journey.

## Generative engine optimization: the discipline behind AI search readiness

Generative engine optimization (GEO) focuses on optimizing content for AI language models, which prioritize well-organized, meaningful content over traditional keyword-based strategies. Unlike traditional SEO, which focuses on search visibility through Google rankings and organic clicks, GEO emphasises being cited directly in AI-generated responses, changing how visibility and performance are measured entirely.

GEO requires a shift from traditional metrics like click-through rates to reference rates: how often a brand or its content is cited in AI responses, not how often someone clicks through from search results. Why a16z backs GEO and has published extensively on why it's overtaking traditional SEO as the primary discovery channel is a useful signal for any investor still on the fence.

For investors, the distinction matters because a strong traditional SEO position doesn't imply strong GEO performance. These are separate scores, and the gap between them is often wider than leadership teams realise.

### What the AI-driven data room should include

Most data rooms don't yet include AI search readiness metrics, but that's changing fast. Here's what informed investors should start requesting as standard:

- Brand Visibility Score baseline across ChatGPT, Perplexity, and Google AI Mode
- Share of Model Voice versus named competitors in the category
- Content freshness audit showing percentage of key pages unrefreshed beyond 13 weeks
- Schema markup implementation status across core web properties
- Third-party citation audit covering review platforms, comparison tables, and forum discussions
- Prompt coverage map showing which buyer journey stages the brand is cited in

This data tells a sharper story about real market position than traditional SEO metrics alone, and it surfaces competitive advantages or liabilities that don't appear anywhere else in a standard data room.

### The GEO gap: why most portfolio companies haven't solved this yet

If AI search readiness is this important, why aren't more companies already on top of it? The honest answer is that GEO is still in its early days as a standardised discipline, and most B2B SaaS marketing teams are running SEO playbooks built for a pre-generative world.

Deploying AI search optimization necessitates a cultural shift towards continuous learning within organisations, including upskilling employees on data literacy and prompt engineering. That's not a quick wins exercise; it requires deliberate investment in how teams think about content, measurement, and what search visibility means in an AI-first world.

Only 22% of marketers are actively tracking AI visibility. That means the vast majority of companies in any given VC portfolio are likely underperforming on a channel growing faster than any other, and the gap between those staying ahead and those falling behind is widening every quarter.

## AI search readiness as a post-investment value creation lever

For VCs who support portfolio companies on growth and GTM, AI search readiness is one of the highest-leverage interventions available right now. The steps are well-defined, the results compound, and the cost of closing the gap early is far lower than fixing it at Series B or later.

The practical programme typically covers:

- Establishing Brand Visibility Score and Share of Model Voice baselines across AI platforms
- Auditing and refreshing content across key pages, prioritising anything unrefreshed beyond 13 weeks
- Implementing organisation schema, HowTo schema, and schema markup across the site
- Building third-party presence and active brand mentions on review sites and relevant platforms
- Consolidating fragmented data and deploying data cleansing to remove inaccuracies in AI retrieval
- Mapping prompt coverage across the full buyer journey and closing citation gaps against most competitors

For portfolio companies with the right foundations, a structured GEO programme typically delivers measurable Brand Visibility Score improvements within 60 to 90 days. Check our AI search benchmarks to understand what strong performance looks like across B2B SaaS categories.

### AI search readiness belongs in every term sheet conversation

The B2B buyer journey has moved inside AI platforms. The companies that understand this and invest in AI search readiness early are building a compounding discovery moat that shows up in pipeline velocity, CAC efficiency, and competitive win rates.

For investors, adding AI search readiness to due diligence frameworks isn't about chasing a trend. It's about accurately measuring market position in 2026, where AI search benchmarks have become as important a growth signal as traditional SEO authority or paid channel performance for any B2B software business.

The brands that have figured this out are already pulling ahead. The question for every investor is which side of that gap their portfolio's business sits on.

### Get ahead of the AI search gap in your portfolio

FirstMotion works exclusively with B2B software companies through VC partnerships, helping portfolio companies build systematic AI search visibility before it becomes a liability. Our PromptPath™ platform maps the full prompt universe buyers use, establishes Brand Visibility Score and Share of Model Voice baselines, and delivers a prioritised GEO roadmap that connects directly to pipeline.

If you're a VC investor who wants to know where your portfolio stands on AI search readiness, book a call with FirstMotion and we'll show you what the gap looks like across your categories.

## Frequently Asked Questions

### What is AI search readiness and why does it matter for B2B SaaS?

AI search readiness refers to how well a company's digital assets, content, and data infrastructure are optimised to be found and cited by AI-driven search engines like ChatGPT, Perplexity, and Google AI Overviews. It matters because B2B buyers increasingly start their research inside AI platforms, meaning a company that isn't visible in AI answers is invisible at the highest-intent moments of the buying journey.

### How is AI search readiness different from traditional SEO?

Traditional SEO measures ranking positions and organic traffic from search results. AI search readiness measures citation rates, brand mentions, and Share of Model Voice across AI platforms. Critically, 28% of ChatGPT's most-cited pages have zero Google organic search visibility according to Ahrefs, so traditional SEO metrics don't predict AI performance.

### What are the six KPIs for measuring AI search performance?

The six core KPIs that replace traditional rank tracking are Brand Visibility Score, Share of Model Voice, citation frequency, prompt coverage across the buyer journey, AI-referred session quality, and brand mentions in third-party content. These measure how often and how favourably a brand appears in AI-generated outputs, not how often people click through from search results.

### How quickly can a portfolio company improve its AI search readiness?

With a structured GEO programme, measurable Brand Visibility Score improvements are typically visible within 60 to 90 days. The foundational work covers content freshness, entity clarity, schema markup, third-party presence, and data cleansing, all of which compound over time rather than delivering one-off gains.

### Why do AI search engines favour some content over others?

AI engines prioritise content that's well-structured, answer-first, topically deep, and supported by third-party validation. They evaluate the first 100 to 150 words of a page disproportionately and cross-reference third-party sources to establish brand authority. First-party data like original research and case studies is heavily favoured as a primary source.

### How does FirstMotion help VCs assess AI search readiness in portfolio companies?

FirstMotion works exclusively with B2B software companies through VC partnerships. We use our proprietary PromptPath™ to run a systematic Brand Visibility Score audit across ChatGPT, Perplexity, and Google AI Mode, benchmark each company against category competitors, and build a prioritised GEO roadmap tied directly to pipeline. It's the fastest way to turn AI search readiness from a blind spot into a value creation lever.

### Can FirstMotion support multiple portfolio companies simultaneously?

Yes. Our model is built around VC platform support, meaning we're set up to run AI search readiness audits and GEO programmes across multiple portfolio companies at the same time. We track Share of Model Voice at the category level, so investors get a cross-portfolio view of where the AI search gaps sit and which portfolio companies to prioritise for quick wins on AI search optimization.

---

# How to Optimise Content to Rank in AI Search Results

Source: https://firstmotion.com/insights/how-to-optimise-content-to-rank-in-ai-search-results

To optimise content for AI search, structure it around direct answers, authoritative signals, and semantic clarity rather than traditional keyword density. AI engines don't rank pages; they cite the sources they trust.

**Key takeaways**

- Structure every page so AI systems can extract standalone answers from your individual sections
- E-E-A-T signals now determine which content AI engines choose to cite and surface first
- Long-tail and conversational queries have now replaced short keywords as the dominant search behaviour
- New AI visibility metrics matter far more than traditional keyword rankings and organic traffic

At FirstMotion, we've spent years helping established B2B software companies navigate this shift: from chasing rankings in traditional search results to building content strategies that earn citations inside AI-generated answers. We've mapped buyer journeys across ChatGPT, Perplexity, and Google AI Overviews, and we know exactly what separates content that gets cited from content that gets skipped.

This article covers everything you need: how AI systems process content, which structural and technical signals drive visibility in AI search results, and how to measure performance in a world where the old metrics no longer capture the full picture.

## Why traditional SEO no longer works in isolation

Search behaviour has fundamentally changed. According to Digital Applied's 2026 analysis, nearly 60% of Google searches now end without a click, emphasising the need to build visibility across both organic and AI search results.

A Gartner study predicts a 25% drop in traditional search volume by 2026, driven by the rise of AI-generated answers, which will significantly impact traditional performance metrics like clicks and page visits. The prediction is tracking directionally: chatbot query volume grew 80% year on year through 2025, even if the full 25% displacement hasn't yet materialised.

That's not a distant forecast. It's already happening.

AI-powered search engines like Google AI Overviews, ChatGPT Search, and Perplexity don't reward content that ranks; they reward content that answers. Traditional SEO metrics like click-through rates and organic traffic are becoming less relevant as AI-driven search introduces new visibility metrics based on how often and how prominently content appears in AI results.

According to Ann Smarty's survey, 90% of businesses are concerned about their decreasing visibility online due to AI answers and large language models, indicating a significant shift in search dynamics. That's not a niche concern in digital marketing; it's a market-wide shift demanding a fundamentally different approach to content creation.

## What do AI search engines actually do differently?

AI search engines don't crawl and rank. They synthesise.

When a user submits a query, AI models generate answers by retrieving, reasoning through, and summarising information from sources they deem trustworthy. Your content isn't competing for a position on a results page; it's competing to become the source an AI engine cites in its response.

AI models pull data from structured, modular layouts. They prioritise conversational context and entities over rigid keyword stuffing. Traditional search engines reward pages that match keywords; AI-powered search engines reward pages that answer user queries with clarity and authority. And because AI models cannot create firsthand, original research, they heavily cite recognised authorities instead. If your content doesn't read as authoritative and well-structured, AI engines will skip it.

## How traditional SEO and AI search optimisation compare

| Factor | Traditional SEO | AI search optimisation |
|---|---|---|
| Primary goal | Rank on search results pages | Earn citations in AI-generated answers |
| Success metric | Keyword rankings, CTR, organic traffic | AI visibility, share of answer, citation frequency |
| Content format | Keyword-optimised pages | Structured, modular, answer-first content |
| Authority signals | Backlinks and domain authority | E-E-A-T, named experts, cited sources |
| Query type | Short keyword phrases | Long-tail, conversational prompts |
| Structured data | Helpful but optional | Essential for AI parsability |

According to Semrush AI search research, AI-driven search is expected to surpass traditional search by early 2028, highlighting the urgency for businesses to adapt their content strategies to remain visible. The brands that adapt now build a compounding advantage that becomes increasingly difficult for slower-moving competitors to close.

## How to structure content for AI discovery

Structure is the single biggest lever for AI content optimisation. AI systems process content in chunks, so every page needs to be easy to skim and summarise. Content that isn't structured for extraction won't get extracted.

## Understanding search intent and structuring clear answers

Start every article with a direct, concise answer to the primary query at the very beginning. Don't build to the point; lead with it. AI engines synthesise and reference sources they deem trustworthy, and a clear answer signals exactly the kind of clarity they look for. Understanding the search intent behind every user query is the foundation of AI-optimised content: match intent first, then build the surrounding structure. This applies equally to blog posts, landing pages, and any other content you want AI search platforms to surface.

According to Kevin Indig's citation research, "44% of all ChatGPT citations come from the first 30% of a page's content." That finding alone makes a compelling case for putting your best answer at the very beginning of every piece you publish.

## Use question-based subheadings throughout

Natural language and question-based subheadings dramatically enhance content accessibility for AI search engines. These formats align with how users actually phrase conversational queries when they consume information online. Instead of a generic H2 like "Benefits of structured data," write "Why does structured data improve AI search visibility?" That framing mirrors how users interact with AI tools, and it makes individual sections far more likely to get cited as standalone answers.

Each H2 section should function as a complete answer to a sub-question on its own. AI engines frequently extract individual sections rather than entire articles, so every section needs a clear topic sentence and a clear takeaway that stands without the surrounding context.

## Build with scannable elements that AI can parse

Incorporating scannable elements such as bullet points, numbered lists, and FAQs into content structure improves clarity and helps both users and AI systems quickly identify key information. AirOps' analysis of 548,534 pages found that "85% of pages AI systems retrieve never appear in the final generated answer." Structure and scannability are what separate retrieved pages from cited ones.

Use proper HTML hierarchy throughout: H2 into H3 into H4. Using proper HTML hierarchy helps search engines and AI systems map the relationships between ideas on a page; without those structural signals, even excellent content gets misread or overlooked entirely.

## The role of structured data and schema markup in AI search

Adding schema markup to your content improves its visibility in features like featured snippets and AI search overviews, the formats most likely to displace traditional organic results. Structured data helps search engines understand your content, making it essential for visibility in AI-generated responses.

Schema isn't new. What's new is how the evidence around it has evolved. A May 2026 Ahrefs study tracking 1,885 pages found that adding JSON-LD schema produced no statistically significant citation lift in ChatGPT or Google AI Mode. The honest case for schema in 2026 is more nuanced: it functions as a trust and entity signal that helps AI systems understand what your content represents, even if it doesn't directly move citation counts. Google's own documentation confirms structured data provides a contextual advantage during AI answer synthesis. Implement it as foundational infrastructure, not as a citation shortcut.

## Which schema types matter most for AI search?

| Schema type | Best used for | AI search benefit |
|---|---|---|
| FAQ schema | Q&A content, help pages | Directly feeds AI answer extraction |
| HowTo schema | Step-by-step guides | Structured process content AI engines prefer |
| Article schema | Blog posts, editorial content | Signals content type and authority |
| Product schema | Product pages, comparisons | Enables AI to reference specific offerings |
| Person schema | Author pages, bios | Strengthens E-E-A-T and named entity signals |
| Organisation schema | About pages, home pages | Builds brand entity clarity across AI systems |

Implementing the right schema for your content type removes ambiguity about what your content represents. Think of it as infrastructure: it doesn't guarantee citations, but it gives AI systems the entity clarity they need to trust and reference your content accurately.

## Content clusters and topical authority

Content clusters allow for the creation of multiple, linked articles around a single core topic to establish authority in a subject area. This matters enormously for AI search because AI engines consistently favour sites that demonstrate comprehensive expertise over isolated pieces of content.

A single strong article on a topic is good. A cluster of 10 to 15 tightly linked articles signals topical depth that transfers directly into AI search visibility. AI models assess the breadth and depth of a site's subject knowledge before deciding how much to trust it as a source, and content clusters give them the evidence they need.

## How to optimise content for AI search: writing for search intent and citation

Optimising for AI search engines requires a shift from chasing literal keywords to focusing on clear intent, semantic context, and direct answers. That shift changes how you plan, write, and structure every piece of content you publish.

Here's what that looks like in practice:

- **Lead with the answer.** Put the most direct response to the user's query at the very beginning, in the first 1 to 2 sentences. AI engines extract the opening of a page first.
- **Use natural language.** AI tools prioritise conversational context and entities over keyword stuffing. Write the way people speak, not the way a keyword research tool tells you to.
- **Place the primary keyword early.** Include it in the H1, the first 100 words, at least one H2, the title tag, and the meta description.
- **Cover the topic fully.** Thin content that skims a subject won't earn citations. Valuable content that addresses follow-up questions, provides genuine depth, and serves the user's complete search intent will. Creating content that covers a topic end-to-end is what AI engines reward.
- **Reference named entities.** AI models understand the world through entities: people, companies, tools, and frameworks. Naming and linking to recognised entities strengthens semantic relevance.
- **Keep it up to date.** AI engines favour content that reflects current information. Ahrefs analysed 17 million citations and found that "content updated in the past 90 days earns 67% more AI citations than stale content." Stale statistics and outdated references reduce citation probability significantly.
- **Keep paragraphs short.** AI systems extract information in chunks. Dense walls of text make extraction harder and reader retention lower.

## Optimising content for conversational and long-tail queries

Search behaviour is becoming more conversational, necessitating the incorporation of long-tail, conversational queries throughout the text of every page you publish. AI-driven search queries are typically longer and more specific than traditional keyword searches, often resembling natural questions rather than short phrases. Someone who once searched "best project management tool" now asks "what's the best project management tool for a software development team managing multiple client projects at once?"

Long-tail keywords are your best asset here. They're less competitive than head terms, far more aligned with how users phrase queries to AI tools, and much easier to structure clear, direct answers around. Weave them naturally into subheadings, introductions, and answer-oriented paragraphs throughout your content.

Write the way people talk. That doesn't mean informal or sloppy; it means accessible and direct. Content that reads as genuinely helpful consistently outperforms keyword-stuffed, robotic content across every AI search platform and in the eyes of human readers too.

## E-E-A-T: the authority framework AI engines depend on

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) was developed by Google to assess content quality, and it's now being algorithmically encoded into how AI-driven search results determine which content to feature. AI heavily relies on E-E-A-T principles to filter misinformation, making it essential for site owners to demonstrate real-world experience and expertise in every piece of content they publish.

At FirstMotion, we've seen this pattern consistently across the B2B software companies we work with: the content that earns the most AI citations isn't the most technically optimised; it's the most demonstrably authoritative.

## How to strengthen each E-E-A-T signal

**Experience**

Include specific, firsthand observations in your content. Phrases like "in our experience working with B2B software companies" or "we've seen this approach consistently outperform" signal genuine experience that AI engines prioritise over generic information. AI models cannot create firsthand experience; they depend on content that documents it clearly.

**Expertise**

Demonstrate subject knowledge through accurate terminology, nuanced explanations, and awareness of current industry context. Don't oversimplify to the point of being generic. Content that contains specific claims, named frameworks, and original analysis reads as expert-level to both AI systems and human readers.

**Authoritativeness**

Reference and link to authoritative sites and recognised sources: statistics, research papers, official documentation, and industry publications all strengthen authority signals. According to Princeton's KDD 2024 GEO study, "content with cited sources, statistics, and quotations can improve AI visibility by up to 40% compared to unoptimised content." ConvertMate's 2026 analysis of 12,500 queries across 8,000 domains corroborates those findings, confirming that statistics addition and source citation remain the two strongest GEO techniques across platforms.

**Trustworthiness**

Be transparent about limitations. If something is debated or uncertain, say so clearly. Include a named author with genuine credentials. Make sure all specific claims carry evidence, or label them clearly as opinion. AI systems assess trustworthiness at the page level and the domain level; both matter for sustained AI search visibility.

## Why named authors matter for AI search citations

One of the clearest E-E-A-T signals is a named author with verifiable credentials. AI engines track entities, and a named person with a publishing history, a LinkedIn profile, and cited expertise is a significantly stronger authority signal than "the editorial team."

Implement Person schema on your author pages and link those pages from every article you publish. This directly strengthens the authority signal associated with every piece of content that author produces, and it's one of the fastest ways to improve AI citability across your entire content library.

## Multimodal content: how images and video drive AI discovery

AI search isn't limited to text. Multimodal algorithms read images and videos to formulate answers, which means your content optimisation strategy needs to extend beyond the written word.

Visual elements like images and screenshots not only improve user engagement but also provide additional context for AI search results, making them particularly valuable for instructional content. Alt text, descriptive captions, and image file names all contribute to how AI systems interpret visual content. Every image published without descriptive alt text is a missed signal.

## How video content creates additional AI citation pathways

Embedding videos directly in your content creates another pathway for AI discovery. Many AI engines analyse video transcripts and metadata, making video a rich source for citations. According to Ahrefs' platform citation analysis, YouTube is one of the most cited domains in Google AI Mode. A well-structured video on the same topic as your written article effectively doubles your citation surface area.

Incorporating diverse content types significantly increases your chances of being featured in AI answers. Modern AI systems are increasingly multimodal, and the content that performs best across AI search platforms tends to be the content that helps users discover answers across multiple formats and access points.

## How to measure AI search performance

Traditional SEO metrics like CTR and keyword rankings tell you how visible you are in traditional search results. They don't tell you how often AI engines cite you in generated answers, surface you in ChatGPT Search, or include you in Google AI Overviews.

As AI search evolves, marketers must track new performance indicators that reflect actual visibility in AI-generated responses rather than relying solely on legacy metrics. According to Averi's 680 million citation analysis, "only 11% of domains get cited by both ChatGPT and Perplexity." These aren't slightly different audiences; they're entirely different citation ecosystems requiring distinct optimisation strategies.

According to Ahrefs' 540,000 query analysis, "Google AI Mode and Google AI Overviews cite the same URLs only 13.7% of the time, despite reaching semantically similar conclusions around 86% of the time." If you're optimising for Overviews alone, you're missing a substantial portion of Gemini-powered AI visibility.

## AI search metrics worth tracking

| Metric | What it measures | Why it matters |
|---|---|---|
| AI citation frequency | How often AI tools reference your content | Primary indicator of AI visibility |
| Share of answer | Your brand's presence in AI responses for target queries | Tracks competitive AI search position |
| Referral traffic from AI platforms | Visits arriving from ChatGPT, Perplexity, Gemini | Quantifies AI search as a traffic source in Google Analytics |
| Branded search volume | Searches for your brand name | Signals awareness driven by AI summaries and mentions |
| Google AI Overviews appearances | Presence in Google's AI-generated summaries | Tracks visibility in the most widely used AI search format |
| Traditional rankings | Position in standard search results | Remains a relevant signal alongside AI metrics |

Don't abandon traditional SEO metrics entirely. Google search still drives significant volume, and tracking impressions in Google Search Console remains a useful baseline. What changes is that you track them alongside AI search metrics rather than treating them as the full measure of your content's performance.

## Creating an AI content optimisation strategy built for AI search

The most important insight from working across dozens of B2B software companies is this: AI engines don't discover content randomly. They cite sources they've already determined are trustworthy, authoritative, and well-structured. Building that status requires a sustained, multi-layered content strategy rather than one-off optimisation.

Here's what a consistent AI search content programme looks like:

1. **Audit existing content** for AI parsability: direct answers, clear structure, schema markup, and E-E-A-T signals. Start with your strongest blog posts and highest-traffic pages.
2. **Map content to buyer queries** at every stage of the decision journey, not just top-of-funnel awareness content. AI summaries appear throughout the research process.
3. **Build content clusters** around your core topics to establish the topical depth that AI engines reward with sustained citation frequency.
4. **Run keyword research** around conversational, long-tail queries specific to your category. These are the queries your buyers submit to AI tools, and they should shape your entire content calendar.
5. **Refresh underperforming content** with improved structure, updated statistics, and stronger authority signals before creating net-new content.
6. **Earn mentions on authoritative sites** that AI engines already trust: industry publications, recognised forums, and review platforms like G2 and Capterra. User generated content on these platforms is heavily cited by AI tools.
7. **Monitor AI visibility** across ChatGPT, Perplexity, Google AI Overviews, and Google AI Mode monthly. Highlight key points of progress and gaps, and adjust your content calendar accordingly.

Consistent execution across all seven activities compounds over time. Each piece of content you optimise for AI discovery adds to the authority signal associated with your domain and increases the probability that AI engines treat your site as a primary source.

## The future of AI search and your content strategy

Google's search generative experience has already reshaped how users consume information online. AI Mode, Perplexity's answer engine, and ChatGPT Search are all expanding their reach and improving the quality of their generated answers rapidly.

Content that earns citations in AI-generated answers reaches users who never visit your website directly. It shapes how AI tools describe your product category, recommend solutions, and answer the follow-up questions your buyers ask during their research. That's a fundamentally different kind of organic visibility from a keyword ranking.

Average content used to rank. Average content doesn't get cited. The threshold has moved, and the brands that invest in quality, structure, and authority now are the ones that users discover in AI-generated answers tomorrow.

## Start building your AI search visibility today

At FirstMotion, we've built our entire methodology around this challenge. Our ContextualJourney™ platform maps real AI search behaviour across ChatGPT, Perplexity, and Google Gemini, identifying the content gaps your competitors haven't noticed. Our PromptPath™ framework gives B2B software brands the strategic direction to future-proof their go-to-market as AI-first buyer journeys continue to evolve.

If your best content isn't being surfaced by AI tools, or your exec team is asking why you don't appear in Perplexity or Google AI Overviews for your key use cases, that's exactly the problem we solve. Speak to the FirstMotion team about an AI search visibility audit and a content strategy built for the AI search era.

## Frequently Asked Questions

## What does it mean to optimise content for AI search?

Optimising content for AI search means structuring it so that AI-powered search engines like Google AI Overviews, ChatGPT Search, and Perplexity can accurately parse, extract, and cite it in generated answers. It goes beyond traditional keyword optimisation to include structured data, E-E-A-T signals, conversational language, and direct answers placed at the very beginning of every page.

## How is AI search optimisation different from traditional SEO?

Traditional SEO focuses on ranking in search results and driving clicks to your website. AI search optimisation (GEO or AEO) focuses on earning citations in AI-generated answers, with success meaning you become the source an AI engine references. Traditional performance metrics like organic traffic and CTR are increasingly insufficient as standalone measures of content performance in 2026.

## Does schema markup really help with AI search visibility?

Yes, and significantly. Adding schema markup helps AI systems understand what type of content they're dealing with, improving visibility in features like AI overviews and featured snippets. Research shows content with proper schema markup has a 2.5 times higher chance of appearing in AI-generated answers. FAQ schema, HowTo schema, and Article schema are all particularly effective for the types of content most frequently cited in AI-generated responses.

## How important is E-E-A-T for content to be cited by AI engines?

It's the single most important authority signal AI engines use to decide which content to surface. Named expert authors, cited sources, original research, and authoritative backlinks all directly increase the likelihood of your content being treated as a primary reference by AI models rather than a secondary or ignored source.

## What's the difference between GEO and AEO?

Generative Engine Optimisation (GEO) focuses on optimising content to be cited and surfaced by generative AI tools like ChatGPT, Perplexity, and Google Gemini. Answer Engine Optimisation (AEO) focuses specifically on earning direct answers in AI and voice search responses. In practice, both approaches overlap significantly, prioritising structured content, authoritative signals, and direct answers over traditional keyword optimisation.

## How does FirstMotion approach AI search optimisation for B2B software companies?

FirstMotion combines classic enterprise SEO with AI-native capabilities including prompt mining, GEO strategy, and ContextualJourney™ buyer-journey mapping. Unlike generalist agencies, we focus exclusively on established B2B software and SaaS companies with complex, research-heavy buyer journeys, delivering strategies tied directly to leads, pipeline, and revenue rather than vanity metrics.

## What makes FirstMotion's AI search content strategy different from other agencies?

Our ContextualJourney™ platform mines real prompts from ChatGPT, Perplexity, and Gemini to identify content gaps competitors haven't noticed yet. Our PromptPath™ framework maps every content decision to specific buyer journey stages and AI search behaviours, so every blog post, guide, and landing page we produce earns AI citations from day one rather than being retrofitted later.

---

# AI Search Benchmarks for B2B SaaS: What Good Actually Looks Like in 2026

Source: https://firstmotion.com/insights/ai-search-benchmarks-for-b2b-saas-what-good-actually-looks-like-in-2026

**Author:** Tom Batting
**Date:** May 15, 2026

Good AI search benchmark performance for B2B SaaS in 2026 means your brand is consistently cited by ChatGPT, Perplexity, and Google AI Mode when potential customers research solutions in your category. It's not about ranking on page one; it's about being the brand AI systems recommend.

## Key Takeaways

- A Brand Visibility Score above 22% is the strong benchmark for growth-stage B2B SaaS.
- Only 11% of domains get cited by both ChatGPT and Perplexity; platform optimisation is essential.
- AI-referred visitors convert at 4.4x the rate of traditional organic search visitors.
- Share of Model Voice tracks your brand's presence in AI answers versus competitors.

*At [FirstMotion](https://firstmotion.com), we work exclusively with established B2B software companies navigating this shift. We've seen how brands that benchmark their AI search performance early build compounding visibility advantages that competitors struggle to close. [Speak to our team today](https://firstmotion.com/contact) to find out how we can help.*

This article breaks down the metrics that matter, the benchmarks to aim for, and the practical steps B2B SaaS teams can take right now.

## Why traditional SEO benchmarks no longer tell the full story

Search has fundamentally changed. Traditional tools like Google Search Console track rankings and clicks from search results. But as of mid-2026, approximately 60% of searches end without a single click to a website, according to Bain & Company.

Meanwhile, Google AI Overviews now appear in roughly 25% of all Google searches, according to Conductor's analysis of 21.9 million queries. Your product might rank number one organically and still lose the customer to an AI-generated answer that doesn't mention your brand.

The metrics that matter now sit inside AI-generated responses: how often your brand is mentioned, how you're framed against competitors, and what share of the AI conversation in your category you actually own. This is why AI search benchmarking has become a core part of any serious B2B growth strategy.

If you're new to this space, our [GEO explainer for B2B marketers](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) is a good place to start.

## What B2B SaaS AI search benchmarks actually measure

B2B SaaS stands for Business-to-Business Software-as-a-Service: cloud-based software used by businesses for tasks such as accounting, CRM, and productivity, delivered on a subscription basis that organisations pay a recurring fee to access. Because buyers research these solutions thoroughly before contacting a vendor, the modern B2B buying journey now happens inside AI systems, not search results pages.

AI search algorithms are evaluated by how effectively they retrieve, reason through, and synthesise information in response to a user query. When a potential customer asks ChatGPT to recommend a CRM, the model draws on its stored knowledge, applies relevance scoring, and responds with a summary reflecting its training data.

Unlike traditional SEO metrics, which log rankings and clicks, AI search benchmarks assess how often your brand is present in model responses, how accurately it's represented, and how consistently your content gets retrieved. A comprehensive scoring mechanism evaluates AI search performance based on summary text relevance, citation accuracy, and hallucination rates.

## How AI search models are evaluated: the benchmark landscape

To understand what good looks like for B2B SaaS, it helps to know how AI search systems are assessed. Researchers and regulatory bodies use technical benchmarks to evaluate model capabilities, and these directly shape which systems get deployed and trusted by the buyers you're trying to reach.

General LLM benchmarks like MMLU are less useful for distinguishing top search models because scores are now generally above 90%, creating benchmark saturation. This has prompted researchers to adopt harder evaluations. HLE (Humanity's Last Exam) includes 2,500 expert-level questions, with human domain experts averaging 90% accuracy and top AI models scoring considerably lower on the same tasks.

CRAG and FRAMES are benchmarks focused on retrieval accuracy and reasoning in AI search systems: CRAG tests Retrieval-Augmented Generation (RAG) systems with over 4,400 question-answer pairs, while FRAMES focuses on multi-step reasoning. BeIR evaluates retrieval performance across 18 datasets, including Wikipedia, news, and social media.

Public leaderboards like LMSYS Chatbot Arena encourage competition among AI providers, driving rapid advancements in search model capabilities. The AI systems your potential customers use to evaluate software are continuously upgraded, which means citation requirements evolve alongside them.

## The core AI search benchmark metrics for B2B SaaS

**Brand Visibility Score**

Brand Visibility Score is calculated as the percentage of AI-generated answers for your target prompts that include your brand. According to Search Engine Land, the formula is straightforward: answers mentioning your brand divided by total answers for your space, multiplied by 100.

A score of 22% is a strong benchmark for growth-stage B2B SaaS, based on observed benchmarks across competitive software categories. That means if you run 100 high-intent prompts relevant to your category, your brand appears in at least 22 of the resulting AI answers.

Leading brands in mature SaaS categories push this toward 35 to 40%. If you're currently in single digits, there's a significant citation gap to close before competitors entrench.

[Get your baseline score](https://firstmotion.com/contact) with a FirstMotion benchmark audit.

**Share of Model Voice**

Share of Model Voice translates raw citation data into competitive context. It answers the question: out of every 100 category prompts, how often does AI mention you versus your nearest competitors?

According to [LLM Pulse](https://llmpulse.ai/blog/geo-metrics/), this is one of the most decision-relevant metrics available, because AI answers typically surface only a handful of brands per response. If your Share of Model Voice is 28%, you're appearing in more than a quarter of the category conversation.

Track this metric per prompt cluster, not just at the domain level. A B2B SaaS company in the CRM space should benchmark separately for prompts around CRM, customer journey optimisation, and seamless integration with existing platforms. Each cluster tells a different competitive story.

**Citation frequency across the customer journey**

Citation frequency measures how often your content is retrieved and used by AI systems when answering specific questions. It's distinct from Brand Visibility Score because your content can be used as a source without your brand being explicitly named.

[Search Engine Land](https://searchengineland.com/geo-metrics-to-track-476642) reports that pages updated within the past 12 months are twice as likely to retain citations. Separately, according to AirOps research, more than 60% of citations from commercial queries surface content refreshed within the last 6 months. For B2B SaaS, treating content freshness as a citation maintenance strategy is as important as any technical fix.

**Answer inclusion rate**

Answer inclusion rate measures how often your owned content contributes to an AI answer, regardless of brand name visibility. This matters for informational and mid-funnel queries where AI engines are synthesising information across multiple sources before recommending a solution.

Pages that are easy for AI systems to parse share consistent structural characteristics: clear headers, defined sections, cited statistics, and answer-first formatting. According to Search Engine Land, URLs cited in ChatGPT average 17 times more list sections than uncited pages, and according to AirOps research, pages with 3 or more schema types have a 13% higher likelihood of being cited by AI engines.

**Platform benchmarks: ChatGPT, Perplexity, and Google AI Mode**

Not all AI platforms cite the same content. According to Averi's analysis of 680 million citations, only 11% of domains are cited by both ChatGPT and Perplexity. These aren't slightly different audiences: they're entirely different citation ecosystems requiring distinct optimisation strategies.

| Platform | Citation Behaviour | Content Preference | B2B Buyer Profile |
|----------|-------------------|-------------------|-------------------|
| ChatGPT | Favours encyclopedic, authoritative sources | Long-form, well-structured, cited statistics | Marketing and ops leaders |
| Perplexity | Cites multiple sources per answer with clear attribution | Community content, Reddit, transparent sourcing | Technical buyers and developers |
| Google AI Mode | Driven by Gemini models, synthesises across formats | YouTube, visual content, structured data | Broader research and evaluation phase |

According to Ahrefs' analysis of 540,000 query pairs, Google AI Mode and Google AI Overviews cite the same URLs only 13.7% of the time, despite reaching semantically similar conclusions in around 86% of cases. If you're only optimising for AI Overviews, you're missing a substantial portion of Gemini-powered visibility.

For B2B SaaS companies with complex buyer journeys, the implication is clear: a single GEO strategy won't cover all 3 platforms effectively. Technical buyers using Perplexity for citation transparency need different content signals than marketing leaders defaulting to ChatGPT.

See how we approach platform-specific optimisation at our [GEO agency page](https://firstmotion.com/services/ai-search-optimisation).

## What good looks like: a GEO Score benchmark

Beyond individual metrics, a [GEO Score](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) provides a composite view of your site's structural readiness to be cited by AI engines. Based on Topify's GEO Score benchmark data, a score above 70 is considered competent. Above 85 is where category leaders operate.

B2B SaaS companies start with a natural advantage because they tend to produce high volumes of informational content. The problem is that most of this content is written for humans browsing a features page, not for AI systems trying to extract a specific, self-contained answer.

The most common technical issues suppressing GEO scores include legacy robots.txt files that unintentionally block AI crawlers like GPTBot and ClaudeBot, JavaScript-rendered content that AI crawlers can't parse, and an absence of JSON-LD schema and FAQPage markup. No llms.txt file to guide crawlers toward priority pages is another frequent gap. Fix these structural issues and visibility improvement follows relatively quickly.

## The business case: why AI search benchmarks connect to pipeline

AI search benchmarking isn't a vanity exercise. The commercial data is unambiguous.

According to Semrush research published in June 2025, [AI search visitors convert](https://firstmotion.com/insights/is-ai-traffic-higher-quality-more-likely-to-convert) at 4.4x the rate of traditional organic search visitors. By the time someone arrives via an AI recommendation, the AI has already done the shortlisting work. They arrive pre-qualified and decision-ready.

The volume of B2B buyers now using these channels is significant. Multiple 2025 studies put 89 to 94% of B2B buyers as using generative AI at some point during their purchasing journey, including Forrester's Buyers' Journey Survey and 6sense's 2025 B2B Buyer Experience Report. The brands that aren't benchmarking their AI visibility right now are flying blind through most of the modern B2B customer journey.

See [why AI traffic converts differently](https://firstmotion.com/insights/is-ai-traffic-higher-quality-more-likely-to-convert) and what that means for pipeline forecasting.

## How to set your AI search benchmark baseline

Here's a practical sequence for B2B SaaS teams:

1. **Define your prompt universe.** [Map your B2B prompt universe](https://firstmotion.com/insights/enterprise-b2b-how-to-map-prompts-for-ai-search-generative-engine-optimisation) using our dedicated guide. List 30 to 50 queries your ideal customer profile and buyer personas would ask AI tools during research, and identify which prompt clusters matter most.
2. **Run prompts across platforms.** Use ChatGPT, Perplexity, and Google AI Mode. Log if your brand appears, how it's described, and which competitors are cited alongside you.
3. **Calculate your Brand Visibility Score.** Count brand appearances across all prompts, divide by total prompts, multiply by 100. This is your baseline.
4. **Audit your technical foundation.** Check robots.txt for AI crawler access. Test key pages for schema markup. Validate that your highest-value pages are indexed by AI crawlers.
5. **Analyse the gap.** Identify prompts where competitors are cited and you're not. Assess if it's a format problem, a topic gap, or a relevance issue, and flag which sections need the most urgent attention.
6. **Track Share of Model Voice.** Benchmark against 3 to 5 competitors to prioritise which prompt clusters to tackle first.

From there, building high-quality content around your target audience's tasks and challenges becomes a measurable programme.

## What makes B2B SaaS content citation-worthy in AI search

AI search platforms have fundamentally changed how B2B buyers discover, evaluate, and shortlist software. What all major platforms share is a preference for content structured to respond directly to a specific user query, supported by cited expertise and verifiable data.

## Write for buyer problems, not product features

Your content needs to reflect the real-world problems your customers are trying to solve. A CRM vendor shouldn't only publish content about their software. They should also publish content that helps organisations understand how to manage customer data, analyse pipeline performance, support sales teams at scale, and evaluate cost effectiveness when assessing a new platform.

AI-powered search engines favour content that directly addresses a real user need. Producing high-quality content in formats like blog posts and webinars is one of the most effective strategies in B2B SaaS marketing for building citable authority.

## Address buyer questions about seamless integration and long-term value

B2B SaaS products are delivered on a subscription basis, allowing customers to pay a recurring fee without significant upfront costs. The model offers cost-effectiveness, scalability, automatic updates, and accessibility from anywhere, making it particularly attractive for startups and distributed teams.

A user-friendly marketing site serves as the first point of contact for potential customers after an AI recommendation, so it needs to reinforce the same positioning the AI cited. Organisations in sectors like accounting, legal, and HR are particularly thorough, and SaaS vendors in those verticals need content that addresses compliance, data handling, and integration with existing infrastructure.

## Surface your trust signals in retrievable content

Industry events and third-party resources like analyst reports are trust signals that AI engines retrieve as evidence of market validation. A free trial or freemium version, combined with referral programmes, can also generate the kind of user-validated proof that AI systems recognise.

Co-founder voices carry weight. Content reflecting genuine domain expertise performs well because it signals authentic knowledge. AI systems are increasingly good at distinguishing real expertise from generic marketing content.

## Treat AI benchmark evolution as a content maintenance task

RAG systems and answer engines prioritise citation accuracy, hallucination rates, and the freshness of information when responding to a query. Content maintenance isn't optional; it's how you hold the citations you've earned.

When errors occur in AI-generated answers, such as hallucinated product features or outdated pricing data, brands whose content is consistently cited are most likely to have those errors corrected. Log discrepancies, update relevant pages, and validate corrections have been picked up.

## AI search visibility is a pipeline asset, not a vanity metric

If you're a B2B SaaS company that hasn't yet established your AI search benchmark, the gap between you and the brands already optimising is growing every month. [AI-referred traffic grew 527%](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google) year-over-year between January and May 2025, according to Previsible's AI Traffic Report published in Search Engine Land. The consideration sets AI engines are building around SaaS categories are solidifying fast.

The companies that establish their baseline now, explore their citation gaps, and build systematic programmes around these metrics will own the category conversation. The ones that wait will find themselves benchmarking from behind.

**Start benchmarking your AI search performance today**

[FirstMotion](https://firstmotion.com/services/ai-search-optimisation) helps B2B software companies build systematic visibility across ChatGPT, Perplexity, and Google AI Mode. We use our proprietary PromptPath™ to map your prompt universe, establish Brand Visibility Score and Share of Model Voice baselines, identify citation gaps against competitors, and build a GEO programme that compounds over time.

We work exclusively with established B2B software companies, so our benchmarks are built around long sales cycles, non-linear buyer journeys, and multiple stakeholders. Working through [VC investors](https://firstmotion.com/pricing/investors), we help portfolio companies make this shift with confidence. [Book a call](https://firstmotion.com/contact) to find out where your brand stands.

## Frequently Asked Questions

## What's an AI search benchmark for B2B SaaS?

It's a measure of how often and how favourably your brand appears in AI-generated responses across ChatGPT, Perplexity, and Google AI Mode. Key benchmarks include Brand Visibility Score, Share of Model Voice, and citation frequency across your core buyer intent queries.

## What's a good Brand Visibility Score for B2B SaaS in 2026?

Above 22% is a strong benchmark for growth-stage companies based on observed performance across competitive software categories. Category leaders often reach 35 to 40%. Single digits means a significant citation gap that competitors will exploit if left unaddressed.

## How is AI search performance different from traditional SEO?

Traditional SEO tracks rankings and clicks from search results. AI search performance tracks visibility inside generated answers, where your brand can influence a buying decision before a single click ever happens. With 60% of searches now ending without a click, AI visibility metrics aren't optional anymore.

## Why do buyers convert at higher rates from AI-referred traffic?

They arrive pre-qualified. The AI has already contextualised your solution against their specific challenge before they reach your site. That's why Semrush research found AI search visitors convert at 4.4x the rate of traditional organic search visitors.

## Do we need different content for each AI platform?

Yes. Only 11% of domains are cited by both ChatGPT and Perplexity. Each platform has different citation patterns: ChatGPT favours long-form authoritative content, Perplexity prioritises transparent community sources, and Google AI Mode leans on structured and multi-modal content. One strategy won't cover all 3.

## How does FirstMotion's PromptPath™ framework work?

PromptPath™ maps the full prompt universe your buyers use during research, runs those queries systematically across all 3 major AI platforms, and calculates your baseline Brand Visibility Score and Share of Model Voice. You get a prioritised GEO roadmap targeting the specific prompt clusters where your citation gaps versus competitors are largest. [See how it works](https://firstmotion.com/services/ai-search-optimisation).

## What results can we expect from a FirstMotion GEO programme?

In our experience, clients typically see measurable Brand Visibility Score improvements within 60 to 90 days. We focus exclusively on B2B software companies through VC partnerships, so everything we do connects back to pipeline: Share of Model Voice in high-intent categories, AI-referred session quality, and assisted conversions. [Book a call](https://firstmotion.com/contact) to discuss what's achievable in your category.

---

# What is Google AI Mode and What Does It Mean for B2B Marketers?

Source: https://firstmotion.com/insights/what-is-google-ai-mode-and-what-does-it-mean-for-b2b-marketers

Google has fundamentally changed how people search for information online. The introduction of AI Mode, powered by advanced Gemini models, marks a new paradigm in search. AI Mode delivers conversational, synthesised answers that reshape how B2B buyers research solutions, compare vendors, and make purchasing decisions. For marketers at software and SaaS companies, understanding this shift isn't optional. It's essential for survival. In this article, we'll provide details on how this concept works and what it means for marketers.

## Key takeaways

Google AI Mode is a Gemini-powered, conversational search experience that reduces traditional blue links and is rolling out beyond the US, including the UK as of early 2026. Built on Gemini 2.5 and Gemini 3 models, it represents the most powerful AI search layer Google has ever deployed on top of core search.

AI Mode compresses what previously required multiple searches into a single conversational thread, delivering comprehensive overviews, vendor comparisons, and decision frameworks within one interface. Buyers get more direct answers, fewer clicks, and longer in-answer journeys.

For B2B software and SaaS companies, this accelerates the shift from classic SEO to AI Search Optimisation, including [Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO)](https://firstmotion.com/services/ai-search-optimisation), focused on winning mentions, citations, and recommendations inside AI answers rather than just ranking on page one.

At FirstMotion, we help established B2B software companies systematically improve visibility in AI Mode, Gemini, and other answer engines. [The data suggests this shift is already happening at scale](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google), and B2B marketers must adapt before high-intent interactions disappear from their analytics entirely.

## What is Google AI Mode?

AI Mode is Google's Gemini-powered search experience, a new concept and search feature that fundamentally changes how search works. Instead of the traditional SERP of ten blue links, AI Mode returns a conversational AI answer by default, with supporting links and sources. Think of it as Google's response to ChatGPT and Perplexity: a standalone, opt-in mode designed for complex research and multi-step queries.

Gemini 2.5, a modified version of Google's core AI model, is used in AI Mode to generate concise answers by distilling information gathered from various sources. It's capable of handling complex, multi-step queries, representing a significant evolution from AI Overviews, which appeared earlier in 2024 as snapshot summaries atop traditional results. AI Mode goes further by creating a fully separate, conversational interface.

The rollout context matters for B2B marketers with global audiences. AI Mode launched first in the US via Search Labs before wider availability in late 2025, subsequently expanded to India, and was introduced in the UK by early 2026. Access is typically available through a dedicated tab or icon beside the search bar on Google's homepage. [You can read our full breakdown of the UK launch and what it means for B2B brands here](https://firstmotion.com/insights/google-launches-ai-mode-in-the-uk).

AI Mode can switch between "Fast" and "Pro" model options. Fast mode delivers quick, lightweight answers for straightforward queries, while Pro mode handles complex, multi-criteria questions. Visually, AI Mode looks dramatically different from classic Google search results, with a large AI answer card dominating the top of the page and traditional organic results appearing in a more limited capacity below.

## How to access Google AI Mode

Getting into AI Mode is straightforward for users in supported countries. AI Mode is available through:

* The Google homepage on desktop, via the "AI Mode" tab next to the search bar
* The Google app on mobile, via a dedicated icon or menu option
* Directly at google.com/aimode

Users must be signed in to a personal Google Account to access certain features, including advanced personalisation options. Age eligibility requirements apply, typically 18+ in most regions, and language support remains primarily English in early phases, though this is expanding. Core AI Mode queries remain free for most users, but subscribers to certain Google AI or Gemini plans may see higher usage limits and priority access to Pro model features.

For B2B marketers, the most important step is personal: enable AI Mode on your work machines so you can see first-hand what your prospects experience when they research vendors and solutions. Start searching with the questions your buyers actually ask, and observe which brands, sources, and content types appear in answers.

## How does Google AI Mode work?

Understanding the mechanics behind AI Mode reveals why it represents such a fundamental shift for B2B marketing.

The concept of query fan-out underpins AI Mode's approach: it breaks down user questions into subtopics and issues multiple queries simultaneously. When a buyer asks something like "What's the best project management software for distributed engineering teams with compliance requirements?", AI Mode doesn't just search for that exact phrase. It decomposes the query into sub-questions about project management features, remote team collaboration, compliance frameworks, and engineering workflows, then searches them all in parallel.

The Gemini models then reason over results from web pages, Google News, Maps, Shopping Graph, and other proprietary indexes to synthesise these into a cohesive, narrative-style answer. AI Mode behaves more like an assistant than a list of results. Users ask follow-up questions, refine constraints, and stay inside one evolving conversational thread instead of clicking back and forth across websites.

Marketers must understand the limitations. Model hallucinations remain possible, particularly for niche B2B topics where training data may be sparse or outdated. Guardrails on commercial and YMYL (Your Money or Your Life) content mean answers may not always match brand messaging. This is why actively managing how your brand is represented across AI systems matters as much as traditional SEO.

## Key capabilities inside Google AI Mode that matter for B2B research

Several specific features within AI Mode directly impact how B2B buyers conduct research. Understanding these capabilities helps marketers anticipate buyer behaviour.

## Deep Search

Deep Search represents AI Mode's most powerful capability for B2B research, with the ability to autonomously explore hundreds of related queries on behalf of the user. When activated, it produces expert-style summaries with citations. What previously required hours of vendor research can now be compressed into minutes.

## Multimodal input

AI Mode handles multimodal queries, allowing users to ask questions using text, voice, or images. For B2B contexts, this means prospects can snap photos of dashboards, error messages, or product screenshots and ask AI Mode to explain options or identify alternative tools.

## Agentic behaviour

Inspired by Google's Project Mariner, AI Mode can take actions beyond simply answering questions. It can fill forms, compare multiple SaaS pricing pages, or draft RFP-style checklists based on product categories. Similar capabilities extend to B2B software evaluation.

## Visual generation

AI Mode can generate feature matrices and cost comparison tables directly in the answer, often without requiring a click to any vendor website. For B2B buyers, this means vendor comparisons can happen entirely inside the search interface.

## Conversational continuity

AI Mode maintains conversational continuity, allowing users to ask follow-up questions to refine results without starting a new search. This enables the kind of iterative research typical in B2B buying, where initial broad questions narrow toward specific vendor requirements over multiple interactions.

## Browser integration

AI Mode in Google enables side-by-side browsing in Chrome when clicking a link in an AI summary. This reduces friction when prospects want to explore a cited source without losing their research thread.

## Task organisation

AI Mode's ability to organise tasks and workflows is enhanced by features like Canvas. These tools reflect Google's understanding that B2B research involves multiple sessions, stakeholders, and information sources.

## Gemini 3 Pro and model choices inside AI Mode

AI Mode can run on multiple Gemini model variants, typically a "Fast" default and a more powerful "Pro" option. Understanding these choices matters because the model selection affects which sources get cited and how vendor categories are framed.

Gemini 3 Pro enhances reasoning capabilities and enables advanced image generation. Its ability to deliver more detail in answers is especially valuable for B2B applications, supporting better handling of multi-criteria vendor evaluations and synthesis of technical documentation into concise buyer-level narratives. Pro-powered sessions include dynamic layouts, expandable sections, and interactive visualisations that create an experience closer to a research assistant than a static page.

Availability of Pro inside AI Mode is subject to constraints. Daily usage caps exist, prioritising users on paid Google AI plans. Language limits apply, with English remaining the primary supported language, and regional availability varies, with US and UK users typically having the most consistent access.

B2B marketers should test both Fast and Pro for their core keywords and buyer questions, as the model choice can subtly change which sources are cited, how detailed the answer becomes, and how vendor categories are framed.

## Personalisation and "Personal Intelligence" in AI Mode

Google is layering a "Personal Intelligence" system on top of AI Mode that customises answers based on a user's past searches, Maps activity, Gmail, Calendar, and other Google apps. By 2026, AI Mode connects to Google Workspace to provide highly personalised answers. Current constraints include English-only availability, US-first deployment, and strict account controls allowing users to toggle personal context on or off.

For B2B buyers, this means AI Mode might suggest vendors based on previous trials revealed in Gmail receipts, recommend nearby event venues based on travel calendars, or tailor content to job role and industry inferred from work-related searches. Content that explicitly addresses "CTO evaluating security platforms" or "procurement manager comparing SaaS contracts" has clearer signals for personalisation matching.

Users can correct or override personalisation via follow-up prompts. B2B brands should be transparent in their own data practices as AI search personalisation becomes more common, since prospects increasingly expect clarity about how their information is used.

## What does Google AI Mode mean for B2B buyer journeys?

This is the strategic core of the AI Mode challenge. [AI Mode is fundamentally changing how long, research-heavy B2B journeys unfold](https://firstmotion.com/insights/how-are-ai-search-tools-like-chatgpt-reshaping-the-b2b-buyer-journey), from first problem awareness to vendor selection, determining which companies will thrive and which will struggle.

## Early-stage research changes

Instead of many fragmented keyword searches ("what is CRM", "benefits of CRM", "CRM alternatives"), buyers now leverage AI Mode to answer multi-part questions and produce complete vendor category explanations in a single response. The map of a traditional buyer journey, with its discrete search moments, collapses into extended conversational threads.

## Mid-funnel implications

AI Mode can generate comparison tables, checklists, pros/cons lists, and RFP templates that may name or omit specific vendors. This effectively makes AI Mode a gatekeeper for vendor consideration sets. If your brand doesn't appear in these synthesised answers, you may never make it onto a buyer's shortlist, regardless of your traditional search rankings.

## Late-stage impacts

Buyers can use AI Mode to summarise case studies, translate long technical papers, and sanity-check contracts or SLAs. This reduces direct contact with sales teams until very late in the decision process. Prospects arrive more informed but with perspectives shaped entirely by AI-synthesised content.

## Compressed visible touchpoints

Much of the buyer's learning now happens inside AI Mode directly, and classic web analytics capture a smaller portion of the real journey. According to [6sense's 2025 Buyer Experience Report](https://6sense.com/resources/research/b2b-buyer-experience-report/), buyers are already around 70% through the decision-making process by the time they first reach out to a vendor. [G2's research](https://www.prnewswire.com/news-releases/new-g2-research-half-of-b2b-software-buyers-now-start-their-research-with-ai-chatbots-302742807.html) shows that 51% of B2B software buyers now start their research with an AI chatbot more often than with Google, up from 29% in April 2025.

## Risks and challenges for B2B marketers in an AI Mode world

Ignoring AI Mode while focusing only on classic SEO and paid search creates significant risks for B2B organisations.

## Reduced click-through rates

The introduction of AI Mode has led to a significant decrease in click-through rates for websites. [Ahrefs' study of 300,000 keywords](https://ahrefs.com/blog/ai-overviews-reduce-clicks-update/) found that, as of December 2025, the presence of an AI Overview correlates with a 58% lower average click-through rate for the top-ranking page. For B2B marketers relying on content marketing for lead generation, this represents a fundamental challenge.

## Omission risk

If your brand isn't well-represented in trusted sources, analyst content, or structured data, AI Mode may summarise your category without ever naming your solution. According to [2X's AI Visibility Index](https://www.demandgenreport.com/industry-news/news-brief/2x-survey-finds-96-of-b2b-companies-are-invisible-in-ai-discovery/52536/), 95.7% of B2B companies appear primarily in AI queries where buyers already know the brand name, meaning they are largely absent from the AI-generated answers shaping vendor shortlists at the earliest stages.

## Misrepresentation risk

AI Mode can simplify or generalise complex B2B offerings, potentially underselling capabilities compared with nuanced product positioning. Model limitations mean AI-generated responses may not accurately reflect your differentiation, particularly for technical or specialised solutions.

## Business model disruption

Content marketing strategies built on driving organic traffic face fundamental challenges when answers appear directly in the search engine. Fewer direct links lead to reduced visibility in search results, disrupting traditional business models that rely on web traffic for lead generation.

## Measurement gaps

Traditional metrics like impressions, CTR, and last-click conversions miss the influence of AI Mode answers. These interactions can bias buyers long before they land on your site, creating a "dark funnel" of influence that [standard analytics cannot capture](https://firstmotion.com/insights/how-ai-search-is-making-the-b2b-dark-funnel-even-darker).

## From SEO to AI Search Optimisation: how strategy needs to evolve

Classic SEO foundations remain important, as AI Mode often pulls from high-authority sources that rank well in traditional search results. However, [success requires extending these foundations into AI Search Optimisation](https://firstmotion.com/insights/is-seo-dead-a-b2b-marketers-guide-in-2025), including Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO).

## Understanding GEO

[Generative Engine Optimisation](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) involves shaping your presence so generative AI systems like AI Mode, Gemini, and other answer engines reliably surface your brand, messages, and proof assets in their synthesised answers. This goes beyond ranking to focus on how AI models understand, cite, and represent your content.

## Understanding AEO

Answer Engine Optimisation involves optimising for direct answers, FAQs, and structured explanations, making it easy for AI Mode to quote, cite, or paraphrase your content as authoritative responses. Content structured with clear question-answer formats, comprehensive definitions, and logical organisation performs better in answer engine contexts.

## The new success metrics

While ranking on traditional SERPs still matters, success now also depends on how clearly content maps to buyer questions, tasks, and intents as expressed in natural language prompts. Research from [Princeton and IIT Delhi analysing 10,000 queries](https://arxiv.org/abs/2311.09735) found that GEO techniques can increase AI visibility by up to 40% in controlled studies. B2B marketers should think about "share of answer" alongside "share of search" to reflect this new landscape.

## How to win visibility in Google AI Mode: working with FirstMotion

If you're a B2B software or SaaS brand that relies on organic discovery for pipeline, [FirstMotion](https://firstmotion.com/services/ai-search-optimisation) is built specifically for this challenge. We're an AI-enabled consultancy focused on established B2B software companies, with deep specialism in SEO and AI search optimisation across markets where AI Mode is most active, including the US, UK, and India.

What makes our approach different is that we don't treat AI search as a tactic bolted onto traditional SEO. Our proprietary ContextualJourney™ platform maps complex B2B buyer journeys into concrete search and AI prompts across stages, roles, and scenarios, so your content matches how buyers actually phrase questions inside AI Mode, Gemini, ChatGPT, and Perplexity. We conduct prompt mining and audience intelligence to understand exactly which queries are shaping your category, then align your site content, thought leadership, and support assets to those expressions.

On the technical side, our work combines schema implementation, site structure, and performance optimisation with AI-native strategies like answer-mapping, entity optimisation, and GEO content production. This means clients stay visible in both classic SERPs and AI Mode responses as the landscape evolves. We also support investors and PE-backed portfolio companies with [digital due diligence in an AI search era](https://firstmotion.com/pricing/investors), assessing how discoverable and defensible a target's digital presence is inside generative engines.

If you want to understand where your brand currently stands in AI-generated answers and build a roadmap to improve it, [get in touch with FirstMotion](https://firstmotion.com/contact) for an AI search audit and strategy session.

## Practical playbook: steps B2B marketers can take now

Here's a concise checklist for how an in-house B2B marketing team at an established SaaS company can start adapting to Google AI Mode over the next 3 to 6 months.

## Run systematic tests

Search your core problem statements, product categories, and competitor names inside AI Mode across regions. Record which brands, concepts, and sources appear most often, and create a simple tracking system to monitor changes over time.

| Test category | Example query | What to track |
|---|---|---|
| Brand awareness | "What is [your brand] and what does it do?" | How AI Mode describes you and which sources it draws from |
| Problem awareness | "How do B2B SaaS companies improve AI search visibility?" | Which solutions are mentioned |
| Solution categories | "Best GEO software for enterprise" | Your brand presence and positioning |
| Competitor comparisons | "FirstMotion vs [competitor]" | How your brand appears in comparisons |

## Refresh priority content

Update your most important pages to answer full, natural-language questions rather than narrow keyword variants. Include clear definitions, comparisons, use cases, and step-by-step explanations that AI Mode can easily summarise. Structure content with explicit headers that match buyer questions.

## Implement structured data

Add and improve schema.org markup for products, FAQs, how-tos, and reviews. Clarify entity relationships, such as company, product lines, and industries served, to help AI Mode understand and connect your brand. This structured data feeds directly into how AI models interpret and cite your content.

## Build citation-friendly assets

Develop original research, benchmarks, and frameworks hosted on your site. Syndicate these through trusted publications to amplify authority. [Understanding what makes content citation-worthy for AI systems](https://firstmotion.com/insights/do-backlinks-still-matter-for-ai-search-geo) is key, as AI Mode relies on high-authority sources to inform its answers.

## Map content to prompts

Work with tools or partners to understand how buyers phrase questions at each journey stage. [Aligning content specifically to those prompt expressions](https://firstmotion.com/insights/enterprise-b2b-how-to-map-prompts-for-ai-search-generative-engine-optimisation) rather than traditional keyword targets is fundamental for effective AI Search Optimisation.

## Measurement and analytics in an AI Mode-dominated landscape

When many early- and mid-funnel interactions take place inside AI Mode, where direct analytics data is opaque, B2B teams must rethink measurement approaches.

## Track proxy signals

Monitor branded search trends, direct traffic changes, and category-level demand signals as proxies for AI visibility. Strong AI Mode presence often leads to later-stage brand searches instead of generic queries. An increase in branded search volume can indicate growing AI Mode visibility.

## Prioritise qualitative research

Buyer interviews, sales feedback, and win-loss analysis become more important for understanding how often prospects rely on AI Mode at different journey stages. Ask directly: "How did you first research solutions in this category?"

## Build an AI snapshot library

Save screenshots or transcripts of AI Mode answers for critical queries over time. Track whether your brand is gaining or losing share of answer against competitors. This manual monitoring reveals trends that automated tools may miss.

## Experiment with attribution

Combine web analytics, CRM data, and self-reported attribution questions to capture AI-driven influence. Include "How did you first hear about us?" questions in forms and sales conversations, and accept that some influence will remain unmeasurable.

| Measurement approach | What it captures | Limitations |
|---|---|---|
| Branded search volume | Downstream AI influence | Doesn't show direct AI citation |
| Self-reported attribution | Buyer memory of discovery | Subject to recall bias |
| AI Mode snapshots | Actual brand presence | Manual, point-in-time |
| Sales feedback | Real buyer behaviour | Anecdotal, not systematic |

## Future outlook: where Google AI Mode is heading by 2027

Looking ahead 12 to 24 months reveals trends that should inform B2B marketing strategy today.

## Deeper search integration

Google has signalled intent to gradually integrate AI Mode more deeply into core search, reducing the distinction between experimental and default experiences. As quality improves and regulatory requirements stabilise in key markets, AI Mode features will likely become standard rather than optional.

## Richer agentic workflows

Expect more sophisticated agentic behaviours for business tasks: configuring SaaS product comparisons, automating demo scheduling, or orchestrating trial sign-ups directly from within AI Mode. The line between research and action will blur further.

## Regional variation

Regulatory environments, particularly in the EU, which has more restrictions on generative AI in search under the AI Act, will influence rollout speed and feature sets. Global B2B brands need region-specific strategies and should test and develop approaches for each major market independently.

## Multimodality and personalisation

Voice search, Google Lens integration, image-based queries, and deeper personalisation through Personal Intelligence will expand AI Mode's capabilities. Content strategies must account for users who discover your brand through screenshots, voice queries, or highly personalised recommendations. B2B marketers who invest early in AI Search Optimisation, audience intelligence, and prompt-aligned content will be better positioned as AI Mode becomes the default way professionals research software and vendors.

## FAQ

**Is Google AI Mode replacing traditional Google Search for B2B queries?**

AI Mode is currently an optional, parallel experience layered on top of core search, not a full replacement. Google has signalled it'll gradually bring more AI capabilities into default results over time, but classic organic listings and ads still appear, especially for high-intent and transactional queries. The prudent approach is parallel optimisation: maintain traditional SEO foundations while building AI-native capabilities alongside them.

**How can I see whether my B2B brand appears inside Google AI Mode answers?**

The most direct method is manual testing: run representative buyer questions in AI Mode and look for your brand name, product names, and links in the answer and citations. Document results in a simple spreadsheet over time, tracking presence, position, and wording to identify trends and gaps. For more systematic analysis, specialist partners like FirstMotion can provide structured audits using [prompt mining and established frameworks](https://firstmotion.com/insights/enterprise-b2b-how-to-map-prompts-for-ai-search-generative-engine-optimisation) across markets and buyer personas.

**Does paid advertising influence how often my company appears in AI Mode answers?**

As of 2026, AI Mode's core answers are driven primarily by organic signals, content quality, and authority, not by ad spend. Citations in the main AI answer reflect content authority rather than advertising investment. Strong paid campaigns can still indirectly increase brand visibility and search demand, but they don't guarantee citations inside AI Mode responses.

**What should B2B marketers prioritise first if resources are limited?**

Start with a focused set of high-value journeys: identify 10 to 20 critical buyer questions that precede high-intent opportunities and audit how AI Mode answers them today. Refresh or create content specifically designed to answer those questions comprehensively, with clear language, structured sections, and supporting proof that AI Mode can easily reference. Add basic FAQ and How-To schema to key pages, and monitor changes in branded search volume and sales feedback as early indicators of progress.

**How is FirstMotion different from a traditional SEO agency in the context of AI Mode?**

FirstMotion combines classic enterprise SEO expertise with AI-native capabilities like prompt mining, Generative Engine Optimisation, and ContextualJourney™ buyer-journey mapping for AI search. Unlike generalist agencies serving local businesses or e-commerce, [FirstMotion focuses specifically on established B2B software and SaaS companies](https://firstmotion.com/services/ai-search-optimisation) with complex, research-heavy buyer journeys. Our work spans both strategy and execution, from AI search audits and opportunity models to content roadmaps and ongoing measurement aligned to AI Mode and other emerging answer engines.

---

# Perplexity vs ChatGPT: Which Works Better for B2B SaaS Research in 2026?

Source: https://firstmotion.com/insights/perplexity-vs-chatgpt-which-works-better-for-b2b-saas-research-in-2026

## Key Takeaways

Both Perplexity AI and ChatGPT are advanced artificial intelligence tools: Perplexity is a research-first AI powered answer engine with default real-time web search and inline citations, while ChatGPT is a general purpose AI assistant optimized for reasoning, content creation, and code.

For B2B SaaS research tasks like ICP definition, TAM validation, competitor mapping, and buyer-journey content, the strongest results typically come from combining both tools in a single workflow.

As of April 2026, both perplexity and chatgpt support web search, multimodal input, and free plus paid tiers, but they differ sharply in citation style, data handling, and governance options for teams.

Perplexity excels as a research and information-gathering tool, making it ideal for users who need accurate, up to date information with transparent sourcing; ChatGPT excels at transforming that research into narratives, strategies, and working assets.

[FirstMotion specializes in designing SEO and AI search optimisation workflows](https://firstmotion.com/contact) that intentionally deploy each tool where it performs best for B2B software companies navigating complex buyer journeys.

## What This Comparison Covers (Specifically for B2B SaaS Research)

This article is written from FirstMotion's perspective, focused specifically on long, research-heavy B2B SaaS buyer journeys where organic search and AI discovery drive significant pipeline.

What you'll learn:

Clear definitions of both AI tools and their core functionality in 2026

A feature-by-feature comparison through a B2B SaaS lens

Specific strengths and limitations for market research, competitive intelligence, and content planning

Pricing considerations and ROI thinking for teams

Concrete workflows for tasks like competitor landscapes, buyer-journey mapping, and AI search optimisation (GEO/AEO)

The lens throughout is practical: how should a B2B software marketing, product, or GTM team actually use these latest AI tools in 2026? Expect actionable scenarios with examples from categories like AI data platforms, vertical SaaS, and B2B security vendors.

## Perplexity vs ChatGPT at a Glance (2026 Snapshot)

Both tools have matured significantly through 2025-2026, driven by rapid advancements in machine learning that underpin their latest features and strategic capabilities. However, their design philosophies remain distinct. Here's how they compare for B2B SaaS teams seeking the right tool for their research stack.

## Perplexity AI (Research-First Answer Engine)

**Default web behavior:** Always-on real time web search with every query, delivering real time answers by scanning live sources and summarizing up-to-date information

**Citation style:** Persistent inline numbered citations linking to original URLs

**Primary strength:** Discovering and validating external information with source transparency

**AI models available:** Sonar Pro, Claude, GPT-5.x variants, Gemini (via Perplexity Pro)

**Unique 2026 feature:** Short video generation up to 8 seconds for Pro/Max subscribers

## ChatGPT (Generation-First Assistant)

**Default web behavior:** Web browsing via Search mode (must be enabled or prompted)

**Citation style:** Secondary references, often synthesized into narrative

**Primary strength:** More than just a research engine, ChatGPT acts as an intelligent assistant that turns research into strategy, content, code, and analysis

**Models:** GPT-5.3 Instant, GPT-5.4 Pro, with 128K token context windows

**Unique 2026 feature:** Native Python execution, voice mode, and custom AI assistants (GPTs)

Both now support image generation and image analysis. However, only Perplexity Pro supports built-in video generation as of early 2026.

For B2B SaaS teams, the practical split is clear: choose Perplexity for discovering and validating external information; choose ChatGPT for turning that information into strategy, narratives, and working assets.

## What Is Perplexity? (Research-First Answer Engine)

Perplexity AI is designed as a research-first AI assistant that emphasizes accurate information delivery through real-time web search integration. Perplexity AI work integrates advanced natural language processing with real-time web searches, leveraging large language models to generate responses and providing citations for transparency. As of April 2026, it treats every user query as a small research project, automatically pulling from news sites, academic papers, product documentation, forums, and industry reports to synthesize concise, citation-backed responses.

The core functionality centers on:

Real time web access by default, with no need to enable special features

Persistent inline citations linking directly to source URLs

A source panel showing which domains informed each response

Synthesis of multiple ai models including proprietary Sonar Pro (128K token context), Claude, GPT variants, and Gemini integrations

For B2B SaaS research, this architecture proves valuable for pulling recent funding rounds from Crunchbase, aggregating G2 and TrustRadius reviews, extracting analyst perspectives from Gartner reports, and scanning competitor pricing pages, all with citations for verification.

Perplexity enables targeted searches in specific areas like academic papers, Reddit, or YouTube through its Focus modes, making it a uniquely versatile research tool. The Focus feature can narrow searches to academic papers or specific social forums, which matters enormously for voice-of-customer mining in SaaS user research. Perplexity also offers tailored environments for finance, patents, and travel research.

Perplexity allows grouping related searches into folders for long-term research projects, helping maintain context across multiple sessions. For advanced users or those on higher-tier plans, the perplexity computer feature enables agentic orchestration by running multiple models simultaneously for comprehensive research and end-to-end AI workflows. This is particularly useful for competitive intelligence initiatives that span weeks or months.

From FirstMotion's perspective, Perplexity acts like a fast, citation-heavy analyst for market, competitor, and topical research in AI search optimisation projects.

## Perplexity's Response to B2B SaaS Queries

Understanding how Perplexity's response is structured helps B2B teams extract maximum value from each query. Unlike a standard search engine results page, Perplexity's response combines a synthesized answer at the top with numbered inline citations and a source panel on the side. This means teams don't just get a list of links; they get an interpreted answer they can act on immediately.

Perplexity's response quality depends heavily on prompt specificity. Vague queries produce generic summaries; specific, scoped queries produce citation-dense, actionable answers. It's also worth noting that Perplexity's response evolves in real time, so a query run today may produce a different answer than the same query run six weeks ago, making it particularly valuable for tracking fast-moving categories like generative AI tooling, cybersecurity, or B2B payments infrastructure.

## Perplexity Strengths for B2B SaaS Research

Perplexity is particularly effective for fact checking and academic research, as it provides real time web access and automatic citations, ensuring users receive verifiable information. Here's where it shines for B2B SaaS teams:

**Real-time accuracy with citations:** Pulling April 2026 news on AI data privacy regulation, EU AI Act updates, or the latest features from a competitor's release notes, with numbered sources you can click through

**Breadth of source synthesis:** Combining product docs, GitHub issues, Reddit threads from r/SaaS, and industry blogs into one answer, often citing 10-20 sources per response, which helps users extract key insights from aggregated data for more informed decision-making

**Early-stage discovery:** Building an initial longlist of vertical SaaS competitors in logistics, AI CRM vendors, or integration partners in a niche you're just entering

[**GEO/AEO visibility research**](https://firstmotion.com/services/ai-search-optimisation)**:** Seeing which pages and domains Perplexity repeatedly cites for key queries like 'how to choose compliance software' or 'best AI data platforms 2026', revealing where your content needs to appear

[**Voice-of-customer mining**](https://firstmotion.com/insights/enterprise-b2b-how-to-map-prompts-for-ai-search-generative-engine-optimisation)**:** Using Focus modes to restrict searches to Reddit discussions or YouTube reviews, uncovering buyer pain points and objections in specific SaaS categories

Perplexity's real-time web search capability makes it particularly effective for academic research, fact checking, and understanding complex topics, as it synthesizes information from live sources with clear source attribution. The inline citation format makes it straightforward to verify claims directly against original sources.

## Perplexity Limitations and Risks

While Perplexity delivers strong citation coverage, B2B teams must understand its constraints:

**Hallucination despite citations:** It can still synthesize incorrectly or over-index on popular sources; high-stakes claims like security certifications or customer counts require clicking through and validating against primary sources

**Weaker multi-step planning:** Less effective at building multi-quarter content roadmaps, funnels, or detailed buyer-journey narratives on its own; better at answering questions than structuring complex strategies

**Conversation memory limits:** Perplexity may forget previous parts of a conversation more quickly than ChatGPT, making long iterative sessions less seamless

**Internal data constraints:** Difficult to 'teach' Perplexity your internal CRM analytics or proprietary data unless integrated via enterprise APIs

**Compliance and privacy:** Public Perplexity instances shouldn't be fed confidential product roadmaps, customer lists, or unannounced funding information; regulated B2B sectors (FinTech, HealthTech, cybersecurity) need enterprise-grade configurations with legal review

Perplexity can explain code but lacks the interactive Python environment found in ChatGPT, limiting its utility for data analysis workflows that require execution.

## What Is ChatGPT? (Generation-First Conversational Assistant)

ChatGPT is a conversational AI assistant and generative tool optimized for creative writing, coding, reasoning, and complex tasks. In 2026, powered by OpenAI's GPT-5.x family including GPT-5.3 Instant for quick tasks and GPT-5.4 Pro for advanced reasoning (both with 128K token context windows), it functions as a generation-first assistant rather than defaulting to live web retrieval. ChatGPT's response to user queries is known for its quality, depth, and ability to translate inputs into clear, accurate, and actionable outputs.

Key features relevant to B2B SaaS teams:

**Long-context conversations:** Project-style threads that maintain context across extensive planning sessions

**Search/browsing modes:** When enabled, blends real time data into conversational answers for up to date news and market developments

**Custom GPTs:** Tuned assistants for specific B2B tasks like GEO content prototyping, sales objection handling, or technical documentation

**Code and data workflows:** Native Python execution, CSV analysis, visualization generation, and SQL scripting directly in the interface. ChatGPT is also highly capable at generating code, assisting with debugging, and supporting developers in creating and optimizing software across multiple programming languages.

ChatGPT offers integration for image generation and direct file analysis, as well as voice conversations through ChatGPT's voice mode. ChatGPT's voice mode enables hands-free, interactive conversations for more natural, voice-based user interactions, and supports real-time visual queries, useful for analyzing screenshots of competitor interfaces or product diagrams.

For B2B SaaS applications, ChatGPT excels at drafting product positioning, messaging frameworks, email sequences, sales decks, and SQL/Python scripts for analytics. While a knowledge cutoff exists for offline model knowledge, web-enabled modes bridge the gap for 2025-2026 developments.

FirstMotion uses ChatGPT internally to prototype GEO/AEO-focused content, buyer-journey-aligned prompts, and structured asset formats for clients.

## ChatGPT's Response Format and Problem Solving

ChatGPT's response style differs fundamentally from Perplexity's. Where Perplexity's response is structured around sourced facts, ChatGPT's response is built around reasoning chains and narrative flow, ideal for tasks where the output needs to persuade, instruct, or plan. For complex problem solving, this matters: ask ChatGPT to evaluate three go-to-market approaches for a new compliance product, and it'll reason through trade-offs, surface assumptions, and recommend a path. That kind of structured problem solving is hard to replicate with a research-first tool.

ChatGPT's response also compounds with context. The more background you provide, the more tailored the output. For iterative problem solving, ChatGPT's threading model lets teams refine outputs across multiple follow up questions without losing context, particularly effective for tasks like workshopping a positioning statement or progressively building out a buyer persona.

## ChatGPT Strengths for B2B SaaS Research and Strategy

ChatGPT is better suited for creative writing tasks, such as generating stories, scripts, and marketing copy, due to its superior natural language generation capabilities. Here's where it delivers for B2B SaaS:

**Research-to-strategy transformation:** Converting raw Perplexity outputs into [structured ICP definitions](https://firstmotion.com/insights/how-are-ai-search-tools-like-chatgpt-reshaping-the-b2b-buyer-journey), JTBD breakdowns, and narrative storylines for positioning

**Planning ability:** Creating 6-12 month SEO plus AI search content roadmaps targeting each stage of a complex B2B buyer journey

**Code and data analysis:** Generating Python, R, or SQL for analyzing data from CRM exports, win-loss records, or keyword datasets; building dashboards and ROI calculators for RevOps

**Conversational depth:** Iterating on positioning angles, refining messaging for different personas, and workshopping objections like a virtual strategist

**Multimodal analysis:** Analyzing screenshots of competitor pricing pages or product diagrams and summarizing differentiators for product marketing teams

ChatGPT is well-suited for learning complex topics, as it can provide detailed explanations and step-by-step breakdowns that adapt based on user feedback. For coding and debugging tasks, ChatGPT outperforms Perplexity by providing sophisticated code generation and interactive problem solving across multiple programming languages.

ChatGPT frequently outperforms other models in complex problem solving and multi-step reasoning tasks. It can adopt different personas and write high-quality scripts, blog posts, and marketing copy. ChatGPT dominates creative tasks including storytelling, marketing, coding, and conversational long-form content.

## ChatGPT Limitations and Risks

Despite its strengths, ChatGPT carries specific risks for B2B SaaS research:

**Outdated training data without Search:** Without browsing enabled, it may rely on outdated information for fast-moving SaaS categories like AI data platforms consolidating through 2025-2026

**Hallucination risk for concrete facts:** Funding amounts, customer counts, and security certifications require explicit cross-checking with primary sources

**Secondary citation style:** Comparatively, ChatGPT's sources are often less prominent or authoritative than those of Perplexity. Even with web access, references are synthesized into narrative rather than cited inline, requiring extra diligence for analyst-grade research

**Privacy and compliance requirements:** B2B SaaS teams should use enterprise-grade ChatGPT with data controls for sensitive GTM strategy, pricing tests, or M&A analysis

**Direction not destination:** ChatGPT outputs work best as direction and drafts, with human experts validating numbers, legal statements, and security claims before publication

ChatGPT excels in generating original content such as articles, code, and creative writing, while Perplexity is more focused on research-driven synthesis rather than long-form creative content.

## Key Differences Between Perplexity and ChatGPT (Through a B2B SaaS Lens)

Both chatgpt and perplexity share the same underlying large language models paradigm, but their distinct design philosophies (retrieval-first versus generation-first) create meaningfully different user experiences for B2B research. Notably, customizable AI tools like GPT can be tailored to execute particular tasks, such as database querying or interview simulation, further enhancing their versatility for different user needs.

Key differences for B2B SaaS teams:

**Information retrieval:** Perplexity defaults to real time search with transparent source attribution; ChatGPT requires enabling Search mode and synthesizes web data into narrative

**Conversation depth:** ChatGPT maintains richer context across long sessions; Perplexity excels at discrete, source-heavy queries

**Planning ability:** ChatGPT is stronger at multi-step reasoning and creating structured roadmaps; Perplexity is better at answering specific research questions

**Code and data workflows:** ChatGPT runs code and analyzes files natively; Perplexity explains code but can't execute it

**Enterprise collaboration:** ChatGPT offers more mature enterprise admin tools as of 2026; Perplexity is catching up with secure enterprise options

Perplexity AI stands apart as a research librarian or analyst: fast, source-heavy answers optimized for 'what's true now?' questions. Think of ChatGPT as a strategist or copywriter who takes inputs and transforms them into narratives, frameworks, plans, and working code. For AI search optimisation, Perplexity serves as a good proxy for answer engines (revealing what surfaces today); ChatGPT helps [design content and prompts tailored to perform well on those engines](https://firstmotion.com/insights/enterprise-b2b-how-to-map-prompts-for-ai-search-generative-engine-optimisation).

## ChatGPT and Perplexity as Complementary AI Chatbots

The most effective B2B SaaS teams aren't choosing between chatgpt perplexity: they're deploying both as complementary AI chatbots within a structured research-to-content pipeline. Perplexity is the intelligence analyst: fast, precise, grounded in current sources. ChatGPT is the strategist and writer: exceptional at synthesizing inputs into polished, long-form outputs. Neither role is redundant. From a governance perspective, teams should define which workflows use which tool, what data can be inputted, and how AI-generated outputs are reviewed before external use, and treating both as raw productivity tools without governance leads to inconsistent quality and elevated compliance risk.

## How They Handle Web Search and AI Search (GEO/AEO)

Understanding how each tool handles web search matters enormously for B2B teams focused on AI search optimisation. Perplexity's approach: every query triggers real time web search by default, with citations showing which domains it trusts for a given topic. This transparency makes it invaluable for understanding how AI search engines currently perceive your category. ChatGPT's approach: web browsing is a mode that must be enabled or prompted; when active, it blends live data into conversational answers, but citations are less central to the experience.

[How FirstMotion uses this distinction](https://firstmotion.com/contact): Perplexity samples which assets appear in answer engines for key B2B SaaS queries like 'best SOC 2 compliance software 2026' or 'top AI data platforms for enterprise.' ChatGPT designs the GEO/AEO content formats, FAQ structures, and prompt patterns that help surface client assets across AI platforms. Together, they reveal both 'what AI search is surfacing today' and 'what content we should create to win those surfaces.'

## How They Handle Data, Code, and Files

For B2B SaaS revenue and analytics teams, the data handling difference is significant. ChatGPT's paid tiers can run Python code, analyze files directly, and generate visualizations, ideal for internal performance analysis like examining HubSpot exports or building cohort analyses. Perplexity is superior when data lives on the public web: industry benchmarks, conversion rate surveys, and third-party analyst reports. The rule of thumb: ChatGPT owns 'inside the firewall' data work; Perplexity owns 'outside the firewall' intelligence gathering.

## Perplexity vs ChatGPT: Pricing and Value for B2B Teams (2026)

Treat these figures as April 2026 approximations, as pricing changes frequently.

Both Perplexity and ChatGPT offer a freemium pricing model, allowing users to access basic features for free while providing paid plans that unlock advanced capabilities, additional subscription tiers, security features, and customization options for enterprise and API access.

## Perplexity Pricing Tiers

**Free version:** Limited daily queries, access to standard models

**Perplexity Pro:** Priced at $20/month for individuals, which unlocks Sonar Pro, Claude, GPT variants, faster responses, higher limits, and video generation. Perplexity Pro is tailored for research-focused users.

**Perplexity Max:** Priced at $200 per month, unlocks advanced features such as multi-model access and enhanced research capabilities, making it suitable for heavy research users

## ChatGPT Pricing Tiers

**Free version:** Basic GPT access with limited features

**ChatGPT Plus:** Priced at $20/month with higher limits and better model access. ChatGPT Plus is designed for users needing creative task support.

**ChatGPT Pro:** Priced at $100 per month, providing significantly more usage and advanced features compared to Plus

**Enterprise plans:** $30-$100+/user with SSO, admin controls, and data retention policies

Perplexity Pro and ChatGPT Plus are both priced at $20 per month, but they cater to different user needs, with Perplexity focusing on research and ChatGPT on creative tasks. ChatGPT offers a higher-tier plan, ChatGPT Pro, priced at $100 per month, which provides significantly more usage and advanced features compared to its Plus plan. B2B SaaS leaders should prioritize enterprise-grade paid plans once teams start sharing sensitive data or integrating with internal systems, with ROI thinking focused on research hours saved, content velocity improvements, and reduced dependence on expensive analyst reports.

## Perplexity Pro: Is It Worth It for B2B SaaS Teams?

Perplexity Pro is designed for research-intensive users who need access to multiple AI models, higher query limits, and advanced features like video generation and agentic research workflows. The core value lies in model flexibility: Pro subscribers can switch between Sonar Pro, Claude, GPT-5.x variants, and Gemini within the same interface, matching model capability to task type. It also unlocks Spaces, Perplexity's collaborative research environment for organizing related searches and maintaining context across long-term projects. At $20 per month, the same price as ChatGPT Plus, the right choice depends entirely on whether your primary bottleneck is research and discovery or strategy and content generation. Most serious B2B teams will want both.

## When to Choose Perplexity: Signals and Use Cases

Knowing when to choose Perplexity comes down to whether your primary need is discovery or generation. Choose Perplexity when you need to know what's happening right now. If your question starts with 'what are the current...' or 'which vendors are...' or 'what did \[competitor\] announce...', it's almost always the right starting point. Its always-on web access means you're working with live intelligence, not model memory that may be months out of date. Also choose Perplexity when citation transparency matters, for analyst-grade research, investor briefs, or externally published content, and for GEO/AEO audits, where seeing which domains Perplexity cites for target queries is the most direct proxy for AI search visibility available without enterprise tooling.

## Is Paying for Pro/Plus Worth It for B2B SaaS?

For serious B2B deep research (ICP development, market mapping, AI search optimisation), paid tiers quickly justify themselves through higher limits and better models. Recommend Perplexity Pro for product marketing, strategy, and competitive intelligence roles who need citation transparency for credibility. Recommend ChatGPT Pro/Enterprise for content, RevOps, and data/BI-adjacent roles who need stronger reasoning, file analysis, and code execution. Treat both tools as part of a broader AI stack with clear usage guidelines and training, rather than allowing ad-hoc experimentation without governance.

## Research and Information Gathering: Where Each Tool Leads

Research and information gathering is the most common use case for both tools, yet each approaches it differently. For tasks requiring breadth and recency, Perplexity leads clearly, given its ability to pull from dozens of sources in a single query and present a citation-backed synthesis is unmatched for surface-level market intelligence. For tasks requiring depth and synthesis, ChatGPT takes over, transforming raw Perplexity outputs into structured deliverables like competitive matrices, JTBD analyses, or messaging hierarchies. The most common mistake B2B teams make is using ChatGPT for tasks that need real-time sourcing, or Perplexity for tasks that need structured strategic output.

## Real World Performance: How Both Tools Perform in Practice

In practice across B2B SaaS use cases, Perplexity consistently delivers on its core promise of fast, sourced answers to specific research questions. Teams that invest in writing precise, scoped prompts see significantly better real world performance. ChatGPT's real world performance is more variable: with minimal context it can produce generic outputs, but with rich context, specific constraints, and clear output formats, it's exceptional for strategy, positioning, and content tasks. From FirstMotion's direct experience, real world performance is most consistent when teams build prompt templates for recurring tasks, eliminating variability and allowing junior team members to produce senior-quality outputs reliably.

## When to Use Perplexity vs ChatGPT for B2B SaaS: Concrete Scenarios

This section provides practical 'if you're doing X, use Y like this' guidance tailored to B2B SaaS marketing, product, and GTM teams.

Common workflows and which tool leads:

| Workflow | Primary Tool | Secondary Tool | Why |
| --- | --- | --- | --- |
| Market/category research | Perplexity | ChatGPT | Real-time sources, then narrative synthesis |
| Competitor intelligence | Perplexity | ChatGPT | Current data, then positioning strategy |
| Buyer-journey mapping | ChatGPT | Perplexity | Structure and planning, informed by discovery |
| Keyword and topic research | Both equally | N/A | Different strengths per phase |
| Content creation | ChatGPT | Perplexity | Generation with research validation |
| Sales enablement materials | ChatGPT | Perplexity | Narrative structure with current proof points |
| AI search visibility audit | Perplexity | ChatGPT | See what surfaces, then optimize for it |

When using ChatGPT to simulate Perplexity's outputs for content optimization, it's valuable to analyze Perplexity's response to specific prompts, especially for answer engine optimisation, since Perplexity's response often provides detailed, technically accurate insights that can be directly used to refine content for answer engines and improve practical applicability.

| Scenario | Start with Perplexity | Then use ChatGPT |
| --- | --- | --- |
| Top-of-market and category research | Map vendors, funding, acquisitions, and analyst perspectives. Click into Gartner Magic Quadrants, TechCrunch, and key blogs for deeper sourcing. | Synthesize into a category narrative: history, current dynamics, emerging subsegments, and differentiation opportunities. |
| Competitor and positioning research | Pull value propositions, feature tables, recent launches, and public pricing. Always validate pricing on the actual competitor site. | Compare positioning angles, craft messaging pillars, and role-play as a skeptical economic buyer to surface objections your content must address. |
| Buyer journey mapping | Use Focus modes to mine Reddit, G2, and YouTube for real buyer questions at each stage. | Organize into a structured journey: awareness, problem framing, solution exploration, vendor comparison, and validation. Map each to content formats and GEO/AEO prompts. Feeds into FirstMotion's ContextualJourney™ methodology. |
| SEO and AI search (GEO/AEO) content | See which pages and formats are cited for target queries across category and non-Google surfaces. | Design content clusters, pillar pages, and answer-engine-friendly structures. Build prompt libraries mapping buyer intents to AI-ready formats. |
| Sales and executive materials | Harvest competitive proof points, third-party validations, and market data for pitch decks and one-pagers. | Structure narratives: problem-solution decks, ROI calculators, objection-handling scripts, executive summaries. Always verify numbers against CRM and finance before external use. |

## How FirstMotion Uses Both Tools in AI Search Optimisation Projects

FirstMotion is an AI-enabled consultancy for established B2B software and SaaS companies navigating the shift toward AI-driven discovery. Our work focuses on SEO and AI search optimisation for companies with long, research-driven buyer journeys.

Perplexity serves as the discovery and validation workhorse: Market landscapes, competitor positioning, regulatory trends, and citation patterns across AI answer engines

ChatGPT serves as the strategy and content design workhorse: ICP definitions, buyer-journey frameworks, content roadmaps, and prompt playbooks

Our [ContextualJourney™ platform](https://firstmotion.com/services/ai-search-optimisation) integrates outputs from Perplexity (audience signals, real questions, citation patterns) into structured buyer-journey maps created and refined via ChatGPT. The goal's never to pick a 'winner' but to architect a repeatable research-to-content pipeline that boosts digital visibility and pipeline in the AI search era.

## Example: Using Perplexity and ChatGPT in a SaaS Due Diligence Project

Consider an investor evaluating a data-security SaaS company in early 2026. Phase 1 (Perplexity): Rapidly map the competitive landscape, pull EU AI Act regulatory trends, and aggregate customer sentiment across G2, TrustRadius, and Reddit. Perplexity surfaces 15-20 sources with clear citations, revealing which competitors are gaining mindshare and which compliance concerns dominate buyer conversations.

Phase 2 (ChatGPT): Synthesize those findings into a strategic brief covering positioning risks, growth opportunities, go-to-market strengths, and AI search visibility gaps, structured for investment committee review, with clear recommendations and follow up questions for management. This combined approach helps investors make evidence-based bets on product and GTM priorities in an AI-disrupted search environment.

## Final Verdict: Which Should B2B SaaS Teams Choose?

There's no universal winner in the perplexity vs chatgpt comparison. The best choice depends on whether you're gathering external facts or turning insights into strategy and content.

Choose Perplexity when you need current, sourced external information with transparent citations: competitor updates, market data, regulatory developments, and AI search visibility patterns. Choose ChatGPT when you need deep thinking, planning, writing, coding, and data analysis, transforming research into positioning narratives, content roadmaps, buyer-journey maps, and working analytics scripts.

Serious B2B SaaS organizations should treat both as complementary tools in their research and GTM stack, with training and governance rather than ad-hoc use. Budget for paid tiers where sensitive data or high-volume usage is involved. Audit your 2024-2026 workflows and identify where each tool could replace manual research, spreadsheet assembly, or slow agency cycles, and the productivity gains compound quickly.

If your team's navigating AI search optimisation, buyer-journey complexity, or the challenge of staying visible across both traditional search engines and AI platforms, [FirstMotion can help](https://firstmotion.com/contact) design workflows that integrate both tools for higher-quality leads and pipeline. We work with established B2B software companies to build research-to-content systems that actually move the needle in 2026's discovery landscape.

## FAQ: Perplexity vs ChatGPT for B2B SaaS Research

These FAQs address common questions B2B SaaS leaders ask about AI chatbots for research.

## Can I rely on Perplexity or ChatGPT alone for due-diligence-level research?

Neither tool should serve as a sole source for investment, legal, or security-critical decisions. They're powerful accelerators, not replacements for primary research. For a research paper or formal analysis, AI outputs should inform your direction, not constitute your evidence. Use both to surface questions and sources quickly, then validate key claims via SEC filings, contracts, and internal data.

## How do privacy and data security differ between the tools for B2B SaaS use?

Both vendors offer enterprise plans with stricter data handling, but teams must review current 2026 policies rather than assuming defaults protect sensitive data. Never paste sensitive PII, unreleased financials, or customer lists into public instances. Work with legal and security to configure approved enterprise versions before using either tool for confidential GTM strategy or M&A analysis.

## Which tool is better for understanding AI search impact on our [existing SEO strategy](https://firstmotion.com/insights/is-seo-dead-a-b2b-marketers-guide-in-2025)?

Perplexity is better for observing how AI answer engines surface information in your category, showing which domains and pages it cites for target queries. ChatGPT is better for rethinking content architecture to improve that visibility. FirstMotion combines both in [AI search visibility audits](https://firstmotion.com/insights/is-ai-traffic-higher-quality-more-likely-to-convert): Perplexity reveals where answer engines are shifting discovery; ChatGPT redesigns content formats to capture emerging surfaces.

## How should we train our marketing and product teams on these tools?

Recommend short, role-specific playbooks over generic 'AI training,' with approved use cases for each tool. Start with 3-5 core workflows per team: brief creation, competitor research, content outlines, with review checkpoints for AI-generated outputs. Train teams on Perplexity's Structured Spaces for long-term project context, and on natural conversations and iterative prompting for ChatGPT.

## What's the first practical step if we want to integrate Perplexity and ChatGPT into our 2026 GTM planning?

Start with one pilot initiative: reworking a key product line's buyer-journey content using both tools. Document time savings, note where human review caught errors, and measure early AI search visibility indicators. Then scale across other product lines. The same prompt tested across both tools reveals their complementary nature: Perplexity delivers the facts, ChatGPT delivers the framework.

## How do follow up questions work differently in each tool?

In Perplexity, follow up questions trigger new web searches, producing freshly sourced answers each time, ideal for drilling deeper into a topic. In ChatGPT, follow up questions build on accumulated context, better suited for iterative refinement where each exchange sharpens the previous output. A practical approach: use Perplexity for follow up questions needing new external facts, then switch to ChatGPT to synthesize those facts into a usable output.

---

# Best GEO Agencies in London: 10 Top Partners for AI Search in 2026

Source: https://firstmotion.com/insights/best-geo-agencies-in-london-10-top-partners-for-ai-search-in-2026

**Author:** Alex Price | **Date:** April 21, 2026

London's best GEO agencies combine entity optimisation, structured data, and LLM-ready content to get brands cited in ChatGPT, Google AI Overviews, and Gemini. The commercial opportunity is significant: brands appearing in generative responses earn trust and visibility at the earliest stages of the customer journey.

As [AI search becomes a primary discovery channel for B2B buyers](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google), the agencies that understand how to optimise for generative platforms, not just traditional blue links, are pulling ahead. London has become the natural home for that specialisation.

## Key takeaways

- GEO requires structured data, entity clarity, and LLM-ready content that traditional SEO doesn't address
- The best GEO agencies track [AI visibility across ChatGPT, Gemini, and Perplexity, not just Google Search Console](https://firstmotion.com/insights/is-ai-traffic-higher-quality-more-likely-to-convert)
- B2B SaaS firms with long sales cycles need specialists who map content to multi-stakeholder buyer journeys
- London GEO retainers typically run £3,000 to £25,000 per month depending on scope and complexity
- Integrating GEO with traditional SEO gives brands coverage across both blue links and AI generated answers

At FirstMotion, we've spent years helping established B2B software and SaaS companies win visibility across both traditional and generative search. Our proprietary ContextualJourney™ platform maps real AI search behaviour to buyer-journey gaps your competitors haven't spotted yet.

This guide covers the 10 best generative engine optimisation agencies in London for 2026, what makes each one distinctive, and how to choose the right fit for your business.

## What is a GEO agency and why does London matter in 2026?

Generative engine optimisation is the practice of making your content citation-worthy for AI systems: ChatGPT, Google Gemini, Google AI Overviews, Microsoft Copilot, and Perplexity. It's a [fundamentally different discipline from traditional SEO](https://firstmotion.com/insights/why-a16z-is-betting-on-geo-and-what-it-means-for-b2b-marketers), which focuses on ranking signals and backlinks rather than [how large language models select and synthesise information](https://firstmotion.com/insights/from-keywords-to-conversations-the-ai-search-revolution-in-b2b-saas).

GEO requires a different approach to traditional SEO, demanding structured data, entity clarity, and authoritative content at every layer of your site.

It also demands a sharper understanding of content optimisation: how individual pages are structured, cited, and parsed by AI models before they ever surface in a response.

Leading London agencies have developed unique metrics to track AI visibility inside LLMs, which traditional tools can't measure. That measurement gap is one reason specialist GEO agencies are increasingly sought over generalist digital marketing shops.

The best GEO agencies optimise for entire ecosystems, ensuring brands show up in AI generated answers, summaries, and sources of authority. When selecting a GEO agency, look for transparency and realistic expectations regarding the evolving nature of AI search.

GEO services in the UK typically start from around £1,500 to £3,000 per month for SMEs, with enterprise level projects costing significantly more. Full-stack GEO and technical SEO services for growing businesses typically sit between £1,500 and £8,000 per month. London's talent pool, GDPR expertise, and density of AI startups make it the natural home for GEO specialisation in 2026.

## The role of digital PR in generative engine optimisation

Digital PR has become a cornerstone of effective GEO strategy and one of the most underused levers in AI search visibility. In the context of generative engine optimisation, it goes well beyond traditional link building: it's about amplifying your brand's presence to influence both human audiences and AI systems simultaneously.

A robust digital PR campaign increases brand mentions in authoritative publications and news outlets. Those brand mentions and backlinks act as signals that AI systems use to assess authority and relevance, directly impacting your visibility in AI generated answers and AI Overviews.

Digital PR shapes AI search behaviour by ensuring your brand is consistently referenced in contexts that matter to your audience.

It feeds the authority signals that AI platforms like ChatGPT, Gemini, and Perplexity rely on when deciding which sources to cite.

Content strategies that combine digital PR with technical GEO work consistently outperform approaches that treat them as separate workstreams. The brands that invest in both simultaneously build compounding authority that neither tactic achieves alone.

## 10 best GEO agencies in London for 2026

The following agencies were selected based on demonstrable GEO practice between 2024 and 2026, London headquarters or a major London office, and a strong track record in AI influenced search environments. No agencies paid to appear.

| Agency | Best for | Pricing |
|---|---|---|
| FirstMotion | Established B2B SaaS and software companies with long sales cycles and complex buying committees | On quotation |
| Passion Digital | Brands wanting GEO integrated with paid media across all channels | £3,000 to £10,000 per month |
| Found | Larger brands with extensive content libraries needing restructuring for AI parsability | On quotation |
| Bird Marketing | London companies with multi-market ambitions in regulated sectors like fintech | £2,500 to £9,000 per month |
| SUSO Digital | Brands with large SaaS documentation hubs or ecommerce catalogues needing technical GEO foundations | £2,000 to £7,000 per month |
| Buried | Scale-ups wanting aggressive, ROI-led organic growth across traditional and generative search | On quotation |
| Exposure Ninja | Businesses wanting a structured GEO programme with internal education alongside outsourced delivery | £2,000 to £8,000 per month |
| Blue Array | Organisations with in-house teams needing senior GEO leadership rather than full outsourcing | On quotation |
| Varn | Companies in regulated sectors wanting compliance-focused GEO built on solid information architecture | On quotation |
| Charle | DTC, retail, and Shopify Plus brands wanting AI visibility in product discovery flows | On quotation |

### 1. FirstMotion (specialist B2B SaaS GEO agency, London)

**Best for:** Established B2B SaaS and software companies with long sales cycles and complex buying committees.

FirstMotion is a London-based specialist consultancy [built exclusively for B2B software and SaaS firms](https://firstmotion.com/contact). Its proprietary ContextualJourney™ platform maps real AI search behaviour to buyer-journey gaps, identifying prompts your competitors haven't optimised for. Visibility is tracked across Google AI Overviews, Gemini, ChatGPT, and Perplexity, with entity and schema audits that have driven measurable pipeline improvements for clients in cybersecurity and DevOps. Measurement is always tied to leads, opportunities, and ACV rather than impressions or vanity rankings.

Services: GEO strategy and audits, entity and schema optimisation, AI search monitoring, buyer-journey mapping, answer engine optimisation, digital due diligence for investors.

Pricing: On quotation.

### 2. Passion Digital

**Best for:** Brands wanting GEO integrated with paid media across all channels.

[Passion Digital](https://passion.digital) is a London-headquartered Google Premier Partner (2023 to 2025) and Drum Recommended Agency, now backed by US AI tech firm Pixis.ai. That backing brings intelligent forecasting, real-time optimisation, and automated content workflows to their AI search offering. Content strategies span paid media, organic search, and generative AI, making them a strong fit for brands that want all channels aligned rather than GEO treated in isolation.

Services: GEO and AI search visibility, paid media integration, content strategy, automated content workflows, AI-powered forecasting.

Pricing: £3,000 to £10,000 per month.

### 3. Found

**Best for:** Larger brands with extensive content libraries needing restructuring for AI parsability.

[Found](https://www.found.co.uk) operates with a proprietary Everysearch™ methodology and Luminr platform, built to track search visibility across both traditional engines and generative AI platforms. Recognised by The Drum and Google as a top partner, the agency focuses on how large language models surface brands across Google AI Overviews, Bing Copilot, and Gemini. Case studies show significant visibility uplifts for retail and B2B clients, with particularly strong content optimisation work for brands managing large page volumes.

Services: AI search monitoring, content restructuring for AI parsability, GEO strategy, traditional SEO, Everysearch™ methodology.

Pricing: On quotation.

### 4. Bird Marketing

**Best for:** London companies with multi-market ambitions in regulated sectors like fintech.

[Bird Marketing](https://bird.marketing) is a multi-award-winning agency recognised across Clutch, GoodFirms, and major industry awards, with a London office serving international clients. It combines technical SEO foundations with generative-ready content and AI analytics, integrating AI search visibility from the outset rather than adding it as an afterthought. Their regulated sector expertise makes them a strong fit for fintech and compliance-heavy businesses operating across multiple jurisdictions.

Services: Technical SEO, generative-ready content, AI analytics, GEO strategy, enterprise-level AI search visibility.

Pricing: £2,500 to £9,000 per month.

### 5. SUSO Digital

**Best for:** Brands with large SaaS documentation hubs or ecommerce catalogues needing technical GEO foundations.

[SUSO Digital](https://susodigital.com) is a technically focused London SEO agency that has extended deep expertise into GEO, with particular emphasis on structured data and LLM-friendly site architecture. Their content optimisation process identifies pages already close to citation-worthy and prioritises those for structured data improvements first, making progress measurable from early in an engagement. Published results include 594% AI traffic growth and 321% AI conversion uplift for a global healthcare brand, and 862 AI Overview citations for Skyscanner.

Services: Technical GEO audits, schema implementation, structured data optimisation, LLM-friendly site architecture, content citation optimisation.

Pricing: £2,000 to £7,000 per month.

### 6. Buried

**Best for:** Scale-ups wanting aggressive, ROI-led organic growth across traditional and generative search.

[Buried](https://www.buriedagency.com) is a UK agency with a strong London presence, founded by award-winning growth marketer Will Tombs to integrate AI-driven search with performance-focused SEO. GEO is treated as a core specialisation rather than a bolt-on service, with all content strategies built around pipeline and digital marketing efficiency from day one. The agency focuses on mid-market brands where revenue outcomes matter more than platform mentions or visibility metrics that don't convert.

Services: GEO strategy, performance SEO, AI search integration, ROI-focused content strategy.

Pricing: On quotation.

### 7. Exposure Ninja

**Best for:** Businesses wanting a structured GEO programme with internal education alongside outsourced delivery.

[Exposure Ninja](https://exposureninja.com) is a well-established UK agency with London reach, known for a documented 9-pillar methodology that now incorporates GEO and AI-influenced search. It blends AI-optimised content, semantic keyword architectures, and comprehensive schema, pairing GEO with digital PR and review generation to build compounding authority signals. Their frameworks are particularly well-documented, making them a strong fit for teams that want to build internal GEO capability as they scale.

Services: GEO strategy, AI-optimised content, semantic keyword architecture, schema implementation, digital PR, review generation.

Pricing: £2,000 to £8,000 per month.

### 8. Blue Array

**Best for:** Organisations with in-house teams needing senior GEO leadership rather than full outsourcing.

[Blue Array](https://www.bluearray.co.uk) is a hybrid SEO consultancy with strong London roots, known for embedding specialists within client teams as strategic advisors. Founder Simon Schnieders won Best Large SEO Agency at the UK Search Awards. GEO work focuses on technical foundations and entity clarity, with engagement formats spanning audits, training, and ongoing strategic governance. It's a particularly strong fit for PE-backed SaaS portfolio companies that need senior direction without replacing an existing team.

Services: GEO audits, entity clarity, technical SEO, embedded advisory, team training, strategic governance.

Pricing: On quotation.

### 9. Varn

**Best for:** Companies in regulated sectors wanting compliance-focused GEO built on solid information architecture.

[Varn](https://varn.co.uk) is a search agency with strong technical pedigree and a London presence, focusing on information architecture and structured content for AI models. GEO services centre on entity modelling, schema markup, and content optimisation for AI clarity, the kind of foundational work that enables AI systems to understand, trust, and cite a brand consistently. Their regulated sector experience makes them well-suited to healthcare, finance, and professional services clients.

Services: Entity modelling, schema markup, information architecture, content optimisation for AI, GEO strategy.

Pricing: On quotation.

### 10. Charle

**Best for:** DTC, retail, and Shopify Plus brands wanting AI visibility in product discovery flows.

[Charle](https://www.charleagency.com) is a London-based ecommerce and Shopify-focused agency that has added GEO and answer engine optimisation services for product discovery in AI-powered search environments. The agency integrates technical audits, CRO thinking, and content optimisation to align GEO outcomes with customer lifetime value. Content strategies are built specifically for DTC brands where product discoverability in AI search directly affects revenue, with a strong focus on product-level structured data and entity clarity.

Services: Ecommerce GEO, answer engine optimisation, product structured data, technical audits, CRO integration, Shopify Plus optimisation.

Pricing: On quotation.

## Understanding AI Overviews, AI platforms and their impact on generative search

Google AI Overview features are fundamentally reshaping how users interact with search results. Unlike traditional search, where users sift through blue links on search engine results pages, AI Overviews deliver concise, synthesised answers directly within the search interface.

For brands, this shift means ranking well in traditional SEO is no longer enough on its own. AI platforms now prioritise content that's optimised for generative search: structured data, entity clarity, and authoritative information that can be easily cited in AI generated answers.

Agencies ensuring brands are cited and discovered by AI platforms such as ChatGPT and Google Gemini have to work across content quality, technical infrastructure, and earned authority simultaneously.

That's a fundamentally different brief from traditional digital marketing or organic search work.

Comprehensive AI search monitoring lets you track where and how your brand appears across various AI powered search environments. AI visibility data is now a distinct reporting category from Google Search Console data, and the two measure fundamentally different things.

Brands that adapt quickly by working with the right GEO agency and committing to entity-led content strategies will be best placed to capture attention across both traditional and AI powered search results.

## How GEO agencies work with AI powered search and AI SEO in 2026

As of 2026, the AI search ecosystem spans Google AI Overviews, Gemini, ChatGPT, Copilot, and Perplexity, all drawing from structured and unstructured web data to produce AI generated responses. Typical GEO workflows follow a clear sequence of phases:

- **Audit:** Entity gap analysis, schema review, and content parsability assessment
- **Content reframing:** Restructuring into Q&A formats, adding quotations and statistics for citation-worthiness
- **Evidence enrichment:** Adding authoritative sources, expert quotes, and data points that LLMs favour
- **Monitoring:** Tracking brand mentions in ChatGPT, Gemini answers, and AI Overview appearances
- **Training data attention:** Canonicalisation, freshness signals, ensuring content enters RAG indices appropriately

For B2B SaaS brands, GEO focuses on long-cycle queries like "best SOC 2 compliance software for mid-market fintechs" or "top DevOps platforms with GDPR features." These are [complex, multi-intent queries where AI models synthesise multiple sources](https://firstmotion.com/insights/enterprise-b2b-how-to-map-prompts-for-ai-search-generative-engine-optimisation) into a recommendation.

Training data optimisation ensures your content is fresh, canonical, and structured correctly before it enters a model's knowledge base. Agencies also focus on content optimisation for voice-based and conversational queries to secure answer-ready content positions.

Content structured around the way people speak to AI platforms consistently outperforms traditional keyword-led content. That's a core principle of AI SEO that generalist agencies often miss.

Effective LSI keyword research for conversational and generative queries is another area where specialist agencies outperform generalists. Search recognition strategies that account for how AI crawlers index and prioritise content are now a core part of any serious GEO programme.

## How to choose the right GEO agency - content strategies, content optimisation and digital marketing

The right GEO agency depends entirely on your business model, company stage, tech stack, and internal capabilities. For B2B software and SaaS companies, prioritise agencies with proven long-form content strategies, an understanding of complex buying committees, and a clear methodology for mapping content to multi-stakeholder journeys.

- **GEO and SEO integration:** How do you balance organic traffic growth with AI search visibility?
- **Entity and schema:** Can you show an entity audit from a similar client?
- **AI monitoring:** Which AI platforms do you track? What dashboards do you use?
- **Revenue measurement:** How do you connect GEO work to pipeline, ACV, or CAC payback?
- **Category experience:** Have you worked with companies in our specific vertical before?

Before engaging any agency, run a GEO readiness audit and verify they understand keyword research for conversational generative search queries, not just traditional search. Check whether they've tracked AI visibility in Gemini or ChatGPT for previous clients, and ask about their content optimisation approach for generative platforms specifically.

If you're operating in B2B software or tech with long buyer journeys, consider speaking with FirstMotion about a platform agnostic GEO strategy tailored to your category.

## Why FirstMotion is a strong choice for B2B SaaS GEO and AI visibility in London

FirstMotion is purpose-built for established B2B software and SaaS companies that rely on organic search and AI driven discovery for demand generation and pipeline growth. It's not a generalist digital marketing agency that's added "AI SEO" to its service list: it's a specialist consultancy built for complex B2B buying and competitive search environments.

The ContextualJourney™ platform maps real AI search behaviour by mining prompts from ChatGPT, Perplexity, and Gemini to identify content gaps competitors haven't noticed. Content strategies are tied to specific stakeholders (CFO, CISO, Head of RevOps, engineers) across months-long research cycles.

Measurement is always connected to leads, opportunities, and ACV. AI visibility reporting covers all major generative platforms, not just Google.

FirstMotion also supports investors with digital due diligence for PE firms assessing how visible portfolio targets are inside generative engines. That capability is increasingly relevant as AI platforms reshape how buyers discover and evaluate software.

**Ready to build your GEO game plan?**

If your B2B software or SaaS brand isn't showing up in AI generated answers, you're losing AI visibility at the exact moment prospects are forming shortlists. FirstMotion's GEO audit and strategy workshop identifies where you're being missed and what it'll take to close the gap.

[Request a GEO audit or strategy workshop with FirstMotion](https://firstmotion.com/contact) to assess your current AI search visibility and build a clear roadmap for 2026 and beyond.

## Frequently Asked Questions

### How much do GEO services cost in London in 2026?

UK GEO retainers start at £1,500 to £3,000 per month for SMEs. Mid-market B2B SaaS brands with content production and AI monitoring typically pay £6,000 to £12,000 per month. Enterprise or multi-country programmes run £10,000 to £25,000 or more. Project-based audits start at £8,000 to £20,000 depending on complexity.

### How long before I see GEO impact in AI search results?

Brands with existing authority typically see early signals within 8 to 16 weeks, particularly in AI Overviews. Competitive B2B categories need 6 to 12 months for meaningful coverage, with full programme maturity at 12 to 18 months. The fastest early wins come from fixing entity clarity and structured data first.

### Do B2B SaaS companies really need a specialist GEO agency?

For firms with deal sizes above £50k ARR and research cycles of 3 to 12 months, yes. LLMs increasingly influence high-intent discovery searches, and generalist teams miss nuances like integration queries and procurement-driven prompts. Specialists like FirstMotion structure work around multi-stakeholder research patterns that generalist agencies can't replicate.

### Will GEO replace traditional SEO entirely?

No. In 2026, both coexist. Technical SEO foundations like site architecture, crawlability, and core web vitals directly influence how AI platforms parse and trust content. Think of GEO as building on SEO, not replacing it.

### How can I tell if an agency genuinely understands GEO?

Ask how they track AI visibility in Gemini, ChatGPT, and AI Overviews, whether they can show a sample GEO audit with entity gaps identified, and how they approach training data and RAG sources. Agencies with real expertise will discuss entity salience, conversational keyword research, and citation-worthiness rather than just using "AI SEO" as a buzzword.

### What should I have ready before engaging a London GEO agency?

At minimum: Google Analytics and GSC access, ICP and buyer persona documentation, a product and positioning overview, and clarity on target verticals, geographies, and deal size. GEO requires input from marketing, content, sales, and RevOps. FirstMotion starts with a discovery and buyer-journey workshop to align these inputs before implementation begins.

### What's the difference between GEO and answer engine optimisation?

GEO optimises content, structured data, and brand signals so AI platforms cite your business in generative responses. AEO focuses specifically on direct answer features like featured snippets, voice search, and AI Overview boxes. The best agencies treat both as complementary, building authority signals that deliver coverage across all AI search formats.

---

# From Keywords to Conversations: The AI Search Revolution in B2B SaaS

Source: https://firstmotion.com/insights/from-keywords-to-conversations-the-ai-search-revolution-in-b2b-saas

**Tom Batting • October 3, 2025 • Generative Engine Optimisation • 7 min read**

The world of B2B SaaS is built on discovery. Digital transformation has accelerated changes in B2B SaaS discovery, reshaping how buyers and businesses interact with solutions. For years, discovery has meant mastering search engines through traditional SEO: keywords, backlinks, and ranking mechanics. Search engine optimization (SEO) is the process of improving the quality and quantity of website traffic from search engines, helping buyers discover relevant content. But a major shift is underway. Buyers are no longer typing a few words into Google and scrolling through ten blue links. They are asking questions in natural language, expecting instant, context-aware answers, and AI search has created new ways for buyers to discover solutions. This is the AI search revolution, and it is transforming how B2B SaaS companies and businesses are found, evaluated, and chosen. In this article, we explore the behaviour changes driving this shift, how AI search differs from traditional search, what it means across platforms, and the strategic implications for SaaS marketing leaders, including the impact of AI search on B2B SaaS businesses.

## The Search Behaviour Shift

The B2B buyer journey has always been research-intensive. A typical SaaS purchase involves multiple stakeholders, months of consideration, and dozens of touchpoints. Traditionally, search engines like Google acted as the gateway to information. Buyers typed in keywords such as "best CRM for mid-sized businesses" or "enterprise project management software" and sifted through results, blogs, and review sites. Today, that same buyer is just as likely to turn to AI search tools. Customers now expect more conversational and context-aware answers from these tools. Instead of typing "best CRM", they might ask:

- _"Which CRM platforms integrate natively with HubSpot and support AI automation?"_
- _"What are the key differences between Salesforce, Pipedrive and Zoho for a 100-person B2B sales team?"_

The difference is subtle but profound. Search is no longer about keywords, it's about conversations. AI-powered engines automatically interpret a wide range of search queries, compare options, and summarise insights instantly. AI-powered search engines use Natural Language Processing (NLP) and Machine Learning (ML) to understand user intent and context. In many cases, buyers receive answers directly in the search results without visiting a given page. This means buyers are reaching informed conclusions faster, with fewer clicks, and often without ever landing on a vendor's website. For SaaS companies, this shift means visibility and influence are no longer guaranteed by simply ranking high on Google.

## Traditional Search Engine Optimization vs AI Search

| Aspect | Traditional SEO | AI Search |
|--------|-----------------|-----------|
| Primary focus | Keywords and ranking | Buyer intent and context |
| Result format | Links to websites | Summarised answers, comparisons |
| Ranking signals | Backlinks, site authority, on-page SEO | Authority, structured knowledge, semantic depth |
| User action | Multiple clicks and research | One conversational query |
| Content type rewarded | Blog posts, keyword-optimised landing pages | In-depth expertise, structured documentation, conversational answers |

Indexing and file management practices have evolved in AI search, with less emphasis on manual control of files and more on structured data and semantic understanding.

Traditional SEO rewarded content volume, technical optimisation, and backlinks. It relied heavily on PageRank, indexing, and managing files and URLs to ensure visibility in search results. For example, managing multiple URLs and same content was crucial, specifically through canonical tags and redirects to consolidate link equity and avoid duplicate content issues. Writing content and creating content remain important, but the focus has shifted toward authority and clarity. The choice of domain and descriptive URLs can still influence search visibility, especially when targeting specific markets or improving user experience. Creating compelling and useful content influences a website's presence in search results more than any other SEO suggestions. It is less about chasing every keyword and more about ensuring your solution is consistently represented in AI-generated answers.

## Platform Differences

| Platform | AI Search Impact | SaaS Marketing Implication |
|----------|------------------|---------------------------|
| **Google SGE** | Contextual overviews and comparisons in search results | Focus on structured content and schema markup |
| **ChatGPT, Perplexity, Claude** | Synthesis of answers from multiple sources | Ensure documentation, thought leadership and comparisons are widely accessible |
| **G2, Capterra, TrustRadius** | Frequently cited as authoritative sources | Build reviews, manage sentiment, encourage customer advocacy |
| **LinkedIn, Reddit, X** | Peer conversations summarised in AI responses | Invest in thought leadership and community presence |

AI search is not one monolithic channel. It manifests differently across Google's SGE, independent AI tools, review platforms, and social networks. SaaS marketers must consider visibility across all these points of influence. Providing access to valuable resources across platforms is essential for enabling member participation and keeping users informed.

Connecting different types of content, including forum posts and other user-generated resources, can enhance visibility in AI search by improving discoverability and supporting ranking considerations.

## Strategic Implications for B2B SaaS

| Implication | Why It Matters | Practical Steps |
|------------|----------------|-----------------|
| Authority & Expertise | AI references trusted voices | Publish expert insights, technical guides, case studies, and focus on building strong relationships with clients and partners |
| Structured Data | AI uses schema and structured docs | Implement schema, publish comparison tables, improve documentation |
| Review Platforms | AI cites reviews frequently | Encourage reviews, manage profiles, drive sentiment |
| Content Strategy | Conversational queries matter | Write for humans and AI, answer niche buyer questions |
| New Metrics | Rankings are not enough | Track AI citations, share of voice in generative search |
| Brand Strength | Recognition influences trust | Invest in PR, thought leadership, and consistent messaging. Highlight your company's ability to adapt and aim for leadership in AI search. |

The shift to AI search demands a broader strategy. SaaS companies must optimise for authority, structure, and presence across multiple platforms, while measuring success in new ways. It is also important to promote your company and services both online and offline to maximize reach and brand impact.

## From Search to Conversations

The AI search revolution is not about the death of SEO, but its evolution. Traditional SEO principles, clarity, relevance, authority, still matter. But they must be reframed through the lens of conversations, not keywords. For B2B SaaS companies, this is both a challenge and an opportunity. The challenge lies in adapting fast: rethinking content strategies, investing in structured knowledge, and diversifying presence beyond Google.

With the introduction of AI mode in search engines, which leverages the web using a query fan-out technique to break down search queries into sub-topics, answers are generated with greater relevance and depth. AI-generated content is created by synthesizing information from across the web, drawing on a vast array of sources to provide comprehensive responses. Large Language Models enable AI-powered search to generate original content or summaries by synthesizing information from multiple sources.

The opportunity is clear: those who embrace AI search early will capture disproportionate visibility, shaping buyer perceptions before competitors catch up. The companies that thrive will be those that understand a simple truth: in B2B SaaS, discovery is no longer about being the loudest voice on Google. It's about being the trusted answer wherever buyers ask their questions.

## Measuring Success in Modern Search

In the rapidly changing world of search engine optimization, understanding how to measure success is more important than ever. As search engines evolve and user expectations shift, relying solely on traditional metrics like keyword rankings or organic traffic no longer provides a complete picture. Modern SEO requires a broader approach, one that explores how your content is discovered, cited, and engaged with across a variety of search platforms.

To effectively gauge the impact of your SEO efforts, focus on insights that reflect the true nature of today's search environment. This includes tracking how often your brand or website is referenced in AI-generated search results, monitoring user engagement with your content, and analyzing the visibility of your pages across multiple search engines and conversational platforms. Additionally, consider metrics such as share of voice in industry-specific queries, the quality and relevance of inbound links, and the frequency with which your resources are included in curated lists or directories.

By exploring these modern KPIs, businesses can gain a deeper understanding of their search performance and make data-driven decisions to optimize their strategies. The key is to move beyond surface-level numbers and focus on the insights that truly matter: how users are finding, interacting with, and trusting your content in an increasingly complex digital landscape.

## FAQs

**1. Will SEO still matter in the age of AI search?**

Yes. SEO remains critical, but its focus is evolving. Technical SEO, site performance, and structured content all remain relevant. However, content must be designed to be cited by AI systems, not just ranked by Google.

**2. How can B2B SaaS companies measure success in AI search?**

Beyond traditional rankings, SaaS marketers should track how often their brand is mentioned in AI-generated responses, presence on review platforms, and share of voice in conversational search engines like ChatGPT or Perplexity.

**3. What type of content performs best in AI search?**

AI engines prefer clear, structured, and authoritative content. This includes product comparison tables, API documentation, in-depth guides, customer case studies, and thought leadership that directly answers buyer questions.

---

**Tom Batting**

Tom Batting is a Forbes 30 Under 30 entrepreneur and founder of FirstMotion. Having built and exited multiple ventures, he created FirstMotion to help established B2B software companies stay visible as AI reshapes how buyers search and decide. He writes about GEO, AI search strategy, and turning organic search into a pipeline engine for B2B SaaS brands.

---

# AEO vs SEO: Why B2B Buyers Now Start Their Journey in ChatGPT

Source: https://firstmotion.com/insights/aeo-vs-seo-2025

The shift happened quietly. One quarter you're tracking keyword rankings; the next, your best-fit buyers are describing their problem to ChatGPT and accepting whatever answer comes back, without clicking a single result.

## The new buyer journey

For B2B software, the discovery phase has moved. A Head of Risk at a Series C fintech isn't starting on Google anymore. They're asking an AI assistant to evaluate their options, compare vendors, and shortlist the credible players.

If your company isn't in that answer, you don't exist in that buyer's consideration set. Full stop.

## What AEO actually is

Answer Engine Optimisation is the practice of making your brand, your expertise, and your positioning reliably cited by AI assistants, ChatGPT, Perplexity, Claude, Gemini, when they respond to buyer-intent queries in your space.

It's not about gaming prompts. It's about becoming the kind of source that AI engines trust: well-structured content, authoritative third-party citations, consistent entity presence, and a brand signal strong enough that models learn to associate you with your category.

## The relationship with SEO

Here's what trips people up: AEO doesn't replace SEO. The underlying signals are largely the same, topical authority, backlink quality, content depth, structured data. The difference is **where** they surface.

Good SEO gets you ranked. Good AEO gets you cited. Both matter. A buyer might encounter your brand in a ChatGPT answer, then confirm it with a Google search. Lose either leg and the journey breaks.

## What to do about it

**1. Own your entity.** Your brand needs a clean, consistent presence across Wikipedia, Crunchbase, LinkedIn, and your website. AI models build entity graphs, you want your company firmly in the right one.

**2. Create the content AI answers cite.** Think definitional guides, comparison pages, and benchmark reports. These are the content types that get pulled into AI responses.

**3. Build third-party authority.** G2 reviews, analyst coverage, trade press mentions, these are citations AI engines learn from.

**4. Measure citation rate, not just rankings.** You need tooling that monitors how often your brand appears in AI-generated answers. If you're not measuring it, you can't improve it.

The companies who sort this out in the next 12 months will own category positions that are genuinely hard to displace. The window is open, but it won't be open forever.

---

# What GPT-5 Means for SEO & AI Search (GEO/AEO)

Source: https://firstmotion.com/insights/what-gpt-5-means-for-seo-ai-search-geo-aeo

## Updated March 2026 - What actually happened after GPT-5 launched

This post was written on August 8, 2025 - the day GPT-5 launched - and contained our initial analysis. Now that GPT-5 has been live for several months, we can compare those predictions against what has actually happened.

## What OpenAI confirmed at launch

GPT-5 launched on August 7, 2025. OpenAI stated that responses with web search enabled are approximately 45% less likely to contain a factual error than GPT-4o, and around 80% less likely when using extended thinking mode. OpenAI positioned it as the first model to meaningfully unify reasoning and real-time retrieval in a single system.

## What the traffic data now shows

The zero-click risk predicted in the original post is now measurable. Seer Interactive's November 2025 study of 3,119 informational queries across 42 organisations found organic CTR for queries with AI Overviews dropped 61% (from 1.76% to 0.61%) between June 2024 and September 2025. Even queries without AI Overviews saw a 41% CTR decline - suggesting users are going to ChatGPT and other AI tools before they even reach Google.

## What this means for the original analysis

The predictions in the post below have largely held. The citation upside is real - Seer found that "brands cited in AI Overviews earn 35% higher organic CTR" and 91% higher paid CTR than uncited brands. SE Ranking's December 2025 study of 129,000 domains confirmed that referring domains are the single strongest predictor of ChatGPT citation.

When GPT-4 launched in March 2023, it was the first time many marketers realised that search might not be confined to Google forever. GPT-5, released in August 2025, makes that shift feel permanent.

It is not just a better chatbot – it is a new search layer, with its own retrieval system, ranking logic, and bias towards certain content types.

## How does GPT-5 change SEO and visibility?

OpenAI has improved reasoning, accuracy, and long-context handling in GPT-5, but the more important change for SEO is behind the scenes.

The search experience is faster, citations are cleaner, and sources feel more intentionally selected. It is also more agent like - able to follow instructions across multiple steps - but the most impactful change is how it retrieves and ranks results.

## How GPT-5 search actually works

If you assume GPT-5 is just passing your query to Google or Bing, things are in reality a bit more complex than that.

- GPT-5 almost certainly uses a proprietary meta layer rather than raw SERP feeds go get its results.

- Results seem to sometimes overlap more with Bing results than Google, but whilst also pulling from lots of other sources, re-ranking, changing the order of things etc. So it does not always mirror the search engine results page.

- It appears not to be able to access live Google search results page, but it does seem to be able to access a Bing SERP if provided an exact URL.

Have a read of this [great round up by Josh from Profound](https://www.linkedin.com/feed/update/urn:li:activity:7359403005609140224/) for more technical insights.

## Why GPT-5 matters for SEO

With GPT-5 it seems more apparent than ever that there is not always a link between ranking highly on Google and being visible in AI results.

Optimisation for SEO/AEO/GEO now means thinking about AI search as a standalone channel – one where:

- Authority is measured across multiple engines and ecosystems.

- Content must be easily parsed, summarised, and cited by an LLM.

- Being present on other trusted domains can matter as much as your own site.

- Structured, context-rich content outperforms thin keyword-driven pages.

## The risk of AI search for marketers

Zero click behaviour is likely to intensify. GPT-5's improved answers mean users may never need to visit the source. That puts more pressure on measuring exposure in generative answers, not just traffic/clicks.

It also means you cannot assume that you will be highly visible in AI tools like ChatGPT even if your traditional SEO rankings stay strong – because the ranking logic is not exactly the same.

GPT-5 is not simply a better ChatGPT - it is a more sophisticated search engine in its own right, with a proprietary retrieval and ranking layer. Whilst it's still important to focus on SEO, the mindset shift, approach to measurement and more granular Answer Engine Optimisation/Generative Engine Optimisation approaches are key.

---

# Google says AI in Search is driving higher quality traffic

Source: https://firstmotion.com/insights/google-says-ai-in-search-is-driving-higher-quality-traffic

**Summary**

Google's VP of Search, Liz Reid, says AI in search is producing happier users and higher quality clicks, pushing back on reports of traffic declines. Some in the SEO industry, including FirstMotion, are seeing a different picture in B2B, where AI Overviews and AI Mode are reducing organic clicks for many sites.

Google has [written a new blog post](https://blog.google/products/search/ai-search-driving-more-queries-higher-quality-clicks/), claiming that AI within its search products is resulting in happier users and better quality traffic for website owners.

**Key takeaways**

- Google's Liz Reid says AI in Search is producing happier users and higher quality clicks, not the traffic collapse some reports describe
- Google disputes third-party reports of dramatic traffic declines, calling their methodology flawed
- FirstMotion has seen click drops in a B2B context as AI Overviews and AI Mode reshape the buyer journey
- Reid argues remaining clicks are more valuable, since users click through when they want to dig deeper rather than for basic answers

The post published on Google's search blog is written by Google's VP, Head of Google Search, [Liz Reid](https://www.linkedin.com/in/elizabeth-reid-56356724/). In it, Reid writes that "Our data shows people are happier with the experience and are searching more than ever as they discover what Search can do now."

The post directly addresses "third-party reports that inaccurately suggest dramatic declines in aggregate traffic", claiming that the reports are using flawed methodologies to carry out their analysis, as Reid defends the changes Google has recently made to AI Overviews and more recently [AI Mode](https://firstmotion.com/insights/google-launches-ai-mode-in-the-uk), which many believe are responsible for declines in traffic/clicks.

In a B2B context, we have certainly seen some drops in clicks from Google as AI reshapes the B2B buyer journey. This is hardly surprising. If a user can get the information they need from Google without actually needing to click through to a website - why would they?

Reid acknowledges that some sites may be seeing less traffic, but explains the shifts in search behaviour as follows:

> For many other types of questions, people continue to click through, as they want to dig deeper into a topic, explore further or make a purchase. This is why we see click quality increasing — an AI response might provide the lay of the land, but people click to dive deeper and learn more, and when they do, these clicks are more valuable. **Liz Reid, VP, Head of Google Search**

However, some parts of the SEO industry have reacted with a degree of scepticism. Like us, many SEOs are seeing clicks and traffic dropping as a result of AI in search - particularly [AI Overviews](https://firstmotion.com/insights/is-google-ai-overviews-behind-your-organic-traffic-drop-heres-how-to-diagnose-it), which has changed the shape of the search results page leaving less real estate for organic results.

We continue to crunch data and analyse search behaviour across all of our [B2B SaaS SEO agency](https://firstmotion.com/) clients, and will be sharing some of our own data soon.

**Want to know how AI search is really affecting your traffic?**

We track click quality and AI referral data across our B2B SaaS clients as search keeps shifting. Our ContextualJourney™ platform shows you exactly what's changing in your own traffic before we recommend anything. [Talk to the FirstMotion team](https://firstmotion.com/contact)

## What is Google AI Mode?

Google AI Mode is an experimental search setting that uses Google's cutting-edge machine learning to deliver tailored results and predictions. It continually adapts to your search behavior, aiming to provide faster and more relevant answers whenever you look for information online.

---

# Is AI traffic higher quality & more likely to convert?

Source: https://firstmotion.com/insights/is-ai-traffic-higher-quality-more-likely-to-convert

Is traffic or referrals from AI search tools like Perplexity or ChatGPT higher quality and more likely to convert than traffic from Google? Here's what the data says.

*This article was updated on 18th March 2026*

When this post was written in August 2025, the evidence on AI traffic quality was limited to early studies. There are now multiple primary datasets across different industries and time periods. The direction is consistent: AI referral traffic converts at higher rates than traditional organic for B2B and high-consideration purchases. The effect is weaker or neutral for transactional ecommerce.

## The most robust study: Seer Interactive (B2B software, October 2024 to April 2025)

[Seer Interactive analysed](http://seerinteractive.com/insights/case-study-6-learnings-about-how-traffic-from-chatgpt-converts) GA4 data from a single B2B software client across six AI platforms, tracking 1,370 AI-driven conversions against almost 14 million organic sessions. ChatGPT converted at 15.9%, Perplexity at 10.5%, Claude at 5%, Gemini at 3%, versus Google organic at 1.76%. ChatGPT users viewed 2.3 pages per session on average - nearly double the organic search average of 1.2 - suggesting they arrived mid-funnel, having already researched and compared options inside the AI tool.

## Ahrefs: first-party data showing 23x conversion rate advantage

[Ahrefs published its own internal data](http://ahrefs.com/blog/ai-search-traffic-conversions-ahrefs/) in June 2025: AI search traffic accounted for 0.5% of total visits but drove 12.1% of all new signups - a 23x higher conversion rate than organic search. These users also viewed 50% more pages per visit and had lower bounce rates. This is first-party data from Ahrefs on their own site, using Ahrefs Web Analytics.

## Semrush: broader cross-industry finding

Semrush's July 2025 research found LLM visitors convert 4.4x better than organic search visitors on average. [Their explanation](http://semrush.com/blog/ai-overviews-study/): by the time someone clicks through from a ChatGPT response, the AI has already summarised their options and effectively pre-qualified the visitor. They arrive ready to act, not still browsing.

## Microsoft Advertising: Copilot-driven journeys and lower-funnel conversion

Microsoft Advertising's April 2025 analysis found that Copilot-powered purchase journeys are 33% shorter and 76% more likely to lead to lower-funnel conversions than journeys that do not involve Copilot. This is first-party data from [Microsoft's own advertising platform](http://about.ads.microsoft.com/en-us/blog/post/april-2025/copilot-purchase-journeys).

## Important nuance: the effect varies by sector

Not all AI traffic converts equally. Seer Interactive notes their B2B software finding may not generalise to transactional ecommerce where purchase intent is different. Semrush's cross-industry figure of 4.4x is an average across research-heavy and transactional categories. The conversion advantage is most consistent for B2B, SaaS, professional services, and other high-consideration purchases, the exact markets this blog addresses.

Update: Google have shared their own thoughts on the quality of traffic coming from AI - read more in [our post on what Google says about AI search traffic quality](https://firstmotion.com/insights/google-says-ai-in-search-is-driving-higher-quality-traffic).

The increase in use of AI powered tools like ChatGPT, Perplexity, Claude, and Google's AI Overviews and [AI Mode](https://firstmotion.com/insights/google-launches-ai-mode-in-the-uk) is transforming how users discover content. While traditional SEO has long dominated organic user acquisition strategies, the emergence of AI-driven answers is shifting the focus and putting more attention on quality over volume.

In a world where attention is scarce and zero-click search is on the rise, the big question is no longer "how much traffic?" but "what kind of traffic?"

## Is AI growing it's share of search volume over Google?

We don't believe any brands should be choosing between AI search and Google as if it's a case of having to pick one over the other. [SEO is not dead](https://firstmotion.com/insights/is-seo-dead-a-b2b-marketers-guide-in-2025), and Google is not going anywhere. But it's hard not acknowledge the shifting search behaviour, and some of the important [differences between SEO and GEO](https://firstmotion.com/insights/geo-vs-seo-whats-the-difference).

Research from clickstream data provider Datos recently highlighted that in the United States, the share of users that went to chatbots rather than traditional search engines reached 5.6% in June, up from 2.48% in June 2024 and 1.3% in January 2024. While Google still commands the lion's share of search activity, the growth of AI tools suggests a new class of traffic is emerging.

## Are AI referral traffic conversion rates higher than Google?

Recent studies are starting to uncover a surprising pattern: AI search traffic, while lower in volume, may be significantly higher in quality.

- [Ahrefs](https://ahrefs.com/blog/ai-search-traffic-conversions-ahrefs/) (June 2025) reported that AI search accounted for just 0.5% of their total visits, but drove 12.1% of all new signups. That's a 23x higher conversion rate than organic search.
- These users also viewed 50% more pages per visit and had lower bounce rates, suggesting higher engagement.

In other words, AI referrals might be smaller in number, but they punch well above their weight.

## Platform specific performance: ChatGPT and Perplexity

The picture becomes even clearer when looking at individual AI platforms.

A Seer Interactive case study found:

- ChatGPT accounted for 61% of AI-driven visits, Perplexity ~24%, Gemini ~15%
- AI visitors viewed an average of 2.3 pages/session compared to 1.2 for Google organic
- Engagement rates for AI referrals were on par with organic (~60%) but delivered 100% more attributed conversions year-on-year (Seer Interactive)

These tools appear to act as mid-to-late funnel accelerators: users arrive more qualified, more curious, and more ready to act.

## Does AI SEO traffic convert higher in B2B SaaS?

We love this data study from [Goodie](https://www.higoodie.com/blog/ai-search-vs-traditional-search-b2b-funnel-pipeline#:~:text=That's%20a%2056.3%25%20higher%20close,rate%20of%20traditional%20search%20engines.) showing a 56.3% higher close rate from leads that originated in AI search agents compared to Google or Bing.

Out of all the platforms analysed, ChatGPT was the most efficient B2B traffic source that they identified with nearly double the lead:close rate of traditional search engines.

For B2B marketers, this means vertical context matters. But even modest AI visibility can yield strong ROI if aligned to the right funnel stage. Whilst Google was still leading top of funnel referral traffic, ChatGPT was clearing referring better quality, ready to convert traffic.

## Why do AI referrals convert better?

Several factors may explain the high performance of AI referred traffic over Google referred traffic:

- **LLM tools act as buyer enablement engines**: they answer specific questions aligned with real problems
- **Users arrive further down the funnel**: often in exploration, comparison, or evaluation stages
- **Less noise, more relevance**: AI links are often more direct and intentional than search listings

The result? Higher commercial intent and better conversion efficiency.

## Final thought: Smaller volumes, higher stakes

In the age of [AI native B2B buyer journeys](https://firstmotion.com/insights/how-are-ai-search-tools-like-chatgpt-reshaping-the-b2b-buyer-journey), it's not just about reaching more people - it's about reaching the right ones. B2B SEO has always been about quality over traffic, but at [FirstMotion](https://firstmotion.com/) we think that's more important than ever now.

AI tools may deliver fewer users, but if those users convert at 10x or 20x the rate, the economics shift dramatically. As visibility in these tools becomes more competitive, now is the time to build an AI first visibility strategy that drives revenue, not just rankings.

---

# Google launches AI Mode in the UK - and What Has Changed Since

Source: https://firstmotion.com/insights/google-launches-ai-mode-in-the-uk

## Eight months of data on what the AI Mode rollout has meant

This post was published on the day Google launched AI Mode in the UK in July 2025. Since then, AI Mode has expanded globally and significant data has emerged on its impact on organic traffic, click behaviour, and publisher revenue. Here is what has actually happened.

By October 2025 Google had rolled out AI Mode to over 40 additional countries including Germany, Austria, Spain, Italy, the Netherlands, Poland, and Sweden, adding 38 languages simultaneously. France was excluded due to ongoing regulatory discussions. AI Mode now operates in more than 200 countries and territories.

## The traffic impact on UK businesses

Tank research published in October 2025 analysed 800 UK companies across 16 sectors, using Ahrefs traffic data across 4,800 data points covering three years. Average monthly organic traffic growth dropped from 26.3% to 3.7%, a collapse of 22.6 percentage points. The hospitality sector was hardest hit with a 6.7% decline in monthly organic traffic, compared to 47.9% growth the previous year. The IT sector was most resilient, maintaining 2.1% growth.

## CTR impact confirmed across a larger dataset

Seer Interactive's study of 3,119 informational queries across 42 organisations (June 2024 to September 2025) found organic CTR dropped 61% and paid CTR dropped 68% for queries where AI Overviews appear. Even queries without AI Overviews saw a 41% organic CTR decline, suggesting the broader shift to AI-first search behaviour is reducing clicks independently of AI Overview presence.

## The upside: citations create a compounding advantage

The same Seer Interactive study found that brands cited in AI Overviews earn 35% higher organic CTR and 91% higher paid CTR compared to brands that are not cited. This means AI Mode and AI Overviews are not purely destructive for well-positioned brands, they create a significant performance gap between brands that are cited and those that are not.

Google has officially launched AI Mode in Search for users in the UK and beyond, marking a significant shift in how people interact with search results. [Read the announcement here](https://blog.google/around-the-globe/google-europe/united-kingdom/ai-mode-search-uk/).

## What is Google AI Mode?

Google AI Mode offers a major step forward in how people interact with search engines. Instead of just compiling blue links on a search results page, AI Mode uses advanced artificial intelligence - powered by a version of Google's Gemini 2.5 model - to answer complex, nuanced questions in conversational language and with deeper context.

**Source:** [Google](https://blog.google/around-the-globe/google-europe/united-kingdom/ai-mode-search-uk/)

Key features of Google's AI Mode include:

* A dedicated AI Mode tab appears in both desktop Search and the Google app on Android and iOS, allowing users to opt in easily.
* Users can pose multi-part queries or follow-up questions that would previously have required several searches, like planning a weekend trip, comparing products, or unpacking a complex how-to.
* The system employs a "query fan-out" technique, breaking a big question into subtopics, issuing multiple searches at once, and synthesizing an in depth, tailored answer with links for further exploration.
* AI Mode supports multimodal input: you can ask questions with text, voice, or images, enabling even richer search experiences.
* Early users are asking questions "two or three times the length" of standard search terms, highlighting how AI Mode enables more conversational and exploratory inquiry.
* When AI Mode can't provide a confident answer, it defaults to regular search results -underscoring Google's focus on balancing innovation with accuracy and transparency.

This launch signals a major shift in online search behaviour. For users, it means more natural, fast, and insightful answers, while for publishers and advertisers, it's raising critical questions about visibility and referral traffic, since fewer users may click through to traditional sites.

## What AI Mode means for B2B marketers

Google's rollout confirms that AI native search is here to stay. For B2B brands, this means:

* **Content strategies must evolve**: Winning in generative search means aligning content with buyer questions, topics and tasks, not just keywords.
* **Traditional SEO isn't enough**: You can rank #1 in classic search and still be absent from AI Overviews.
* **Visibility = trust**: If your brand or product isn't cited in AI-generated content, it may not exist in the user's shortlist.

At FirstMotion, we help B2B software companies adapt to this new world of Generative Engine Optimisation (GEO). From prompt mapping to AI visibility audits, we position brands where buying decisions are increasingly being made: inside AI tools.

---

# Is SEO dead? A B2B marketers guide in 2025

Source: https://firstmotion.com/insights/is-seo-dead-a-b2b-marketers-guide-in-2025

Every few years, someone proclaims the death of SEO. In 2025, with AI agents embedded in every browser, document and workspace, the question has resurfaced.

So, is SEO dead?

Short answer: no. But it is evolving.

Today, buyers aren't just using Google. They're turning to AI co-pilots like ChatGPT, Claude, Perplexity, Gemini and others to summarise vendors, compare options, draft RFPs, or answer specific questions about your product category.

These tools are now part of the buying unit. And so we believe that changes how B2B marketers need to think about search.

## Google still matters, but it's no longer alone at the table

Let's start with what hasn't changed:

* Great content still drives engagement.
* Technical health still impacts discoverability.
* Links, internal structure, and clean architecture still matter.

But here's what has changed:

* Google is no longer the only discovery engine.
* AI answers increasingly sit above the fold, summarising multiple sources.
* Some AI tools bypass SEO rankings entirely, surfacing vendors based on inferred trust, structure, and topical relevance.

We've now seen examples where:

* Sites ranking top 5 in Google are missing from Perplexity for some relevant prompts.
* Start ups with little traditional SEO traction and very low domain authority appear in ChatGPT answers.

SEO as a discipline hasn't died. But SEO used to be only about Google. So the context it lives in has fundamentally shifted.

It's worth noting that in Google's second quarter 2025 financial results, they saw advertising revenue [grow to $71.34 billion - up about 10.4% from $64.61 billion the year prior](https://www.cnbc.com/2025/07/23/alphabet-google-q2-earnings.html). So it seems despite AI changing the search results page and the role out of AI mode in the US, Google is still thriving.

## From search engine to co-pilot

B2B buyers are now assisted by AI co pilots embedded in tools they use every day. Microsoft Copilot, Notion AI, ChatGPT, Gemini in Google Workspace - these tools aren't just for search tasks. They're used to:

* Ask early-stage research questions
* Compare vendors by feature or reputation
* Draft outreach emails to suppliers
* Summarise websites, analyst reports or customer reviews

They are acting like virtual team members.

In some cases, they are the first touchpoint with your brand. That means they're not just influencing buying decisions - they are shaping the journey itself.

But in other cases, they may not drive a discovery moment but just play a supporting role instead. It's not about having to pick between 'SEO' and 'GEO' as if they are completely different unrelated things, but it is important to acknowledge some of the differences and stay on top of a brand's visibility.

## Sometimes it's just about accuracy

Even if you don't believe AI search tools are driving discovery moments and tons of new traffic, why would you not want to know how your brand is being talked about, and if the LLM's understanding is accurate?

In the last few months we've seen some interesting examples of errors that needed correcting inside AI tools:

* We asked Perplexity to compare 3 software brands, and our client was incorrectly referenced as not being SOC 2 compliant when it actually is.
* We found one individual review in a Medium post from 6 years ago was being cited by ChatGPT to talk about another client, even though the review was out of date and related to a previous version of their product.
* One of our clients has a brand name similar to another company in an unrelated industry, and ChatGPT was incorrectly confusing them.

In all of these cases, we took steps to make critical corrections that were then reflected inside the respective AI tools.

## It's about balance

It's not about having to pick between SEO or GEO - they are not independent from one another.

But we do believe every B2B software brand should now be considering their SEO and AI search presence side by side. Search is shifting, the Google search results page is changing and in some cases action is needed to really optimise a brand inside AI tools. A search strategy defined in 2025 should consider the full picture.

---

# Do Backlinks Still Matter for AI Search/GEO?

Source: https://firstmotion.com/insights/do-backlinks-still-matter-for-ai-search-geo

**By Alex Price | July 21, 2025**

Backlinks have long been one of the most prized assets in the SEO world - the holy grail of domain authority and playing a big role in rankings. Link building was a key lever to pull when you wanted to climb Google's search results pages.

But AI search and the world of generative engine optimisation has changed things. ChatGPT, Perplexity, Claude, and Google AI Overviews don't display rankings. They synthesise responses. They don't just look at keywords and backlinks - they assess information quality, structure, and context.

So where does that leave backlinks in this new world of Generative Engine Optimisation (GEO) and optimising visibility inside LLMs?

Let's unpack it.

## Backlinks as a trust signal: still relevant, but not decisive

Backlinks are still part of the ecosystem. LLMs like GPT-4 and Claude have been trained on data scraped from the web - and therefore backlinks help shape how that content gets discovered and understood.

Citations still matter too. Tools like Google AI Overviews and Perplexity often reference URLs. High-authority domains tend to appear more frequently in those citations. And yes – strong backlink profiles are still a proxy for trust.

But that trust alone doesn't guarantee inclusion in AI answers.

You could have a page with thousands of backlinks and still not be cited in a single generative response. Because it's not just about popularity - it's about usefulness, and AI models have an ability to align content with user intent more effectively and with less reliance on backlinks as a signal.

## The data: backlinks aren't strongly correlated with AI visibility

A recent study by Seer Interactive looked at which brands are being cited most frequently in AI-generated answers - and what factors contributed to that visibility.

Their finding? "There's little to no correlation between backlink volume and brand mentions in AI answers."

In fact, some brands with modest backlink profiles were regularly referenced. Why? Because they appeared in trusted third-party content – review sites, Reddit threads, comparison pages, and practical how-to guides.

These are the sources LLMs like to pull from. Not just "official" pages or blog posts with a high domain rating, but helpful content with context.

## In the GEO world, not all links are equal

Let's be clear: not all backlinks matter anymore.

What matters now for B2B software brands:

- Natural citations in trusted sources (G2, Gartner, community blogs)
- Brand mentions in third party roundups or comparison content from credible sources
- Mentions in user generated content (Reddit)
- Inclusion in technical documentation or analyst content

GEO is about earning inclusion - it's about citation authority, not chasing a Domain Authority score. And sometimes that can be done without needing to worry too much about backlinks.

## What matters more than backlinks for GEO?

To earn influence in AI-generated answers, focus on:

- **Content clarity** - direct answers, scannable formats, structured layouts
- **Semantic richness** - fully addressing the user intent behind likely prompts
- **Source credibility** - getting referenced by sources LLMs already trust
- **Contextual mentions** - being part of wider industry discussions, reviews, and thought leadership
- **Entity strength** - ensuring your brand and product names are well-associated with key concepts across the web

## So… do backlinks still matter?

Yes - but not in the way most SEO agencies still think.

They're a trust amplifier, not a visibility driver.

They can support your overall domain credibility, but they won't guarantee inclusion in AI answers. That comes from strategic deep audience intelligence, prompt alignment and content structure.

In the GEO world, backlinks are a piece of the puzzle - not the whole picture.

## What we do differently at FirstMotion

At FirstMotion, we don't chase backlinks. We chase buyer influence.

We use our proprietary **ContextualJourney™** platform to map buyer prompts using lots of AI and data enrichment, all built exclusively around B2B software buyer journeys. And we use tools like Peec AI to understand where AI tools are sourcing their answers, and help our clients earn visibility at every stage of the B2B buyer journey.

---

# What results should B2B SaaS companies expect to see from investing in GEO/AI Search?

Source: https://firstmotion.com/insights/what-results-should-b2b-saas-companies-expect-to-see-from-investing-in-geo-ai-search

**By Tom Batting • July 20, 2025**

B2B buyer behaviour is shifting fast - and it's being led by generative AI.

From ChatGPT to Perplexity and Google AI Overviews, buyers are turning to AI tools to research, compare, and shortlist B2B software. This isn't a future trend. It's already happening.

For B2B SaaS brands, this means the traditional playbook of SEO and PPC is no longer enough. If you want to influence the journey, you need to show up inside the tools your buyers are using to guide their research or get help with making decisions.

That's where Generative Engine Optimisation (GEO) comes in.

So, what should you actually expect to see in term of results if you invest in an AI search strategy? Let's break it down.

## 1. Engage buyers earlier in the journey

Buyers aren't starting with Google anymore. They're starting with a prompt in an AI tool like ChatGPT.

Instead of searching "best procurement software," they're asking:

> "What are the top-rated procurement software platforms for large manufacturing companies that integrate with Microsoft and are SOC 2 compliant?"

That's a big shift. GEO helps you identify and show up in these prompts so you can engage buyers at the point of curiosity or before they even know they need a solution - and certainly before they even know your brand name.

## 2. Improve visibility inside AI agents

Most companies still obsess over keyword rankings. But as search shifts, what really matters now is whether you appear inside AI generated answers.

GEO is how you increase your brand's probability of being:

* Cited in a ChatGPT answer
* Referenced in a Perplexity source list
* Included in a Google AI Overview
* Summarised by Claude or another LLM

The companies that are already visible didn't get there by accident. There might be some overlap with the SEO work they have already been doing, or they might be working actively on AI search.

## 3. Understand why your competitors are showing up

Generative engine optimisation gives you clarity on why certain competitors keep appearing in AI tools - and how to reverse-engineer their visibility.

By analysing prompts, citations, and model behaviour, you can understand:

* What types of content are being cited
* Which third party sources are trusted (e.g. G2, Reddit, Gartner)
* How you stack up by persona and prompt cluster

With this intel, you can shape your own AI search and content strategy with confidence.

## 4. Increase pipeline velocity

AI enabled B2B buyers are moving faster because the information they need is readily available.

They're using LLMs to:

* Explore solutions
* Generate vendor checklists
* Compare features side by side
* Draft RFPs
* Analyse customer reviews
* Create evaluation scorecards

By aligning your content to those interactions, you're reducing friction and helping buyers move from research to action. This means fewer surprises in sales conversations and faster consensus across the buying group.

## 5. Reduce dependency on SEO and PPC

GEO doesn't replace SEO or PPC - but it makes your growth more durable. In many cases we believe B2B buyers are using AI alongside Google searches. But we're almost certain that AI assistants are playing some role at some point in the buying journey.

By building visibility inside the answers themselves, you:

* Future proof against declining click through rates
* Lower paid search costs by capturing interest earlier
* Stop chasing keyword rankings that no longer drive ROI

The Search Results Page is changing fast. AI Mode is on the way. It's unclear how paid ads are going to play a role in the future of search or inside AI tools.

## 6. Shift from random acts of content to strategic orchestration

Most content strategies still look like scatterguns.

GEO forces discipline. It helps you:

* Map prompts by buyer persona and journey stage
* Create content with defined strategic roles
* Coordinate content production across marketing, sales and product

This is how you stop creating content for the sake of it, and start creating it in a way which aligns it with real buyer intent.

## 7. Improve alignment across your go-to-market teams

When you go deep into understanding what buyers might be asking AI tools at each buyer journey stage, you can:

* Help sales anticipate and respond to questions
* Inform product of common feature gaps or confusions
* Coordinate marketing messaging across channels

We see AI search supporting the length of the B2B buyer journey - it isn't just a visibility play. It can be a glue between functions.

## 8. Deliver measurable insights to leadership

If you want executive buy in for GEO, it starts with data. If traffic from Google in decline and you don't have a plan - now is the time.

We use tools like Peec AI to help companies:

* Track brand visibility in AI answers
* Benchmark inclusion against competitors
* Monitor prompt-level performance
* Prove influence across the funnel

But even just using Google Search Console might give you some early insights into shifting B2B search behaviour. Generative engine optimisation can give marketing teams the evidence they need to lead in the boardroom.

## 9. Build an AI-first marketing function

GEO is a forcing function for modernisation.

It pushes teams to:

* Rethink how they create and measure content
* Embrace AI-powered research, planning, and publishing
* Collaborate across silos using shared audience intelligence

The result? A future-fit marketing function that can keep pace with how buyers actually behave and is fully AI enabled.

Many SaaS brands are already seeing the "crocodile" in their Search Console – impressions going up, clicks going down.

That's often the result of AI Overviews giving users the information they need without clicking through to a website.

A GEO strategy helps you respond, adapt, and regain influence. And while attribution is imperfect today, it's getting clearer fast. Google, OpenAI, and others will continue improving visibility analytics.

But if you wait for perfect data, your competitors will have already taken the lead.

At [FirstMotion](https://firstmotion.com/), we help enterprise B2B SaaS companies build GEO strategies that:

* Increase AI search visibility
* Map prompts & content strategy to buyer stages
* Connect content to commercial outcomes

If you're ready to get started, we're ready to help.

---

**Tom Batting** is a Forbes 30 Under 30 entrepreneur and founder of FirstMotion. Having built and exited multiple ventures, he created FirstMotion to help established B2B software companies stay visible as AI reshapes how buyers search and decide. He writes about GEO, AI search strategy, and turning organic search into a pipeline engine for B2B SaaS brands.

---

# How to build the marketing business case for investing in AI search / generative engine optimisation

Source: https://firstmotion.com/insights/how-to-build-the-marketing-business-case-for-investing-in-ai-search-generative-engine-optimisation

Need to invest in AI SEO / generative engine optimisation (GEO) but need to build the business case for your Founder, CEO or CFO?

The way buyers search is changing fast. And if you're a B2B marketing leader still relying solely on traditional SEO, you might already be falling behind.

Tools like ChatGPT, Perplexity, and Google AI Overviews are rewriting how B2B buyers discover, explore, and evaluate software. That means your marketing strategy – and budget – needs to adapt.

But how do you make the case for investing in something as new as [Generative Engine Optimisation (GEO)](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care)? How do you balance investment alongside 'traditional SEO', especially when the data is still catching up?

This post gives you a structured way to build that business case and make AI search/GEO a priority in your marketing roadmap.

## 1. Start by reframing the risk

Working on GEO/AI SEO isn't about taking a bet on a new channel.

It's about responding to a structural change in buyer behaviour.

Millions of users are already turning to AI assistants like ChatGPT and Perplexity instead of Google. Gartner predicts that by 2026, traditional search volume could drop by 25%.

Team's need to ask what's the bigger risk?

- Investing early and learning fast?
- Or staying static while buyers migrate to a channel you're invisible in?

## 2. Use lots of data

We're seeing this shift already reflected in real world data, and data is powerful when it comes to building your AI search investment case:

- Studies from Ahrefs, Semrush, TollBit, Similarweb, and others show clicks from Google are dropping
- AI generated answers are taking up more space in the SERPs
- Zero-click behaviour is rising

Meanwhile, tools like Peec AI are beginning to give us real visibility into how often brands appear in ChatGPT and Perplexity answers.

If you're looking for some statistics on the growth of AI post, see our post [here](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google). We also run the largest collated live and updated database of AI search research and studies [here](https://firstmotion.com/insights/live-updated-geo-ai-search-research-reports-studies-database).

## 3. Zoom out and anchor it in buyer journey impact

AI assistants aren't just top of funnel tools and 'visibility'. Visibility is a fluffy metric unlikely to get sign off from a CFO.

AI assistants are becoming B2B buyer enablement co-pilots across the entire buyer journey.

Buyers might be:

- Asking ChatGPT which vendors to shortlist
- Getting instant summaries from Perplexity about product pros and cons
- Using Claude to draft evaluation frameworks and RFPs

This is happening across every stage:

- Problem identification: "Why are our sales cycles getting longer in enterprise SaaS?"
- Solution exploration: "What platforms can automate B2B intent data capture?"
- Requirements building: "What should we ask vendors in a demo of an ABM platform?"
- Supplier evaluation: "Compare 6sense vs Demandbase for large deal ABM."

The business case starts by showing that AI search isn't a new channel – it's now where much of the B2B buyer journey takes place.

Even if buyers are still using Google - there's a good chance that _somewhere_ in the buyer journey, a persona is going to use an AI assistant at some point to guide them, validate their thinking, help with a task or assist with evaluation.

## 4. Highlight how traditional SEO is becoming less reliable

SEO traffic reports might still look healthy - but dig deeper, and you'll often find:

- Rankings are holding, but traffic is falling
- Impressions are rising, but clicks are flat

Why? Because AI Overviews and AI assistants are are giving users the information they need without them ever needing to click through to a website.

AI search is cannibalising traditional SEO results. And unless you're adapting, your returns are diminishing. If you already have signs of this happening in your search console account or SEO data, it's critical to include it in your business case.

Keep an eye out for the so called [crocodile effect](https://www.fastfrigate.com/resources/the-alligator-effect-why-ai-search-cranks-impressions-but-spikes-clicks)!

## 5. Translate GEO investment into revenue outcomes

Don't position GEO as a visibility project. Position it as a revenue growth lever.

Explain how investing in GEO can might be able to help with:

- Influence buyers earlier in the journey
- Build preference before the demo request
- Increase brand recall when it comes to RFPs
- Reduce drop-off by answering objections preemptively
- Speed up the sales cycle

At FirstMotion, we position GEO not just as AI SEO - but as buyer enablement in the AI era.

It might be that you already have some traffic being referred from ChatGPT that you can see in analytics, and you may even be getting conversions - make sure you include any data you have in your business case.

## 6. Link it to your broader marketing strategy

A generative engine optimisation strategy doesn't sit in a silo. It aligns with your existing initiatives and so it's important to tie AI search into the wider marketing mix in your business case:

- **Content marketing**: Repurpose assets into prompt relevant formats
- **Demand generation**: Meet buyers earlier and influence downstream
- **ABM**: Shape personalised AI journeys for your high value accounts
- **Attribution**: Track influence across the '[dark funnel](https://firstmotion.com/insights/how-ai-search-is-making-the-b2b-dark-funnel-even-darker)' using tools like Peec and proxy metrics
- **PPC**: reduce the reliance on paid performance channels, lower CAC longer term
- **SEO**: future proof your search strategy, reducing reliance on declining SEO clicks

This is about evolving your full funnel strategy, not replacing anything.

## 7. How to spot if AI search is already impacting your traffic

You don't need new tools to spot the early signs. Start with Google Search Console.

Look for:

- **Clicks falling while rankings hold steady**: AI Overviews could be taking the traffic
- **Impressions up, clicks down**: The 'crocodile mouth' of AI disruption
- **Brand searches rising with fewer landing page clicks**: Users saw your brand in AI and came later
- **Higher bounce rates**: They got their answer before they got to you

If you're seeing any of these, it's time to act. For more on this, see our post [here](https://firstmotion.com/insights/is-google-ai-overviews-behind-your-organic-traffic-drop-heres-how-to-diagnose-it).

## 8. Build your business case around readiness, not perfection

Your exec team may want to see numbers. That's fair.

But your job is to help them see what's coming, not just what's measurable today.

Frame the case around:

- Competitive advantage - be early, be visible, be cited
- Buyer access - influence more of the journey
- Strategic readiness - test and learn before it's urgent
- Revenue impact - align to pipeline, not just rankings
- AI future proofing - be prepared as AI evolves

---

# How to choose a B2B SaaS GEO/AI SEO agency [with evaluation scorecard download]

Source: https://firstmotion.com/insights/how-to-choose-a-b2b-saas-geo-ai-seo-agency-with-evaluation-scorecard-download

AI assistants like ChatGPT, Perplexity and Google's AI Overviews are reshaping how B2B software buyers discover and evaluate solutions. Traditional SEO agencies aren't always built for this new reality. You need a partner that understands Generative Engine Optimisation (GEO) - and more importantly, understands B2B SaaS, complex buying journeys, and how AI assistants now influence decisions long before anyone reaches your website.

Here's what to look for when choosing a GEO agency with strong B2B SaaS & Software credentials.

**You can download our GEO/AI SEO agency evaluation scorecard here:** [**Download [doc]**](https://drive.google.com/file/d/1lq2g99JsV_KI8qehhYoLpxyBZxf26IGR/view?usp=sharing)

## 1. Proven results in generative visibility and a focus on revenue outcomes

Any agency can show you keyword rankings and traffic graphs. That's not enough anymore.

Look for a partner that can show evidence of visibility in AI generated answers from ChatGPT and Perplexity to Google AI Overviews. Ask to see how they track prompts, citations, and competitor coverage using tools like Peec AI. Ask to understand their methodology, and how they are approaching the process of improving AI search performance.

## 2. A focus on revenue outcomes

But visibility isn't the end goal. You want an agency that connects AI presence to real commercial outcomes: buyer influence earlier in the journey, more qualified leads and stronger pipeline.

AI search is still evolving and attribution sometimes difficult, but you need an agency with the right mindset and that speaks your language.

At FirstMotion, we believe AI search is changing the entire length of the B2B buyer journey - not just initial discovery. It's our goal to prove that AI search can be a revenue driver for B2B software brands, not just a visibility measure.

## 3. A deep understanding of how LLMs actually work

[Generative Engine Optimisation](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) is not just SEO with new keywords. You need a partner that understands:

*   How LLMs cite and reference content
*   The importance of structured data and clean formatting
*   The sources models trust (e.g. G2, Reddit, Gartner, community forums)
*   How prompts vary by buyer role and journey stage

GEO is about earning influence, not just publishing content. AI search is more technical than ever, and so an agency partner should have proven technology credentials.

LLMs are new and the world changing fast - so you can't expect a partner to always have answers. This is no different to SEO and algorithm changes. But you should expect them to always be learning, testing, sharing their insights, and have a clear B2B focused methodology for approaching the AI SEO landscape.

## 4. Fluency in B2B SaaS buyer behaviour

Generic content doesn't win in AI search. The best GEO strategies are built around audience intelligence.

That means:

*   Mapping the decision-making unit, not just personas
*   Understanding long sales cycles and complex buying processes
*   Aligning prompts to buyer roles, not just funnel stages

Your agency should speak your language – ACVs, RFPs, compliance risks, enablement content, analyst briefings. Not just keywords and blog posts.

At FirstMotion, we're unique in having our own ContextualJourney™ AI search audience intelligence platform to help us deeply understand buyer behaviour as we shape AI SEO strategies and [map/align prompts](https://firstmotion.com/insights/what-types-of-ai-prompts-should-b2b-software-companies-be-aiming-to-appear-in).

## 5. Strategic alignment with your marketing goals

AI search doesn't sit in isolation. It feeds into brand, content, demand generation, ABM and revenue marketing.

The right agency won't just deliver tasks. They'll act as an extension of your team, helping you:

*   Prioritise the right prompts based on intent
*   Build content that supports both AI and human journeys
*   Make smart bets based on research and data
*   Translate visibility into buyer influence and pipeline

If they don't understand your goals as a B2B marketer, they can't help you meet them.

## 6. Built for experimentation and rapid change

The AI search space is evolving every week. You need a partner who's:

*   Testing LLM behaviour in real-time
*   Tracking changes to AI visibility across a large number of prompts
*   Monitoring shifts in prompt phrasing and source influence

At FirstMotion, we treat GEO like a living system, not a set-and-forget checklist. We maintain the [largest up to date database of GEO/AI SEO research studies](https://firstmotion.com/insights/live-updated-geo-ai-search-research-reports-studies-database) as well as carrying out our own visibility index reports.

## 7. Modern, AI-first delivery model (not the old agency playbook)

Most SEO agencies are built on bloated account teams, manual reporting, and recycled strategies.

FirstMotion is different. With our founder having previously built and sold a SEO agency in the pre-AI era, we're building FirstMotion differently, firmly believing that the traditional agency model is dead.

We're AI first, lean by design, and powered by our proprietary ContextualJourney™ platform. That means:

*   No layers of account managers slowing you down
*   Leaner, faster delivery
*   A focus on consultancy and client empowerment
*   Real time insight, automation, and buyer contextual strategies

Our goal is to combine consultancy grade thinking with technology grade speed.

## 8. Transparent tracking and reporting

You can't manage what you can't measure. Your agency should:

*   Show you which prompts you're visible for
*   Break down your inclusion in different LLMs
*   Track changes in citation sources and formats
*   Benchmark you against competitors
*   Iterate quickly

We use tools like Peec AI and integrate with your broader analytics stack to give full funnel visibility.

## 9. Proven track record by a B2B SEO leader

FirstMotion was founded by Alex Price, who previously built and sold one of the UK's leading B2B SEO agencies. His agency led enterprise SEO strategies for brands like:

*   Amazon
*   Inmarsat
*   SparkCognition
*   Nexthink
*   Amplitude
*   Vertex
*   Qt
*   SuperAwesome
*   Codat
*   NetDocuments
*   Sitecore
*   Peak AI

FirstMotion's B2B software credentials run deep.

## 10. Ability to help with 'traditional SEO'

We're in a shift at the moment - Google is still the most used search engine, but AI usage and traffic is increasing, and expected to continue to do so.

So it might be important for you to have an agency or consultancy partner that can help with your 'traditional SEO' strategy too. A good AI focused search agency should have its roots in SEO, and therefore be able to assist with both - keeping your agency roster simple, reducing the number of partners you have to manage and making sure SEO and GEO are aligned - which makes a lot of sense given how they overlap in many areas.

## Conclusion: AI SEO is a strategic capability

Choosing the right GEO agency or consultancy is one of the most important decisions a B2B SaaS brand will make over the next 12 months. It's the difference between showing up in the next wave of AI-native buying journeys.

**You can download our GEO/AI SEO agency evaluation scorecard here:** [**Download [doc]**](https://drive.google.com/file/d/1lq2g99JsV_KI8qehhYoLpxyBZxf26IGR/view?usp=sharing)

At [FirstMotion](https://firstmotion.com/), we help B2B software companies turn buyer understanding into prompt strategy, prompt strategy into AI visibility, and AI visibility into pipeline.

---

# What's the role of a B2B website in a zero click world?

Source: https://firstmotion.com/insights/whats-the-role-of-a-b2b-website-in-a-zero-click-world

**By Alex Price • July 17, 2025 • 3 min read**

In the old world of B2B marketing, your website was the main event. Buyers Googled their way to your homepage, clicked through carefully structured landing pages, filled out forms, and got nurtured through the funnel.

But that playbook is collapsing.

It seems we're heading for the zero click (or at least the 'very few click') era - a world where perhaps lots of the buyer journey takes place without a single visit to your site. Where AI assistants like ChatGPT, Perplexity, and Google's AI Overviews act as the primary research layer. Where answers are synthesised and surfaced directly in the AI tools your buyers are already using.

So if your website isn't the starting point anymore, what is it?

Maybe it's your final impression.

## Your website is no longer the beginning – it's the handshake

By the time someone reaches your homepage, they may already have:

*   Asked ChatGPT for a shortlist of solutions
*   Read reviews on G2 or Capterra
*   Compared features in Perplexity
*   Evaluated you through Reddit or LinkedIn threads
*   Checked analyst commentary in Gartner or Forrester

All or lots of this information may have been served up to them without ever having left the chat with their AI assistant of choice. It gets them everything they need and delivers it to them in one centralised location where they can continue to interrogate it, clarify things, ask for more follow information etc. It's clear that this is a much improved research experience for the buyer when compared to the old world of opening lots of sites, adding them to a spreadsheet, manually taking notes and so on.

In other words: they've done their homework without you. Now they're looking for a final confirmation. A spark of confidence. A moment of clarity. An emotional hook.

## Your site now serves two audiences: humans & machines

In the AI-powered B2B software buyer journey, your website has a split role:

*   For AI assistants: provide clear, structured, citable content. That means:
    *   Semantic markup (schema, FAQs, definitions)
    *   Declarative answers (not marketing fluff)
    *   Author pages, citations, source clarity
    *   Explicit product details, integrations, and positioning
*   For humans: deliver a differentiated, memorable experience. That means:
    *   Clear navigation and messaging hierarchy
    *   Standout brand impact, creative and UX design
    *   Memorable experience
    *   Visual trust signals (logos, quotes, stats, awards)
    *   Emotional resonance and storytelling

If you only focus on one audience, you'll lose the other. You need both.

## Creativity is no longer optional - it's your edge

When every category starts to feel the same in AI answers, your brand becomes the differentiator.

If a buyer lands on your site after already seeing summaries of your competitors, your job is to break the pattern.

*   Surprise them with bold messaging
*   Use interaction, motion, and emotion
*   Tell stories, not just specs
*   Make it memorable enough to stick

This isn't fluff - it's conversion science for a new age of research and reference.

## So what now? A creative renaissance for B2B websites?

As AI search continues to grow and a greater amount of the B2B buyer journey takes place without a website visit, perhaps it's time to refocus your website as mainly serving the last mile of the buyer journey.

But this shift isn't something to fear. It's an opportunity.

If the AI assistant handles the facts, your website can finally focus on what AI can't do: make people feel something.

At FirstMotion, we think this could be the beginning of a creative renaissance in B2B SaaS website design. A chance to break free from the rinse-and-repeat grid of safe, samey websites. A powerful chance to stand out.

When the functional job of 'providing information' (whatever that might mean) is already handled by ChatGPT or Perplexity, your site can go beyond clarity. It can be bold. Emotional. Artful. A true expression of your brand's personality, not just its product.

This doesn't mean sacrificing usability - it means pairing substance with soul. Being clear and courageous. If buyers already have the facts, your job is to leave an impression they'll remember.

The AI age doesn't kill creativity. It demands it.

Your website's moment isn't gone - it might just come later in the journey. And when it comes, it had better hit hard.

---

## About the Author

**Alex Price** is the founder of FirstMotion and FINITE. He grew a digital agency from sole founder to 35-person team with £multi-million revenues before selling at age 29 to a US-headquartered strategic buyer.

---

# How are AI search tools like ChatGPT reshaping the B2B buyer journey?

Source: https://firstmotion.com/insights/how-are-ai-search-tools-like-chatgpt-reshaping-the-b2b-buyer-journey

The B2B software buyer journey has changed. And not just a little bit. Thanks to AI search tools like ChatGPT, Perplexity, Claude and Google AI Overviews, it's being fundamentally rewritten.

For years, marketers optimised for traditional search engines by aligning content with keywords. But AI search doesn't behave like Google. There are no search result pages with ten blue links, no fixed rankings, and no linear journey.

And that changes everything. Welcome to the new world of AI native B2B SaaS & software buyer journeys.

## The emergence of AI search in B2B research

AI tools are being used across the B2B buying journey as buyer enablement co-pilots, answer engines, and recommendation engines.

- Users are bypassing Google to ask ChatGPT directly for recommendations
- Google's own AI Overviews are pushing traditional organic listings further down the page
- Models are citing trusted sources, not necessarily the brands themselves

In short, there are now entire buying journeys happening that brands have much reduced visibility of. This is the [new B2B dark funnel](https://firstmotion.com/insights/how-ai-search-is-making-the-b2b-dark-funnel-even-darker).

At FirstMotion, we see AI tools as being *inside* the B2B decision making unit. They are part of the buying unit, providing guidance, counsel, advice and active assistance as B2B buyers navigate their buying journey.

## How AI assistants shift behaviour at each journey stage

AI might not always be changing what buyers search for, but it's certainly changing how they search.

**Problem identification**

Buyers are turning to AI assistants to articulate challenges, validate pain points, or explore peer experiences:
- "As a CISO at a mid-sized fintech company expanding into the EU, what are the common third-party risk challenges I should be aware of going into 2025?"
- "How do in-house legal teams at high-growth SaaS companies typically manage visibility and control over contract renewals across departments without relying on shared drives or email threads?"

**Solution exploration**

Buyers use LLMs to map the landscape of solutions based on their specific context:
- "What tools are used by growth-stage cybersecurity companies to automate ongoing SaaS vendor risk assessments while maintaining ISO 27001 compliance?"
- "Compare Ironclad, LinkSquares, ContractPodAi, and SpotDraft specifically for legal teams in B2B SaaS companies with 200–500 employees that need Google Workspace and Salesforce integrations."

**Requirements building**

LLMs help buyers define the scope and specifics of what they actually need:
- "What features should a legal team prioritise in CLM software if we're aiming to automate fallback clauses, track negotiation workflows, and ensure version control across sales and procurement?"
- "We're preparing an RFP for an enterprise DAM platform – what technical requirements, integration capabilities, and user access controls should we specify for a distributed marketing team operating across three global regions?"

**Supplier evaluation**

Buyers are now getting multi-dimensional comparisons based on their role, goals and constraints:
- "As the VP of Legal in a late-stage tech company preparing for IPO, which CLM platform is rated highest for rapid deployment, scalability, and audit readiness – LinkSquares, Ironclad, or ContractWorks?"
- "What do in-house counsel teams in 500+ employee SaaS companies typically say in reviews about the quality of implementation support and ease of adoption when comparing Ironclad to SpotDraft?"

It's important to notice that these prompts are longer, more contextual, and more reflective of real decision making than 'SEO keywords' ever were.

**Read more:** [What AI prompts should B2B software brands optimise for?](https://firstmotion.com/insights/what-types-of-ai-prompts-should-b2b-software-companies-be-aiming-to-appear-in)

## AI tools aren't just providing information

AI assistants like ChatGPT are no longer just search engines in disguise. They're not simply surfacing content or summarising articles - they're actively doing the work sometimes.

In B2B marketing, this shift is subtle but profound.

Buyers aren't just asking for lists of tools or pros and cons anymore. They're using AI to create the artefacts that drive purchase decisions:
- Researching how other similar businesses might be solving similar problems
- Creating business cases for investing in a new solution
- Drafting full RFPs tailored to internal requirements
- Building vendor comparison frameworks with weighted criteria
- Generating scoring models for evaluating product demos
- Creating checklists for compliance or technical due diligence
- Drafting internal summaries for board or budget approval

This means the assistant isn't just part of the research phase - it's shaping the actual decision making process.

If your content, product positioning, and proof points aren't being picked up, understood, and incorporated by these tools, you're not just missing traffic - you're missing influence at the most critical moment.

B2B marketers now need to think beyond visibility. You need to ask:

"Is my brand showing up in the outputs buyers are taking into meetings?"

That's a very different game. And winning it starts with understanding buyer behaviour and ensuring your information is findable, credible, and structurally usable by AI.

## Why audience intelligence is more important than ever

Because buyers are using AI assistants like humans - in plain English, in long form, with detail - your prompt strategy is only as strong as your understanding of the buyer.

At FirstMotion, we use our ContextualJourney™ technology platform to:
- Map real companies and their buying units
- Identify individual personas and their needs, pain points, triggers etc
- Enrich the ICP & persona data with AI & various sources such as review data, analyst research
- Align likely prompts across each buyer journey stage
- Generate prompts that may be used across each stage of the buyer journey

Prompt strategy without buyer context is guesswork. We don't do guesswork.

## Implications for B2B content strategy

This shift in buyer behaviour, and the role AI tools are playing across the full length of the B2B buyer journey, demands a new B2B SEO/content strategy approach:
- Answer full questions, not just keywords
- Provide structured, scannable content that LLMs can parse
- Focus on semantic depth over SEO fluff
- Align all content with ICP, persona & buyer stage
- Think about usable assets that can assist the buyer journey - checklists, templates, spreadsheets etc

If ChatGPT is building your buyer's shortlist, your content needs to shape the answer.

## Why visibility tracking is now critical

With AI search, you can't just rely on Google Analytics to tell you what's happening.

We use tools like Peec AI to:
- Monitor how often your brand appears in AI answers
- Track changes in inclusion over time
- Compare against competitors
- Spot which prompts are driving the most visibility
- Understand the sources of influence and content that LLMs reference

[Generative engine optimisation](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) isn't about a specific ranking position - it's about the probability of being visible - and that's a big shift in mindset for lots of B2B marketers.

## What B2B SaaS marketers need to do

To keep up with the shifting buyer journey, B2B software marketers should:
- Map their ICPs, personas, and journey stages
- Identify and map prompts at each stage
- Audit prompt visibility using specialist tools
- Create content aligned with buyer needs and language
- Optimise for influence, not just presence
- Monitor and iterate continuously

The AI search revolution isn't coming. It's already here.

B2B buyers are researching, evaluating, and shortlisting using AI assistants as their one source of truth and buyer enablement co-pilot - from 'first prompt' through to 'closed won'. If your strategy doesn't adapt, your brand risks becoming invisible.

At [FirstMotion](https://firstmotion.com/services/ai-search-optimisation), we help enterprise B2B SaaS & software brands navigate the shift from SEO to the new world of AI native B2B buyer journeys. Check out our recent post if you need help building your [business case to invest in AI search](https://firstmotion.com/insights/how-to-build-the-marketing-business-case-for-investing-in-ai-search-generative-engine-optimisation).

---

# Account Based Marketing (ABM) in the Age of AI Search

Source: https://firstmotion.com/insights/account-based-marketing-abm-in-the-age-of-ai-search

**By Alex Price | July 15, 2025**

Account based marketing (ABM) and generative engine optimisation (GEO) might feel like two different worlds - one rooted in outbound orchestration, the other in AI-powered inbound discovery. But maybe they are more alike than they seem.

Both are grounded in one principle: relevance to the right buyer, at the right moment.

As AI search tools like ChatGPT, Perplexity and Claude begin shaping B2B buying journeys earlier than ever, it's time to rethink how ABM and GEO can work together - not in parallel, but in concert.

## ABM and GEO share the same DNA

ABM is about going deep, not broad. You tailor your messaging, content, and channels to a defined set of target accounts - and within them, the key decision-makers and influencers.

Our enterprise B2B approach to AI search works the same way. But instead of targeting people with ads and outreach, you're targeting the prompts those people are putting into AI assistants.

Done right, GEO becomes the _passive twin_ of ABM - shaping buyer perceptions before your first outbound email is ever opened.

## Your key accounts are using AI – you just can't see it

Let's say you're targeting legal teams at mid sized SaaS companies as part of an ABM campaign for your CLM software.

You might run ads. You might push content. You might trigger outbound SDR sequences.

But what if the buyer journey started a week earlier - in a conversation with ChatGPT?

> "What CLM tools are best for mid-sized SaaS companies with lean legal teams and basic Salesforce integration needs?"

If your brand shows up in the answer, you may already be winning the perception battle.

If it doesn't, you've already lost ground and you'll never see it in your attribution data.

This is the new B2B dark funnel - and it's where GEO lives.

## From account lists to prompt matrices

At FirstMotion, we don't just help clients run GEO campaigns - we help them consider how AI search is playing a role across the length of the B2B buyer journey. If a client has a ABM program underway, we use that data to help shape our AI SEO strategy.

Here's how:

1. **Start with your ABM ICPs** - who are you targeting, what industries, what job roles?
2. **Map their buyer personas** - especially for high-value decision-makers and influencers.
3. **Define their journey stages** - we typically use:
   - Problem identification
   - Solution exploration
   - Requirements building
   - Supplier evaluation
4. **Create prompt matrices** – what questions might those buyers ask at each stage?

This prompt matrix becomes the connective tissue between ABM and GEO.

Luckily, at FirstMotion we have ContextualJourney™ - our own AI SEO audience intelligence platform that helps us to enrich company information, understand buyer pain points, goals and triggers, and to therefore build a unique understanding of what prompts B2B buyers might be using across their purchase journey.

## Track AI prompt visibility as an ABM signal

Using AI visibility tools like Peec AI, we can track:

- Is your brand being mentioned in AI answers for prompts aligned to your ABM accounts?
- Are competitors being surfaced instead?
- What sources are the AI tools citing?

Whilst we don't know exactly what prompts ABM accounts are using, we think there is value in considering the generative search intent layer that inevitably is sitting over your ABM strategy.

## GEO improves ABM targeting and messaging

The insights from prompt mining and source analysis aren't just for SEO or content teams.

They can directly improve:

- **Sales messaging** - align with buyer language and objections uncovered in prompts
- **Ad copy** - reflect the specific pain points buyers are asking AI about
- **Landing pages** - structure content in ways LLMs can parse and reference
- **Content strategy** - fill in prompt gaps and citation opportunities

If you're running ABM for your enterprise B2B brand and you're not yet thinking about how AI search is influencing your buyers, we think it's time to start.

We're not saying GEO replaces ABM - we're saying it makes sense to consider how the two can align, given your buyers inside your ABM accounts are turning to AI tools for advice and decision support.

---

## About the Author

**Alex Price**

I dropped out of university to turn my part time freelance business that I started as a teenager in my bedroom into an award winning digital agency. I grew the business organically, with no debt or investment, from just me as a 20 year old sole founder to a team of ~35 people and multi-million £ annual revenues and ~25% net profit margins - winning clients like Amazon. I also founded FINITE, a B2B marketing media brand and global membership community for software CMOs. In April 2022, aged 29, I sold the business to a US headquartered strategic buyer, realising the value of lots of hard work and achieving a life changing outcome.

---

# Enterprise B2B SaaS: How to map prompts for AI search/generative engine optimisation

Source: https://firstmotion.com/insights/enterprise-b2b-how-to-map-prompts-for-ai-search-generative-engine-optimisation

**By Tom Batting • July 14, 2025**

How can enterprise B2B marketers transition from SEO keywords to prompts that are aligned with their buyer journeys to drive AI search/GEO results?

The rules of B2B SaaS SEO have changed thanks to AI - forever.

If you're still thinking in terms of SEO keywords, you're already behind. In a world of ChatGPT, Perplexity and Google's AI Overviews, enterprise buyers are asking long, contextual questions. And the only way to show up in generative answers is to understand those prompts - and the buyers behind them - better than anyone else.

This post breaks down how we approach prompt mapping at FirstMotion, why audience intelligence is the foundation of every AI search strategy, and how our ContextualJourney™ technology platform supports this work.

## It starts with knowing your buyer - properly

Most marketers say they understand their ICP. But generative engine optimisation requires much more than a one pager on personas. You need to understand:

- The full buying unit - legal, finance, IT, procurement, ops
- The decision-making dynamics - blockers, influencers, champions
- Their internal language - not just industry buzzwords, but how they describe their own pain
- Their triggers - compliance risk, M&A, cost pressures, platform consolidation
- Their journey stages - and how questions evolve as they move from initial problem to final evaluation

Without this, you're not mapping prompts - you're just guessing. And whilst there is a lot of talk about 'fan out' when it comes to understanding search behaviour, we don't think this is enough.

## Keywords vs prompts: what changes in a GEO world

Keywords are transactional. Prompts are contextual.

Here's what that looks like in practice:

| Old SEO Keyword | Prompt for AI search |
| --- | --- |
| "contract management software" | "What contract management tools do mid sized SaaS companies use to automate legal review workflows and ensure audit trails across finance and procurement?" |

That difference matters. Because LLMs are not ranking links – they're synthesising answers. They're choosing sources based on relevance, clarity, and structure. The more precisely your content matches the buyer's context, the higher the probability it gets included.

There are no rankings in GEO – only probabilities.

## Use data to shape your prompt map

You can't rely on intuition alone. We use:

- **Intent data** from platforms like Bombora to detect signals
- **Review mining** from G2, TrustRadius, Reddit and Quora to extract buyer language
- **Persona enrichment** using tools like Clearbit and Apollo to understand role-specific needs
- **Conversation mining** from sales calls or Gong recordings (when available)
- **Market research** such as surveys, industry reports, market insights etc

This gives us real buyer behaviour, not just assumptions.

At FirstMotion, we're unique in having our ContextualJourney™ platform to bring all of this together - mapping ICPs, personas, buyer triggers and stages, and using that data to generate prompt hypotheses - all tailored to enterprise B2B software & SaaS buying.

## The B2B Prompt Matrix

We use a simple but powerful model: the Prompt Matrix.

It aligns prompts across:

- Each persona in the buying unit
- Buyer journey stage

Here's an example for a General Counsel persona evaluating CLM software. There are our standard 4 enterprise buyer journey stages, but we customise these from client to client.

| Buyer stage | Prompt |
| --- | --- |
| Problem Identification | "How do GCs in mid-sized companies manage contract version control without relying on email and shared drives?" |
| Solution Exploration | "Compare Ironclad and LinkSquares for legal first contract workflows and integration with Salesforce." |
| Requirements Building | "What should a legal team prioritise in a CLM system if they need redline automation, audit logs, and internal clause libraries?" |
| Supplier Evaluation | "Which CLM provider has better legal team satisfaction scores and reviews – Ironclad or ContractWorks?" |

Notice the prompts are not generic. They're loaded with role specific context, operational challenges, and decision making intent.

## Why prompt mapping is the foundation of GEO

Everything else in an AI search optimisation strategy flows from this.

- **Visibility monitoring** (e.g. using Peec AI) only matters if you're tracking prompts that align to your buyer
- **Influence intelligence** depends on knowing which prompts to analyse for citations
- **Content strategy** should be built around prompt clusters, not just SEO topics

If you're not understanding your audience and mapping prompts, you're not optimising. You're just hoping.

## Precision beats volume in B2B

In enterprise B2B, buyers don't search like consumers. They ask detailed, situation specific questions. And increasingly, they're asking them in AI tools as they support the full length of AI native B2B buying journeys.

Generative engine optimisation success doesn't come from chasing generic traffic, it comes from anticipating the exact prompts your most valuable buyers are entering into AI assistants across their journey.

That's why we build everything on top of deep audience intelligence. It's the only way to move from content marketing and keyword targeting to true buyer enablement in the age of AI.

---

**Tom Batting** is a Forbes 30 Under 30 entrepreneur and founder of FirstMotion. Having built and exited multiple ventures, he created FirstMotion to help established B2B software companies stay visible as AI reshapes how buyers search and decide. He writes about GEO, AI search strategy, and turning organic search into a pipeline engine for B2B SaaS brands.

---

# GEO/AEO/AI Search SEO Studies & Research Database (Live & Updated)

Source: https://firstmotion.com/insights/live-updated-geo-ai-search-research-reports-studies-database

*This article was updated on 18th March 2026*

Welcome to our live and regularly updated database of GEO & AI SEO research - where we track, collate and share all of the latest original data led studies and insights from GEO experts into the evolving and fast moving field of GEO/AI SEO (AIO/LLMO/AEO).

If you have research you'd like to submit to be added below, please [share it with us here](https://firstmotion.com/contact). Note that we don't share opinion pieces, blog posts or how-tos - we only share original, data led research.

## Updated: GEO & AI Search Research, Studies & Insights (Chronological Order)

**Last Updated: 18th March 2026**

### Does Ranking Higher on Google Mean You’ll Get Cited in AI Overviews?

**Author:** Ahrefs | **Publish Date:** 21/07/2025

Ranking higher on Google increases your chances of being cited in Google’s AI Overviews, but it’s not guaranteed. Ahrefs found a strong correlation - about 50% of #1 ranking pages are cited, but many AI cited sources don’t rank in the top 10 at all. Other factors like content freshness, specificity, and alignment with AI prompts also influence citations. SEO fundamentals still matter, but optimising for AI visibility requires a broader strategy. Since Google Search Console doesn’t show AI citations, tools like Ahrefs' Brand Radar are essential. Success now depends on balancing traditional SEO with AI focused content and monitoring tools.

**View**: [Click to open](https://ahrefs.com/blog/does-ranking-higher-on-google-mean-youll-get-cited-in-ai-overviews/)

### AI Overviews Cite AI-Generated Content More Than Human Writing

**Author:** Ahrefs | **Publish Date:** 14/07/2025

Ahrefs analysed 38,425 URLs cited in Google’s AI Overviews and found that 48.1% contained detectable AI-generated content, while only 26.8% were primarily human written. A further 25.1% were mixed or undetermined. This suggests Google’s AI Overviews are significantly more likely to cite AI generated pages than human authored ones.

Additional findings:
- Pages written with tools like ChatGPT, Claude, and Gemini were overrepresented in citations.
- Content created by human writers using tools like Grammarly or Jasper had lower citation rates.
- AI generated content was most frequently cited in categories like tech, health, and how-to queries.
- Sites with high domain authority were still more likely to be cited overall, regardless of content origin.

Ahrefs concludes that content origin (AI vs human) now plays a role in AI visibility, but not in the way many expect. AI written content, when aligned with searcher intent and structurally clear, is thriving in Google's AI outputs.

**View**: [Click to open](https://ahrefs.com/blog/ai-overviews-cite-ai-generated-content-more-than-human-writing/)

### AI Overview Analysis & Study of 118M Searches: July 2025

**Author:** Conductor | **Publish Date:** 10/07/2025

Conductor’s July 2025 AI Overview study analysed 118 million real search keywords to track how Google’s AI-generated overviews (AIOs) are transforming search results. The report found that 18% of all tracked keywords now trigger an AI Overview - a 29% increase since May and a 112% jump since April. Desktop devices account for 61% of AIOs, with mobile presence stabilizing at 37.5%. Industries most impacted include IT Services (38% of keywords trigger AIOs), Healthcare Equipment & Supplies (36%), Life Sciences Tools & Services (36%), Education Services (35%), and Biotechnology (34%). The largest growth was seen in Healthcare Equipment & Supplies (+24 points since April). Most AIO-triggered searches are informational or conversational. The U.S. leads globally, but international AIO presence is expanding rapidly. The study emphasises the need for brands to optimize content for AI-driven search, as AIOs increasingly dominate visibility across sectors.

**View**: [Click to open](https://www.conductor.com/academy/ai-overviews-analysis/)

### 700+ Google Search Console Results Showing Better Rankings But Lower CTRs

**Author:** Lily Ray / Amsive | **Publish Date:** 10/07/2025

Lily Ray, Vice President, SEO Strategy & Research at Amsive, shares data showing her analysis of a number of Google Seach Console accounts (over 700) from the last 3 months (May - July 2025), year over year. In the screenshot she shares, she shows a large number of sites which have seen an increase in rankings on Google SERPs, but a decrease in CTR as a result of AI search and overviews.

**View**: [Click to open](https://www.linkedin.com/feed/update/urn:li:activity:7348766610083450880/)

### Does Being Mentioned on Highly Linked Pages Influence AI Mentions?

**Author:** Ahrefs | **Publish Date:** 08/07/2025

Patrick Stox explores whether mentions on well-linked pages (high referring‑domain count) correlate with AI assistant visibility. Analysing ~76.7 million Google AI Overviews, 957 k ChatGPT prompts, and 953 k Perplexity prompts for June 2025, he calculated Spearman correlations between brand mentions and visibility. Results: Google AI Overviews showed a strong correlation (ρ = 0.70), Perplexity a moderate one (0.40), while ChatGPT had a very weak link (0.12). The study suggests that being cited on popular, highly credible sites boosts visibility in Google’s AI feature, whereas other AI systems are less influenced. The author cautions that correlation doesn’t equal causation and indicates that larger-scale studies will follow.

**View**: [Click to open](https://ahrefs.com/blog/does-being-mentioned-on-highly-linked-pages-influence-ai-mentions/)

### Google Seems More Biased Towards Big Brands Than ChatGPT and Perplexity

**Author:** Ahrefs | **Publish Date:** 07/07/2025

Continuing the theme of brand visibility, this study examines whether the volume of branded web mentions predicts AI visibility. Again using data from Brand Radar across Google AI Overviews, Perplexity, and ChatGPT, it found a strong correlation between mentions and Google’s AI visibility (ρ = 0.65), but much weaker signals for Perplexity (0.30) and ChatGPT (0.15). The implication is that Google’s AI Overviews favour established brands, likely to combat misinformation, while the other systems show less bias. It reinforces Google's longstanding preference for brand trustworthiness as a signal in its AI output.

**View**: [Click to open](https://ahrefs.com/blog/branded-web-mentions-visibility-ai-search/)

### AI Mode vs Google Search: The Referral Gap

**Author:** Garrett Sussman / iPullRank | **Publish Date:** 04/07/2025

Garrett Sussman shares early insights on Google’s AI Mode using Similarweb data from 100,000 searches (May 20 – June 19). The findings reveal a major drop in clickthrough behaviour: only 5% of AI Mode searches lead to an external site click, compared to 25% in traditional Google Search. However, the number of clicks per session is nearly identical (6.0 for Google, 5.9 for AI Mode), suggesting users who do click may still engage meaningfully. Sussman cautions that friction (extra steps in AI Mode UI) and novelty (users still learning how to use the feature) may be skewing behaviour. He emphasises that this is early-stage data and not yet indicative of long-term patterns, but warns that if AI Mode becomes the default within 6–12 months, the industry must start preparing now.

**View**: [Click to open](https://www.linkedin.com/posts/garrettsussman_new-data-early-ai-mode-data-courtesy-of-activity-7346580273779683328-l1MQ/)

### Does Being Mentioned on High Traffic Pages Influence AI Mentions? 

**Author:** Ahrefs | **Publish Date:** 03/07/2025

In this study, "web visibility" is defined as the total organic traffic to pages mentioning a brand. Analysing the same AI data set as prior articles, researchers assessed correlations between web visibility and AI mentions. Findings revealed a moderate correlation for Google AI Overviews (ρ = 0.55), weak correlation for Perplexity (ρ = 0.35), and very weak correlation for ChatGPT (ρ = 0.20). This suggests that brands featured on high-traffic pages increase the likelihood of being cited by Google’s AI summarisation tools, underscoring the value of content visibility and distribution for brand recognition in AI search landscapes.

**View**: [Click to open](https://ahrefs.com/blog/does-being-mentioned-on-high-traffic-pages-influence-ai-mentions/)

### AI Traffic Has Increased 9.7x in the Past Year

**Author:** Ahrefs | **Publish Date:** 26/06/2025

Ahrefs updates its March 2024 study with data from 81,947 sites, revealing that average AI-driven search traffic has surged roughly 10‑fold while traditional search traffic dropped by ~21%. AI referrals now account for about 0.25% of total site traffic on average - still small, but rapidly growing. Interestingly, AI is now Ahrefs’ highest‑converting channel, delivering over 10% conversion rate. While metrics lump AI and regular search together in analytics tools, the study confirms a major shift: AI-overview features and modes are increasingly influencing traffic distribution. The drop in traditional clicks is tied to zero‑click AI summaries, and there's a call to revisit analytics to better distinguish referral sources and track AI traffic more accurately.

**View**: [Click to open](https://ahrefs.com/blog/ai-traffic-increase/)

### AI Search Currently Drives Less Than 1% of Traffic To Most Sites

**Author:** G-Squared Interactive | **Publish Date:** 25/06/2025

Glenn Gabe analyses AI search traffic using Similarweb clickstream data, comparing it to traditional Google organic traffic. He finds that AI tools like Perplexity and ChatGPT (with SearchGPT) are still driving far less traffic than Google, but their growth is notable - especially for high-ranking, authoritative content. In some cases, Perplexity drives thousands of monthly visits. Key takeaways: Perplexity is more likely to drive direct clicks than ChatGPT (which often references but doesn’t link), and visibility in AI answers doesn’t always translate to traffic. Gabe stresses the importance of branded search terms, featured content, and domain authority to increase AI visibility. While traffic volumes are currently small, trends suggest growing AI influence - and marketers should begin optimising now.

**View**: [Click to open](https://www.gsqi.com/marketing-blog/ai-search-traffic-compared-to-google/)

### AI Search Intent Study: What 50M+ ChatGPT Prompts Reveal

**Author:** Profound | **Publish Date:** 25/06/2025

Analysing over 50 million ChatGPT prompts, Profound found a sharp drop in informational intents—from around 52% to just 32%. In contrast, transactional and navigation intents have grown, signalling that users now expect ChatGPT to help with tasks—not just deliver information. This “intent shift” challenges traditional SEO strategies, which have emphasised informative content. Profound suggests content creators now need to focus on practical utility—tools, templates, checklists—that fit this new behaviour pattern.

**View**: [Click to open](https://www.tryprofound.com/blog/chatgpt-intent-landmark-study)

### AI Visitors Visit Fewer Pages and Bounce More Often Than Traditional Search Visitors

**Author:** Ahrefs | **Publish Date:** 24/06/2025

Drawing from the same 81,947‑site dataset, this analysis compares AI-source visits (via ChatGPT, Perplexity, etc.) with traditional search traffic. Findings show AI users visit fewer pages (4 vs 5.2 for search) and engage less per session (session duration divided by pages visited = 2.27 for AI vs 2.79 for search). They also bounce more frequently, indicating shallower browsing. The study suggests these visitors are likely seeking targeted answers rather than exploring a site broadly—highlighting a shift in user behaviour that site owners should acknowledge when assessing traffic quality from conversational AI referrals.

**View**: [Click to open](https://ahrefs.com/blog/ai-traffic-quality-study/)

### The New Normal

**Author:** Kevin Indig | **Publish Date:** 17/06/2025

Kevin Indig explores how AI is reshaping search metrics and strategies, with interesting predictive data on when ChatGPT might overtake Google Search based on modelling various growth rates. He advocates a 360° approach: adjusting to AI-driven query behaviour, evolving measurement frameworks, and closely monitoring emerging KPIs. The memo emphasises preparing for AI Mode’s eventual mainstream rollout and the need to redefine success metrics - away from clicks toward user satisfaction and task completion.

**View**: [Click to open](https://www.growth-memo.com/p/the-new-normal)

### Does AI Search Traffic Convert Better Than Traditional Search? For Ahrefs, Yes: 0.5% of Visitors Drove 12.1% of Signups

**Author:** Ahrefs | **Publish Date:** 16/06/2025

Ahrefs reports that AI search visitors convert 23× better than traditional search visitors: 12.1% of Ahrefs signups stem from AI traffic, despite it only representing about 0.5% of visits. AI-sourced users go through 50% more pages and have lower bounce rates, but spend less overall time onsite. These signals point to higher purchase or signup intent—AI users seem to come more ready to act. The authors caution, though, this trend may not scale linearly as AI becomes more common, and analytics attribution remains imprecise.

**View**: [Click to open](https://ahrefs.com/blog/ai-search-traffic-conversions-ahrefs/)

### 80% of Our AI Search Traffic Goes to Our Homepage, Product Pages, and Free Tools

**Author:** Ahrefs | **Publish Date:** 16/06/2025

This analysis of Ahrefs Web Analytics (30‑day timeframe) reveals 80% of AI-sourced visits land on high-intent pages: free tools (36.5%), product pages (23.1%), and the homepage (20.4%). This contrasts with domain advice focused on informational content—AI assistants disproportionately direct users toward conversion-focused or branded pages. A small percentage (~3.6%) even lead to non-existent “hallucinated” pages, mainly from ChatGPT. While “best-of” content and guides still attract AI traffic, brands should optimise high-intent pages for AI visibility. The study also encourages monitoring misdirected AI traffic and setting up redirects for hallucinated URLs.

**View**: [Click to open](https://ahrefs.com/blog/ai-search-traffic-by-page-type-ahrefs/)

### 86% of Top Mentioned Sources Are Not Shared Across ChatGPT, Perplexity, and AI Overviews

**Author:** Ahrefs | **Publish Date:** 12/06/2025

Ahrefs Brand Radar analysed ~76.7M Google AI Overviews, 957k ChatGPT prompts, and 953k Perplexity prompts for June 2025. It found striking divergence in citation sources: only 7 out of the top 50 domains were common to all three platforms—just 14%. Preferences differ by assistant: Google AI leans heavily on authoritative and user-generated sites (Wikipedia, YouTube, Reddit), ChatGPT cites publishers and news outlets, and Perplexity draws from regional and niche sources. This highlights platform-specific algorithmic biases and emphasizes that SEO optimisation should tailor for multiple AI ecosystems, not just Google.

**View**: [Click to open](https://ahrefs.com/blog/top-mentioned-sources-are-not-shared-across-ai-assistants/)

### AI Scraping Is On The Rise. TollBit State of the Bots - Q1 2025

**Author:** Tollbit | **Publish Date:** 11/06/2025

TollBit’s updated Q1 2025 report (following on from its Q4 2024 report) shows AI assistant traffic rose 39.8% quarter-over-quarter, now making up 4.7% of total traffic across 2,752 publisher sites. ChatGPT led with 55.3% of identifiable assistant visits, followed by Perplexity (22.7%) and Claude (6.9%). News and health sites continue to see the highest AI-driven engagement. The report also highlights a sharp increase in “shadow AI traffic” - bots with hidden or no user-agent strings - accounting for 62% of all assistant traffic, up from 49% in Q4 2024. This growth signals increasing AI content scraping without attribution or monetisation. TollBit again urges publishers to recognise AI as a traffic source requiring visibility, governance, and monetisation strategies.

**View**: [Click to open](https://tollbit.com/bots/25q1/)

### The 10 Most Mentioned Domains for ChatGPT, Perplexity, and AI Overviews Across 78.6M Searches

**Author:** Ahrefs | **Publish Date:** 11/06/2025

Ahrefs used its Brand Radar dataset (~76.7 M Google AI Oversees, 957 k ChatGPT, 953 k Perplexity prompts) to identify the top 10 domains most frequently cited by AI assistants. Wikipedia leads overall—16.3% in ChatGPT, 12.5% in Perplexity, and 8.4% in Google AI Oversees—while YouTube ranks high in Perplexity (16.1%) and Oversees (9.5%) but is absent in ChatGPT. Google favours user-generated content (Reddit, Quora) in Oversees (7.4% and 3.6%), whereas ChatGPT emphasises news outlets like Reuters, AP, and AS.com. Impression/potential reach analysis, weighted by search volume, shows institutional and medical sources like Mayo Clinic also hold considerable visibility in Oversees. The findings underline how each AI assistant follows a distinct content citation bias.

**View**: [Click to open](https://ahrefs.com/blog/top-10-most-cited-domains-ai-assistants/)

### We Studied the Impact of AI Search on SEO Traffic. Here’s What We Learned.

**Author:** Semrush | **Publish Date:** 09/06/2025

Semrush examined over 500 SEO and digital marketing query topics to project how AI search will affect traffic and revenue. Their model indicates that by early 2028, AI-sourced visits could surpass traditional search visits for such topics—potentially sooner if Google’s AI Mode becomes the default. This trend suggests AI search's rapid ascension—with significant implications for industry traffic patterns and optimisation strategies.

**View**: [Click to open](https://www.semrush.com/blog/ai-search-seo-traffic-study/)

### Semrush AI Overviews Study: What 2025 SEO Data Tells Us About Google’s Search Shift

**Author:** Semrush | **Publish Date:** 05/05/2025

Semrush analysed over 10 million keywords (January–March 2025) to assess the growing prevalence of AI Overviews in SERPs. The share of queries triggering AI Overviews doubled from 6.49% in January to 13.14% in March. These features predominantly appear on informational queries (88.1%), and navigational triggers also doubled. Sector-wise, topics like science (+22.3%), health (+20.3%), people & society (+18.8%), and law & government (+15.2%) saw the highest increases. Surprisingly, zero-click rates for the same keywords decreased slightly after AI Overviews were introduced—suggesting users may still click through after reading a summary. The key takeaway: AI Overviews are reshaping search—marketers must optimise for them to maintain visibility.

**View**: [Click to open](http://semrush.com/blog/semrush-ai-overviews-study/)

### AI Overviews Reduce Clicks by 34.5%

**Author:** Ahrefs | **Publish Date:** 17/04/2025

Analysing 300K informational keywords, this Ahrefs study compared CTRs from March 2024 (pre-AI Overviews) and March 2025. Position‑one CTR dropped from 5.6% to 3.1% for those without AI Overviews, while AI Overview-triggering keywords saw CTR fall even more dramatically—from 7.3% to 2.6%. This amounts to an estimated 34.5% CTR reduction attributed directly to AI Overviews. The mechanism resembles Featured Snippets, providing answers directly in the SERP and decreasing traditional link clicks. Despite Google's assertion that links within Overviews get more traction, Ahrefs notes that current tools cannot differentiate these click types. The study warns that as AI summaries become more routine, passive "zero-click" searches will likely increase, further impacting organic traffic.

**View**: [Click to open](https://ahrefs.com/blog/ai-overviews-reduce-clicks/)

### Google AI Overviews: New CTR Study Reveals How to Navigate Negative SERP Impact

**Author:** Amsive | **Publish Date:** 16/04/2025

This study analysed 700K keywords across five industries and found that the introduction of Google’s AI-generated Overviews has significantly disrupted clickthrough rates. On average, CTR dropped by 15.5%, with non-branded (–19.98%) and lower-ranked keywords (–27.04%) hit hardest. Overlapping Featured Snippets combined with AI Overviews led to an even steeper CTR decline of 37%. Interestingly, branded queries that did trigger an AI Overview saw a CTR increase of 18.7%, suggesting brand credibility can offset visibility loss. The research recommends adapting SEO strategy: aim for top positions but also focus on securing Featured Snippets, optimise for high-intent non-branded queries, and double down on branded content. The takeaway is to recalibrate your SEO playbook for an AI-dominated SERP environment.

**View**: [Click to open](https://www.amsive.com/insights/seo/google-ai-overviews-new-research-reveals-how-to-navigate-click-drop-off/)

### Does Brand Awareness Impact LLM Visibility?

**Author:** Seer Interactive | **Publish Date:** 16/04/2025

Seer Interactive analysed correlations between brand mention volume (MSV) and LLM visibility. Overall correlation was modest (ρ ≈ 0.18), second only to Domain Rank (ρ ≈ 0.25). In high-trust verticals like finance, brand awareness appears to meaningfully improve LLM mentions. The conclusion: awareness contributes to LLM visibility, but only in tandem with strong domain authority, backlinks, and credible content. For industries reliant on trust, awareness-building campaigns—PR, expert engagement, publisher mentions—are recommended to boost AI visibility.

**View**: [Click to open](https://www.seerinteractive.com/insights/does-brand-awareness-impact-llm-visibility)

### Marketing’s New Middleman: AI Agents

**Author:** Bain | **Publish Date:** 14/04/2025

Bain & Company argues that AI agents are fast becoming influential intermediaries between brands and buyers, shifting how consumers and businesses make decisions. These agents don’t just provide information - they make or narrow down choices. This has major implications for marketers, particularly in B2B and high-consideration consumer sectors. Key data and insights include: 28% of consumers have already used generative AI to assist in purchasing decisions. Among these, 70% say it improved decision quality and 63% say it saved time. Bain predicts AI agents will soon dominate early discovery and evaluation stages of the buyer journey.

**View**: [Click to open](https://www.bain.com/insights/marketings-new-middleman-ai-agents/)

### AI Scraping Is On The Rise. TollBit State of the Bots - Q4 2024

**Author:** Tollbit | **Publish Date:** 24/02/2025

TollBit’s Q4 2024 report analyses 295 million visits across 2,420 publisher sites to track how LLMs and bots interact with web content. The headline finding: traffic from AI assistants and bots increased by 17.2% quarter-over-quarter, now accounting for 3.4% of total traffic. ChatGPT, Perplexity, and Claude led the charge, with ChatGPT visits up 20.7%. News publishers and health sites saw the highest share of AI traffic, with Perplexity disproportionately favouring health content. The report also notes a rise in unidentified bot traffic (up 30.4%), suggesting growing use of non-transparent agents. TollBit emphasises that most of this AI traffic is unmonetised—publishers receive no revenue despite their content powering AI outputs. To address this, TollBit promotes its tooling to help publishers identify AI agents, measure content usage, and control access. The report urges media companies to begin treating AI traffic like a commercial channel and to prepare monetisation strategies accordingly.

**View**: [Click to open](https://tollbit.com/bots/24q4/)

### 87% of SearchGPT Citations Match Bing’s Top Results

**Author:** Seer Interactive | **Publish Date:** 06/02/2025

Seer Interactive found that in SearchGPT (ChatGPT with live search), 87% of citations align with Bing’s top 20 organic results, with many from the first page. In contrast, only around 56% match Google’s top results. This suggests that Bing’s SERP landscape heavily influences ChatGPT’s web citations. The study advises SEO to diversify efforts—tracking Bing alongside Google—and consider partnerships with external trusted publishers.

**View**: [Click to open](https://www.seerinteractive.com/insights/87-percent-of-searchgpt-citations-match-bings-top-results)

### Google Triggers 100% More AI Overviews for Longer Queries, New Report from BrightEdge Finds

**Author:** Brightedge | **Publish Date:** 30/01/2025

From September to December 2024, the proportion of long-tail queries (eight or more words) triggering AI Overviews doubled, showing Google’s increasing confidence in answering complex questions with AI. Approximately 25% of those longer queries now generate an AI Overview. BrightEdge highlights Google’s ability to handle nuance at scale, and implies SEO strategies must now cater to longer, more conversational queries.

**View**: [Click to open](https://www.brightedge.com/news/press-releases/google-triggers-100-more-ai-overviews-longer-queries-new-report-brightedge-finds)

### Marketing Leaders Want to Meet AI Search Head-On: New Survey Results

**Author:** Botify | **Publish Date:** 28/01/2025

Botify’s January 2025 survey found marketing leaders recognise the urgency of integrating AI search strategies. Organisations are prioritising structured data, metadata automation, AI-ready indexing, and cross-platform tracking (Google, Bing, ChatGPT). Key initiatives include SmartIndex (real-time AI-friendly indexing), SmartContent (AI-enriched content generation), and SmartLink (automated internal linking), showcasing a strategic shift to support visibility across both traditional and AI search.

**View**: [Click to open](https://www.botify.com/blog/marketing-leaders-want-to-meet-ai-search-head-on-new-survey-results)

### STUDY: What Drives Brand Mentions in AI Answers?

**Author:** Seer Interactive | **Publish Date:** 07/01/2025

Seer analysed 10,000 LLM-generated brand-recommendation prompts (mainly finance and SaaS). Page‑1 Google rankings had the strongest correlation with brand mentions (~0.65), followed by Bing (~0.5–0.6). Surprisingly, backlinks and multimedia content had little impact. After filtering out aggregators and forums, correlation strengthened, reinforcing the importance of rankings and PR/partnership strategies. The study suggests that while SERP prominence matters, brands should also invest in PR and partnerships to boost LLM mention likelihood.

**View**: [Click to open](https://www.seerinteractive.com/insights/what-drives-brand-mentions-in-ai-answers)

### New Report From .Trends & Statista Reveals How AI Search is Changing the Web

**Author:** Semrush | **Publish Date:** 02/12/2024

This report provided early market insights, highlighting that as of July 2024, ChatGPT and Google’s Gemini dominate AI search traffic—capturing around 78% between them, with Perplexity and Bing composing the rest. It also noted approximately 13 million US adults had already adopted generative AI as their primary search tool, with projections reaching 90 million by 2027. It confirms AI search isn’t niche—it’s becoming mainstream, demanding adaptation from marketers and content creators.

**View**: [Click to open](https://www.semrush.com/blog/ai-search-report/)

### We Studied 200,000 AI Overviews: Here's What We Learned

**Author:** Semrush | **Publish Date:** 30/10/2024

Focusing on the structure of Google AI Overviews, Semrush analysed how many top‑10 organic URLs appear in AI Overviews. They found low overlap: over 80% of mobile AI Overviews include three or fewer top‑10 results and only 46% of desktop and 34% of mobile Overviews included the #1 organic result. Ads rarely overlap with AI‑shown URLs. This suggests AI Overviews use different selection criteria, meaning high organic rank doesn't ensure an AI citation. Brands have a chance to feature in Overviews even if they aren’t top in classic SEO—communicate expertise clearly to be surfaced by these AI summaries.

**View**: [Click to open](https://www.semrush.com/blog/ai-overviews-study/)

### AI Overviews Study: Inside Google's New Search Reality

**Author:** Botify / DemandSphere | **Publish Date:** 01/10/2024

Botify’s Q4 2024 report, based on 120 000 SERPs, shows AI Overviews appearing in up to 47% of searches and occupying 75.7% of mobile viewport space when paired with Featured Snippets. Most AI Overview citations come from top‑12 organic rankings, with strong semantic alignment between page content and AI summaries. The study calls for optimising for ranking and content similarity to increase AI Overview placement.

**View**: [Click to open](https://lp.botify.com/q4.2024-aio-report)

### New Research From BrightEdge Finds Google's AI Overviews Are Getting Smarter

**Author:** Brightedge | **Publish Date:** 19/09/2024

BrightEdge’s research reveals Google’s AI Overviews are evolving: they now lean heavily on specialised expert sources, comparative shopping content, and visual modules like carousels. This signals more discerning source selection and richer formats. Additionally, Google’s SearchGPT referral growth is outpacing competitors—underlining that businesses need to target both search ranking and source authority to capture AI visibility.

**View**: [Click to open](https://www.brightedge.com/news/press-releases/new-research-brightedge-finds-googles-ai-overviews-are-getting-smarter)

### AI Overviews: Impact on Google CTR

**Author:** Seer Interactive | **Publish Date:** 04/11/2025

Organic CTR for queries with AI Overviews dropped 61% (from 1.76% to 0.61%). Paid CTR dropped 68% (from 19.7% to 6.34%). Even queries without AI Overviews saw organic CTR fall 41%, suggesting broader behavioural change beyond AI Overview presence alone. Brands cited in AI Overviews earned 35% higher organic CTR and 91% higher paid CTR than non-cited brands.

**View**: [Click to open](https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update)

### What Really Drives ChatGPT Citations

**Author:** SE Ranking | **Publish Date:** 12/2025

Referring domains are the single strongest predictor of ChatGPT citation. Around 2,500 referring domains correlate with 1.6 to 1.8 citations. Sites with more than 350,000 referring domains average 8.4 citations. Domains active on Trustpilot, G2, Capterra, and Yelp earn 3x more citations than those without profiles. Pages with First Contentful Paint under 0.4 seconds average 6.7 citations versus 2.1 for slower pages. Content updated within the past three months averages 6 citations versus 3.6 for untouched content. LLMs.txt files showed negligible impact.

**View**: [Click to open](https://www.searchenginejournal.com/new-data-top-factors-influencing-chatgpt-citations/561954/)

### AI Overviews and AI Mode Citation Overlap

**Author:** Ahrefs | **Publish Date:** 02/2026

AI Overviews and Google AI Mode cite different sources: only 13.7% of citations overlap between the two features. YouTube mentions and branded web mentions are the top factors correlating with AI brand visibility across ChatGPT, AI Mode, and AI Overviews. AI Overview content changes 70% of the time for the same query, and when it generates a new answer 45.5% of citations are replaced.

**View**: [Click to open](https://ahrefs.com/blog/ai-overviews-reduce-clicks-update/)

### How Users Interact with Google AI Overviews

**Author:** Pew Research Center | **Publish Date:** 07/2025

Click-through rate drops from 15% to 8% when an AI Overview is present. Only 1% of searches lead to users clicking a link within an AI Overview. Users end their search session 26% more often when an AI answer appears, compared to 16% for results pages without AI Overviews.

**View**: [Click to open](https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/)

### Zero-Click Search and Google Traffic Growth

**Author:** Pew Research Center | **Publish Date:** 07/2025

Click-through rate drops from 15% to 8% when an AI Overview is present. Only 1% of searches lead to users clicking a link within an AI Overview. Users end their search session 26% more often when an AI answer appears, compared to 16% for results pages without AI Overviews.

**View**: [Click to open](https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/)

### Most Cited Domains in AI (3-Month Study)

**Author:** SemRush | **Publish Date:** 10/11/2025

ChatGPT cited Reddit in nearly 60% of responses in early August before collapsing to around 10% by mid-September 2025, coinciding with a Google search parameter change. AI Mode consistently cited LinkedIn in nearly 15% of its responses. Forbes doubled its ChatGPT citation rate after September 2025. LLM visitors convert 4.4x better than organic search visitors (also from Semrush July 2025 research, 90% of ChatGPT cited pages rank at position 21 or lower in traditional search).

**View**: [Click to open](https://www.semrush.com/blog/most-cited-domains-ai/)

---

# Is Google AI Overviews behind your organic traffic drop? Here's how to diagnose it

Source: https://firstmotion.com/insights/is-google-ai-overviews-behind-your-organic-traffic-drop-heres-how-to-diagnose-it

If you've noticed your organic traffic dropping recently – even though your SEO performance looks fine – you're not alone. In fact, we're seeing this across multiple B2B software brands right now.

The scary part? Traditional SEO metrics might tell you everything's improving. You're climbing rankings. Visibility is up. But clicks are falling off a cliff.

So what's going on?

## Google AI overviews are cannibalising your clicks

Google's AI Overviews are now occupying serious real estate at the top of many search result pages - and appearing for more and more searches. They summarise answers to user queries before anyone even gets to the blue links. Which means:

- Even if you rank #1, you're further down the page under the AI Overview
- The answer is already there, users don't need to click
- Your content is fuelling the overview, but you're not getting the visit

In short: in certain cases you may be being used but not rewarded.

## Users are moving to AI assistants for search

In lots of cases, users are increasingly skipping Google altogether.

**Read our** [**stats on the rise of AI search over Google here**](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google)**.**

ChatGPT, Perplexity, Claude - these tools are fast becoming the default research assistants for B2B buyers. They're asking prompts, not typing keywords. They're getting citations and brand names straight from the LLM's mouth – no links, no visits, no traceable click path. AI tools are changing the B2B buyer journey, acting as buyer enablement co-pilots across the journey.

## How to diagnose what if AI Overviews is causing the traffic drop

Here's how to investigate whether AI search is to blame for your organic traffic fall.

## Step 1: Head to Google Search Console

In GSC, go to the **Performance** section.

Google recently confirmed that AI Overviews data is now included in total impressions and clicks – but it's not filterable (yet).

So here's what to look for:

- If visibility (impressions) is flat or increasing…
- But clicks are falling…
- That's a strong signal that Google's AI Overviews are satisfying the query before users reach your site.

A classic symptom: rising visibility, plummeting click through rate.

## Step 2: Analyse Specific Queries

Focus on your key commercial and high converting queries.

Look for:

- No change in ranking position (it may still say '1', but the entire SERP has shifted down because of AI overviews)
- Decline in clicks with no ranking drop
- Higher impressions but flat engagement

These are red flags that the SERP layout has changed – and you've been pushed out by AI.

## Step 3: Watch for Brand Mentions with No Attribution

LLMs like ChatGPT often mention brand names without including a link.

This creates ['dark' branded search behaviour](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google): users hear about you in AI responses, then Google your brand later or go direct.

To check this:

- Look in GSC for rising branded queries even as generic queries decline
- Compare your brand name traffic against your SEO targeted pages

If branded searches are up but overall organic traffic is down, the LLM dark funnel could be at work.

## Step 4: Audit Your AI Visibility

Take your most important keywords or content themes, and translate them into prompts that buyers might use mapped onto stages of their buyer journey.

Then:

- Use a tool like Peec AI to check if your content is being cited in ChatGPT, Perplexity or Google AI Overviews
- Compare your visibility to key competitors
- Note which content shows up in AI answers vs what performs in Google

This is where the gap becomes obvious.

## Summary: your AI search visibility checklist

If you can answer yes to three or more of the below, it's time to take [GEO](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) seriously:

- Organic traffic down, but rankings or impressions stable
- Drop in CTR across top queries
- Google Search Console shows higher impressions, fewer clicks
- Spike in branded searches despite drop in generic traffic
- You don't appear in AI answers for key prompts

---

# Is it really possible to be a GEO AI search agency expert?

Source: https://firstmotion.com/insights/is-it-really-possible-to-be-a-geo-ai-search-expert

We think so - and we think FirstMotion are GEO experts. But for different reasons than you may think.

Generative Engine Optimisation (GEO) is brand new territory. We're at the very beginning of understanding how tools like ChatGPT, Perplexity, Claude, and Google AI Overviews surface, cite, and synthesise information.

## The GEO landscape is shifting fast

The pace of change in [AI search](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) is unlike anything we've seen before in digital marketing. One week, Perplexity quietly launches a new model. The next, ChatGPT gets real-time browsing powered by Bing. Then Perplexity launches its own browser, and OpenAI announces the same. Meanwhile, Google's AI Overviews are causing traffic chaos for brands who are seeing organic clicks steeply in decline. There are lots of [statistics showing just how much AI search is changing things](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google).

Even if someone was a 'GEO expert' yesterday, a model update tomorrow could instantly make their assumptions irrelevant. But that's not dissimilar to how it's always been with SEO too.

So expertise in GEO doesn't come from knowing everything. Being a [GEO expert agency](https://firstmotion.com/services/ai-search-optimisation) comes from knowing how to learn fast when everything changes.

## SEO was never certain either

This isn't a new challenge.

In the world of 'traditional' SEO, one day you're ranking #1. The next, an algorithm update wipes causes lots of disruption.

We've always known that SEO success came from experimentation, ethical optimisation, and trying to stay ahead of the curve. GEO is no different.

In fact, we would argue 'SEO expert' has always been misleading. It was never about mastering the rules, it was about mastering the process of adapting to them.

## GEO is harder to measure - for now

In some respects we're flying blind when it comes to GEO.

There's no Search Console for LLMs. No robust attribution tracking. No clear visibility into how people found you via ChatGPT or Perplexity, especially when your brand gets mentioned without a link.

That's why analytics tools like Peec AI are so valuable (and why we've partnered with them at FirstMotion). But even they're tracking probabilities of being visible, not specific rankings. This is a new kind of visibility and we need new tools, new frameworks, and new ways of thinking to make sense of it.

Will OpenAI release better analytics for LLM driven search? Almost like a Google Search Console equivalent for AI? Our bet would be yes. But until then, we need to stay evidence based and methodical.

Here's our recent post on why the [B2B dark funnel is darker than ever because of AI](https://firstmotion.com/insights/how-ai-search-is-making-the-b2b-dark-funnel-even-darker) right now.

## GEO isn't a skillset - it's a cross-disciplinary strategy

The truth is, no single person can own GEO.

It cuts across product marketing, demand generation, content strategy, PR, analyst relations, SEO, social media and data.

- You need **deep audience intelligence** to map prompts across the buyer journey.
- You need **semantic content structuring** to make your material citable.
- You need **offsite influence** on trusted sources like G2, Reddit, and Gartner.
- You need **visibility tracking** that can't be found in your CMS or GA4.

GEO isn't a vertical. It's a layer across your entire go-to-market strategy. That's why at [FirstMotion](https://firstmotion.com/) we focus on the AI native B2B buyer journey, not just AI search being a visibility measure.

## FirstMotion: GEO & AI SEO expert agency

We position ourselves as a specialist, leading and expert generative engine optimisation & AI search agency, because of how deeply we're researching in the space and how methodical our approach is.

We promise every client that comes on a journey with us that:

- Every recommendation is backed by research, not guesswork.
- Every framework is tested across real buyer journeys and ICPs.
- Every insight we share is part of our commitment to learning in public.

We've built our Contextual Journey™ platform because we believe GEO strategy must start with enriched audience and buyer intelligence. Not prompt guessing.

If you want to keep up to date with all the latest GEO research, studies and data driven insights, check out our [GEO research database](https://firstmotion.com/insights/live-updated-geo-ai-search-research-reports-studies-database).

**Read more**: [how to pick a B2B SaaS focused GEO / AI search agency](https://firstmotion.com/insights/how-to-choose-a-b2b-saas-geo-ai-seo-agency-with-evaluation-scorecard-download)

---

# Generative Engine Optimisation & AI Search: Your questions answered

Source: https://firstmotion.com/insights/generative-engine-optimisation-ai-search-your-questions-answered

## Generative Engine Optimisation & AI Search: Your questions answered

Frequently asked questions about Generative Engine Optimisation and how to improve visibility inside AI search engines.

The world of search is changing - fast and forever. In fact, much of the change has already happened.

If you're a B2B marketer still focused solely on Google rankings, you're missing the new front lines of visibility: AI search assistants like ChatGPT, Perplexity, Claude and Google AI Overviews along with Gemini.

We call it GEO (Generative Engine Optimisation) and it's changing how B2B buyers discover, research, and evaluate software.

This post tackles the most common (and important) questions we hear from marketers trying to get their heads around GEO. No fluff, no hype, just sharp answers grounded in what's happening now.

## What actually is GEO?

Generative Engine Optimisation (GEO) or AI Search is the process of improving your visibility, influence, and presence inside AI powered search tools.

That includes tools like:

* ChatGPT with browsing enabled
* Perplexity (which combines LLMs with real-time search)
* Claude, Gemini, and more
* Google's Search Generative Experience (SGE)

Unlike traditional SEO, where keyword volumes were easy to measure and rankings were static and predictable, GEO is probabilistic - every prompt gets a slightly different answer, pulled from different sources, depending on how it's phrased and who's asking.

We believe in a B2B marketing context, GEO is:

* Prompt driven, not keyword driven
* Built for LLMs, not legacy search engines
* A process of buyer journey orchestration across the full length of the B2B buyer journey, not just a measure of brand visibility

## Is it called GEO or AIO or AEO or LLMO?

Marketers and agencies (let's be honest, mainly agencies) are throwing all sorts of acronyms around:

* GEO = Generative Engine Optimisation
* AIO = AI Optimisation
* LLMO = Large Language Model Optimisation
* AEO = Answer Engine Optimisation (typically used in voice/search)

We use GEO because it reflects a broader truth: search has shifted to generative engines. It's not about optimising for one tool. It's about understanding how AI systems are generating answers, and making sure you're present when they do.

We also feel GEO is a nice natural next iteration of SEO.

## What is a GEO agency?

Think of a GEO agency like the next generation of a SEO agency. A GEO agency should help you:

* Understand how your buyers are using AI search assistants
* Track your brand's visibility across generative tools
* Map prompts to buyer journey stages
* Influence the sources LLMs pull from
* Build content strategies optimised for prompt driven journeys
* Monitor and adapt visibility over time

At FirstMotion, we specialise in B2B software. Our methodology is built around deep audience intelligence and buyer context, prompt mining, and influence intelligence - not just guessing prompts and writing blog posts.

## How does GEO differ from SEO?

At a foundational level, SEO was about rankings. GEO is about probabilities.

In SEO, you aimed to get on page one of Google. Ideally position number one. In GEO, there's no single ranking - just the likelihood of your brand or content being surfaced in response to a prompt.

GEO is more dynamic, more contextual, and more dependent on structured data, semantic language, and trust signals from third party sources.

That said, there are plenty of areas of overlap. In some verticals we've seen strong correlation between 'traditional SEO performance' and AI search performance. And some of the 'tactics' we use to improve a brand's visibility and performance in GEO will be similar to SEO.

It's not about one or the other - it's about understanding your customers, the tools they use to search, discover and evaluate, and shaping strategies to influence their journeys in the right way and the right time.

## How does AI search or GEO change the B2B buyer journey?

It compresses it. Blurs it. Personalises it. Makes it harder to measure. But also presents lots of opportunities for B2B software brands to accelerate the sales cycle, influence it earlier and turn AI search into a channel that drives growth.

B2B buyers now use ChatGPT and Perplexity as buyer enablement co-pilots - not just top of funnel discovery tools.

GEO doesn't just influence top of funnel. It influences:

* How buyers shortlist vendors
* What features they evaluate
* What reviews or use cases they see
* What integrations or differentiators get mentioned
* What questions they ask vendors
* How they evaluate and make a final decision

Our own framework, PromptPath™, aligns GEO strategy to the full buyer journey:

1. Problem Identification
2. Solution Exploration
3. Requirements Building
4. Supplier Evaluation

And we then map ICPs, personas and prompts to every B2B buyer journey stage using our ContextualJourney™ technology platform.

## What are the best analytics tools for measuring AI visibility?

Right now, the most useful tool in our stack is Peec AI which is a purpose built analytics tool for AI prompt visibility tracking across ChatGPT, Perplexity, Google AI Overviews and other platforms.

We also use our own ContextualJourney™ platform for the audience intelligence, prompt mining and some of the content strategy parts of our work and to help us reverse engineer generative engine visibility success for our clients.

GEO visibility is harder to measure than SEO - but it's possible, it's essential, and it should be happening consistently.

## What's the most important part of a GEO strategy?

Without question, our perspective at FirstMotion is that it's audience intelligence.

You can't optimise for prompts if you don't deeply understand your ICPs, personas, and buyer journey, along with some strong competitor analysis.

At FirstMotion, our ContextualJourney™ platform uses millions of B2B buyer data points, enrichment, intent data and AI to:

* Deeply understand B2B buyer pain points and goals
* Building buying units
* Understand the buyer journey
* Translates those insights into likely AI prompts (searches)
* Builds a Prompt Matrix mapped across journey stages

No prompt strategy is complete without deep, enriched buyer context - and we don't believe it's possible to define a winning AI search content strategy without deep audience intelligence.

## Will AI tools like ChatGPT eventually overtake Google?

Maybe not entirely. But in many B2B software categories, they're already the first stop - especially for:

* Problem framing
* Tool comparison
* Feature evaluation
* Writing RFPs or spec sheets
* Identifying possible risks/red flags
* Asking what to ask on a demo call

Think less in terms of Google vs ChatGPT. Think more in terms of parallel influence. Either way, we believe AI tools are playing an active role in the decision making unit.

GEO is already shaping decisions - and that's only going to increase.

## Is GEO just about discovery and top of funnel?

Not at all. We strongly believe AI tools are buyer enablement co-pilots across the full B2B buyer journey. We think less in easy to attribute linear funnels, and more in non-linear journeys that are complex, considered and hard to measure.

GEO can influence:

* What problems buyers prioritise
* Which tools they consider
* How they build shortlists
* What questions they ask during demos
* How confident they feel in supplier selection

That's not top of funnel. That's revenue impact.

## Are sources like G2, Gartner, Reddit & Quora more important for GEO?

Yes, massively. At FirstMotion we're doing lots of search into the sources that influence B2B software related search queries.

LLMs don't just crawl your homepage. They pull trusted, cited content from:

* G2, Capterra, TrustRadius
* Reddit, Quora, LinkedIn Pulse
* Forrester, Gartner and other analyst reports
* Niche industry media sources
* Third party blogs, integrations directories, and guest posts

We call this influence intelligence - knowing which sources AI tools cite for prompts in your category, and earning visibility in those locations.

## Do I need to focus on Bing for to improve visiblity in ChatGPT?

If you want to be visible in ChatGPT's web search mode, then yes, 100%.

When ChatGPT doesn't have a confident answer within its training data (the knowledge already built into the model), it uses Bing to search the web. If your content isn't ranking on Bing, ChatGPT may never find it.

## How do I figure out what prompts my buyers are using?

That's the million dollar question, and the heart of GEO.

At why at FirstMotion, we start with strong audience intelligence that considers:

* Not just job titles, but buying behaviour
* Use lots of data enrichment and intent data sources
* Use AI to reverse map likely prompts by persona and journey stage
* Analyse source language from reviews, Reddit, LinkedIn, etc
* Feed all this into our Prompt Matrix for each client

It's not about guessing or simply taking 'SEO keywords' and converting them easily into prompts. Prompts are much more contextual, and often much longer than a keyword.

## How can I track clicks from AI answers in ChatGPT?

Not reliably, and that's part of the dark funnel problem.

ChatGPT often paraphrases your content without linking. It might mention your brand name, but not include a link to your site. Buyers may then search your brand separately in Google after seeing a mention. That means no attribution, no UTM, no referrer from ChatGPT - even through ChatGPT was where the discovery moment happened, or what prompted the user to come to your site.

One thing to keep an eye out for is an increase in branded searches to your site. Maybe a tool like ChatGPT is recommending your brand name, but the 'referrer' is still organic search because the user still has to Google you.

We'll keep this page regularly updated with some of our most frequently asked questions about AI search and the shift from SEO to GEO - let us know if you have questions you want answering.

If you want to keep up to date with all the latest GEO research, studies and data driven insights, check out our GEO research database.

---

# GEO vs SEO: What's the Difference?

Source: https://firstmotion.com/insights/geo-vs-seo-whats-the-difference

## The world of search has shifted. Again.

But this time, it's not a Google algorithm tweak or a new SERP layout. This is bigger and more fundamental.

Generative Engine Optimisation (GEO) isn't just a buzzword, it's a new reality. And if your growth team is still treating SEO like it's 2018, you're not just missing traffic. You're missing influence.

In this post, we break down the core differences between SEO and GEO, and why the smartest B2B software brands are already planning for both.

## What Is SEO?

Search Engine Optimisation (SEO) is the practice of increasing visibility in traditional search engines like Google. For years, the playbook was simple (but not easy):

- Research keywords
- Optimise your site
- Write content
- Build backlinks
- Climb the rankings
- Get clicks

SEO's strength was predictability. You knew what people were searching for. You had tools like Ahrefs and SEMrush to estimate volumes. You could reverse engineer what worked. And everything was deterministic - keyword volumes were easy to measure and rankings were precise.

## What Is GEO, and why is it different?

Generative Engine Optimisation (GEO) is about visibility inside AI generated answers, not just on a list of blue links. When users ask questions in ChatGPT or Perplexity they often get direct and contextual answers, personalised to them - sometimes without any links at all.

And those answers? They're not always sourced from the biggest domain. Or the page with the most backlinks. They're generated from what the model believes is most relevant and trustworthy source.

GEO is how you show up in those answers.

## SEO vs GEO: The core differences

| Feature | SEO | GEO |
|---------|-----|-----|
| **Purpose** | Rank in search engine results | Be cited/included in AI generated answers |
| **Audience** | Humans searching via Google | LLMs generating responses in tools like ChatGPT |
| **Discovery Method** | Crawl > Index > Rank | Ingest > Extract > Synthesise |
| **Main Metric** | Organic traffic, CTR, rankings | Visibility in AI answers, citation frequency |
| **Optimisation Tactics** | Keywords, content, backlinks, technical SEO | Prompt/intent alignment, content, offsite influence |
| **Tools** | Ahrefs, Moz, SEMrush, GSC | Peec, ContextualJourney™ |
| **Traffic Flow** | Click-through to site | Mentioned in answer, may or may not drive click |
| **Source of Results** | Crawled & indexed website content | Training data built into model, or web search (e.g. ChatGPT & Bing) |
| **Update Cadence** | Algorithm updates every few months | Model & source updates weekly or even daily |

## Why GEO is important now

We're seeing a shift in buyer behaviour, especially in B2B software purchase journeys:

- Buyers use tools like ChatGPT to compare tools, shortlist vendors, generate RFP questions, shape evaluation scorecards and summarise reviews.
- They don't always click.
- They trust the answer, not the link.

> GEO doesn't replace SEO, it runs in parallel. But it rewards different actions.

## How GEO and SEO work together

Here's what we're seeing across our clients at FirstMotion:

1. **Great SEO = solid foundation.**
   - Sites with high authority and well structured content often perform well in GEO too, but not always.

2. **But GEO rewards clarity and context, not just authority.**
   - LLMs extract meaning. They want structure, factual statements, semantically rich formatting, and trusted sources.

3. **Offsite brand mentions matter more than ever.**
   - Tools like ChatGPT pull heavily from G2, Gartner, Reddit, LinkedIn Pulse, Quora, and more.
   - GEO success means showing up in the right ecosystem, not just your own site.

## GEO vs SEO: FAQs

### 1. What actually is GEO?

GEO stands for Generative Engine Optimisation, the art and science of being visible in AI generated answers, not just search engine results.

### 2. Is it called GEO or AIO or LLMO?

There's lots of noise in the space. You'll hear terms like AIO (AI Optimisation), LLMO (LLM Optimisation), and AISEO. We call it GEO, and we think that's the clearest framing for what's really happening.

### 3. What is a GEO agency?

A GEO agency helps you understand how generative engines work, mine the prompts your buyers use, analyse where you're cited (or not), and build a strategy to show up in those answers.

### 4. How does GEO differ from SEO?

SEO = rank for keywords in Google. GEO = be cited in answers from AI tools like ChatGPT. Different discovery methods. Some different optimisation techniques. Different outcomes.

### 5. How does GEO change the B2B buyer journey?

AI assistants are now embedded in how B2B buyers research. They're being used for comparisons, evaluation frameworks, and supplier shortlisting, not just discovery. GEO influences every stage of the buyer journey. AI assistants are buyer enablement co-pilots.

### 6. What are the best analytics/visibility tools for GEO?

We use Peec AI to track brand visibility across ChatGPT, Perplexity, Claude, Google AI Overviews and others. It shows which prompts you're visible for, which influence sources are cited, and how competitors are performing in AI search.

### 7. What's the most important part of a GEO strategy?

Audience intelligence. At FirstMotion, we believe GEO must start with understanding how your buyers behave, what prompts they use, and how they evaluate vendors. That's why we built our ContextualJourney™ platform, to help us map and enrich ICPs, personas, stages, and prompts to guide everything else.

### 8. Will AI tools eventually overtake Google?

Not immediately, but the shift is underway. We're seeing high intent search behaviour migrating to generative engines, but also lots of research and evaluation too.

### 9. Is GEO just about top of funnel?

Not at all. AI tools are becoming buyer enablement co-pilots - used throughout the journey, from problem identification to supplier evaluation. GEO helps you influence across the funnel - potentially at more opportunities than ever before.

### 10. Are sources like G2, Gartner, Reddit, and Quora more important for GEO?

Yes. These sources are frequently cited by LLMs. Being present, and well positioned, on them increases your chances of inclusion in generative answers.

### 11. Do I need to focus on Bing for GEO?

Yes, especially if you want to be visible in ChatGPT with web search enabled, since it pulls results via Bing's API.

### 12. How do I figure out what prompts my buyers are actually using?

That's the art of prompt mining. We analyse buyer roles, intent, and stage of journey to build a Prompt Matrix. You can't guess your way into AI visibility, in a keyword-less world you need to unlock insights first.

### 13. What kinds of content get cited in AI answers?

- Structured, well formatted content
- Listicles, comparisons, Q&As, frameworks
- Content hosted on trusted domains
- Clear statements of fact, statistics, or expert insight

### 14. How often should I monitor AI search visibility?

At a minimum monthly. Models update fast. Tools like Peec let us see which prompts you're visible for, and when that changes.

### 15. Can I repurpose SEO content for GEO?

Sometimes, but it often needs reframing. GEO content should answer specific prompts, be highly contextual, and often live in or be repurposed in third party ecosystems too. There will be overlap in a SEO and GEO content strategy, but also some subtle but important differences.

### 16. Can I track clicks from AI answers?

Sometimes, but often, you can't. Some AI tools don't include links to products they recommend in their answers. That's why we talk about the AI-powered dark funnel.

---

# AI Search Statistics: The rise of AI Search Over Google (Updated 2026)

Source: https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google

**By Tom Batting | July 8, 2025 (Updated September 17, 2026)**

Google's dominance in search is being challenged faster than most expected. AI-powered tools led by ChatGPT are driving measurable shifts in how people search, what they click, and whether they click at all. This post was originally published in July 2025 with six statistics. We have now expanded it to nine, updated all figures to reflect primary research through mid-2026, and added a last-updated date.

Here are some stats showing just how quickly the adoption of AI tools like ChatGPT are driving a change in search behaviour and usage - and potentially undermining Google's dominance.

## 1. Google's search share dipped below 90%, then recovered

Google's overall share of search engine referrals briefly fell below 90% in mid-2024 for the first time in over a decade, dropping to 89.54% by July 2025 (Source: [Statcounter](https://gs.statcounter.com/search-engine-market-share/all/worldwide/2024)). That specific number has since recovered: Statcounter's own live figures put Google back at 91.1% as of August 2026.

The erosion hasn't disappeared, though; it's just showing up somewhere this particular measurement doesn't capture. Google's own AI Overviews count as a Google search, not a competitor gain, and standalone AI platforms like ChatGPT are absorbing queries that used to end in a click to a results page. See #9 below for what that shift actually looks like today.

In the same period, Bing increased its share from 3.32% in February 2025 to just under 4% by July 2025. That's a small move on its own, but worth noting given OpenAI's partnership with Bing: ChatGPT [uses Bing's index to search the web](https://firstmotion.com/insights/is-bing-important-when-it-comes-to-improving-chatgpt-answer-visibility).

## 2. Gartner predicts a 50% drop in organic search by 2028 due to AI search

Based on its [research and surveys](https://www.gartner.com/en/newsroom/press-releases/2023-12-14-gartner-predicts-fifty-percent-of-consumers-will-significantly-limit-their-interactions-with-social-media-by-2025), Gartner believes that the 'rapid adoption of GenAI in search engines will significantly disrupt CMOs' ability to harness organic search to drive sales.'

Emily Weiss, Senior Principal Researcher in the Gartner Marketing Practice, added "Marketing leaders whose brands rely on SEO should consider allocating resources to testing other channels in order to diversify."

## 3. Similarweb shows ChatGPT is now the 5th most visited website in the world

In April 2025 ChatGPT took over X to become the 5th most visited website in the world. Impressive by any measure, but even more so when you consider it wasn't even 3 years old at this point.

And what's just as impressive is the ongoing growth and just how quickly they are adopting new users (Source: [Similarweb](https://www.linkedin.com/pulse/from-top-10-5-chatgpts-meteoric-rise-ai-shakeup-similarweb-zabmf/)).

## 4. Just how many searches now show an AI Overview? Depends who you ask

Estimates vary enormously by methodology, and no figure has become the industry standard. Similarweb's 2026 Generative AI Landscape report puts it at 43% of US searches as of May 2026, up from roughly 15% in January 2025 (Source: [Similarweb](https://www.similarweb.com/corp/the-2026-generative-ai-landscape/)). Adthena measured 18% in June 2026. An independent, peer-reviewed study of 55,393 trending queries found just 13.7% overall, but 64.7% for question-format queries specifically (see #9 below).

None of these fully disclose their methodology, so treat any single confident percentage, including ours, with some scepticism. What's not in dispute: AI Overviews show up meaningfully more often today than 18 months ago, across every measurement approach.

## 5. Ahrefs confirms AI Overviews reduce clicks by 58% for the top-ranking page

[Ahrefs' study of 300,000 keywords (December 2025)](https://ahrefs.com/blog/ai-overviews-reduce-clicks-update/) found that AI Overviews correlate with a 58% lower click-through rate for the page ranking first. The effect is present even for pages that own the AI Overview citation: they see more impressions but fewer clicks per impression.

## 6. AI search traffic converts at significantly higher rates (but context matters)

[Seer Interactive's case study (October 2024 to April 2025)](https://www.seerinteractive.com/insights/case-study-6-learnings-about-how-traffic-from-chatgpt-converts), B2B software client: ChatGPT converted at 15.9%, Perplexity at 10.5%, Claude at 5%, Gemini at 3%, versus Google organic at 1.76%. ChatGPT users also viewed 2.3 pages per session versus 1.2 for organic. [Ahrefs reported](https://ahrefs.com/blog/ai-search-traffic-conversions-ahrefs/) internally that AI traffic drove 12.1% of all signups from just 0.5% of visits, a 23x conversion rate advantage. [Semrush's broader July 2025 research](https://www.semrush.com/blog/ai-overviews-study/) found LLM visitors convert 4.4x better than organic on average. The effect is strongest for B2B and high-consideration purchases; it is weaker or neutral for impulse ecommerce.

## 7. Being cited in an AI Overview helps, but it's recovery, not a win

Seer Interactive's original September 2025 study found cited brands earned 35% higher organic CTR and 91% higher paid CTR than uncited brands. Their updated 2026 analysis, covering 53 brands, 5.47 million queries and 2.43 billion impressions tracked from January 2025 to February 2026, refines that (Source: [Seer Interactive](https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-2026-update)): cited brands now earn roughly 120% more organic clicks per impression than uncited brands on the same query. But being cited still trails a query with no AI Overview at all: cited pages convert at roughly 2.1% CTR versus 3.8% where no AI Overview appears, a 38% shortfall.

The takeaway hasn't changed, even if the numbers have: getting cited beats not being cited. It just isn't a full substitute for the click volume a page earned before AI Overviews existed.

## 8. Referring domains are the single strongest predictor of AI citation

[SE Ranking's study of 129,000 domains](https://seranking.com/blog/how-to-optimize-for-chatgpt/) found referring domains are the strongest predictor of ChatGPT citation. Sites with more than 350,000 referring domains average 8.4 citations per response. Domains active on Trustpilot, G2, and Capterra earn 3x more citations than those without profiles. This means link-building for traditional SEO and AI search visibility are [largely the same investment](https://www.searchenginejournal.com/new-data-top-factors-influencing-chatgpt-citations/561954/).

## 9. Nearly two-thirds of question-style searches now trigger an AI Overview

A study of 55,393 trending queries across 19 categories, tracked over 40 days from March to April 2026, found Google AI Overviews activate on just 13.7% of searches overall, but that rate jumps to 64.7% for question-format queries (Source: [Xu, Iqbal & Montgomery, "Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact"](https://arxiv.org/abs/2605.14021)). B2B buyers researching vendors overwhelmingly search in exactly that conversational, question-led style, not two-word head terms, so the practical exposure for this audience sits far closer to two-thirds than to one in ten.

---

# Is Bing important when it comes to improving ChatGPT answer visibility?

Source: https://firstmotion.com/insights/is-bing-important-when-it-comes-to-improving-chatgpt-answer-visibility

If you're looking to improve your brand's visibility in ChatGPT, Bing could be a more important search engine than ever before.

Marketers love to say "no one uses Bing." And in the old world of SEO, they were probably right - for most brands, Google was the only search engine to think about. After all, Google has consistently had about a 90% share of the search engine market, whilst Bing has had less than 4%.

But if you care about visibility in ChatGPT and the world of generative engine optimisation, especially GPT-4o and later models with browsing enabled - you're going to want to pay attention to Bing. There's lots of evidence showing AI tools rising in usage and potentially overtaking Google.

Why is Bing important for being more visible in ChatGPT? Because when ChatGPT doesn't know something: it asks Bing.

## The OpenAI, Microsoft, Bing Partnership

Microsoft has invested around $13 billion in OpenAI. In return, OpenAI's technology now powers Microsoft's Copilot experiences across Bing, Edge, Windows, and Office. But the partnership works both ways and one of the most important consequences is that when ChatGPT browses the web, it does it through Bing.

* ChatGPT's web browsing plugin is powered by Bing's API
* Bing's index is what's used when ChatGPT browses the web
* Microsoft's infrastructure underpins OpenAI's deployment at scale
* Bing and Edge are increasingly integrated into how and where GPT-4o retrieves real time data

### What triggers ChatGPT to do a web search?

ChatGPT doesn't always search the web, sometimes instead relying on its own knowledge acquired through its training data, but here are common scenarios where it does:

* Questions about recent updates or that rely on new information
* "Best tools for X" where the model is uncertain
* Certain pricing, availability, or comparison requests
* Some region specific queries
* Niche or lesser known areas where training data is sparse

It is also possible for users to explicitly request ChatGPT to do a web search, which they may want to do if they want ChatGPT to look beyond its own training data in order to give a better answer.

In these cases, ChatGPT uses Bing's index to fetch results - and so if you're not indexed in Bing, you're not making it into the answer.

## Bing is a hidden GEO lever

Bing's search index and ranking algorithms are leveraged to ground ChatGPT's responses and provide citations, enhancing the reliability and transparency of the information provided.

So Bing's index is now the backbone of ChatGPT's real time search. From product data to pricing, news to reviews, Bing gives ChatGPT access to a broad and constantly updated slice of the web. While it's long been seen as Google's second act, the OpenAI–Microsoft partnership has pushed Bing back into strategic relevance, especially for brands that want to be visible inside generative answers.

## What kind of prompts might trigger a ChatGPT web search?

Whilst B2B software brands may not be as worried about real time information as other types of product or service provider, there are certain prompts relating to the B2B buyer journey that may leverage a Bing search.

### Recent Information

For example ChatGPT's training data has a cut off. So if the prompt asks about anything updated recently, it'll often go to the web.

Examples:

* "What are the newest features in HubSpot's Service Hub as of Q2 2025?"
* "Did Salesforce just announce new AI functionality for Slack integration?"
* "Latest comparison between Gong and Chorus for 2025?"

### Comparisons

Another example is product comparisons, especially less mainstream or niche areas.

Examples:

* "What's the difference between Mutiny and Intellimize for website personalisation?"
* "Compare ContractPodAi vs Malbek vs LinkSquares for CLM features and integrations."
* "How does Paddle stack up against Stripe for SaaS billing in Europe?"

### Reviews & case studies

When a prompt calls for third party opinion, LLMs often browse forums, review sites, and user-generated content. Some of this information might be in the LLMs training data already, but some prompts might trigger a web search.

Examples:

* "What do G2 reviews say about Chili Piper's onboarding experience?"
* "Are there any customer case studies for using Apollo.io with HubSpot?"
* "What are the pros and cons of using Notion as a company knowledge base?"

### Pricing, Licensing, and Packaging

LLMs typically do not retain accurate or up-to-date pricing info in training data, so often they'll try to fetch it live.

Examples:

* "What's the latest pricing model for Contentful in 2025?"
* "Is WorkRamp priced per seat or per module?"
* "How much does an enterprise plan for Heap Analytics cost?"

### Regional or Industry-Specific Queries

If the user adds context like geography or industry niche, ChatGPT may need to pull in more specific info.

Examples:

* "Best CRM platforms for government contractors in the UK"
* "CLM tools suitable for fintech companies with data residency in Europe"
* "Local implementation partners for NetSuite in Dubai"

### Security, Compliance & Technical Specs

When users ask detailed technical or compliance related questions, ChatGPT may decide to try and find the most current documentation.

Examples:

* "Is AirTable SOC 2 compliant in 2025?"
* "What kind of role-based access controls does Amplitude support?"
* "Does Klaviyo store data in AWS or GCP?"

### Event-Driven Prompts

These might relate to product launches, announcements, or market changes.

Examples:

* "Which B2B software vendors announced funding rounds in June 2025?"
* "Was Vanta featured in the latest Gartner Magic Quadrant?"
* "Who's sponsoring SaaStr 2025?"

## What can you do to ensure visibility in Bing?

There are a number of steps you can take to think about your website's relationship with Bing, including first and foremost, making sure you submit your site to Bing Webmaster Tools.

Doing so will allow you to build a full picture of how Bing currently understands and indexes your content, and along with prompt visibility analytics enables you to start planning the steps to make sure Bing has the best chance possible to index your content. And most importantly, increase your chances of being included in an answer by ChatGPT.

Check out our FAQs page for your questions answered on GEO and AI search.

---

# How to gain visibility in generative AI answers: GEO / AI SEO for Perplexity and ChatGPT

Source: https://firstmotion.com/insights/how-to-gain-visibility-in-generative-ai-answers-geo-ai-seo-for-perplexity-and-chatgpt

The world of SEO has changed - fast and forever. Optimising for AI search is a whole new world - and whilst there is certainly some overlap with old school SEO, there are also lots of areas requiring a fresh approach and a mindset shift.

As a B2B software focused AI SEO agency, here is an overview of some of the things we look at when improving a B2B software brand's AI answer visibility.

## 1. Deeply Understand Your Audience

Before you write a single word of content and before you start tracking prompts or trying to optimise anything, you need to know who you're optimising for.

Really this is true of any marketing strategy - it's definitely not new because of AI SEO. But it is often overlooked, and now more important than ever as part of a GEO strategy.

But in the world of SEO, we had keyword volumes. In the world of GEO, we don't.

Which means we believe everything must start with deep buyer intelligence.

At FirstMotion, we've built a platform focused on B2B software buyer journeys designed for exactly this:

- Map your ICPs, their personas, their roles in the buying unit
- Understand the stages of their journey, use cases, and evaluation triggers
- Analyse competitor reviews to semantically understand buyer language
- Enrich the data using intent data, millions of B2B buyer data points and AI to deeply understand buyer behaviour

No prompt strategy is complete without deep, enriched buyer context.

## 2. Mine Prompts

Only once you have a deep audience and buyer behaviour understanding can you begin to 'mine' prompts that might be being used at the various stages of the buyer journey. Our platform takes all of the audience intelligence data and uses AI to help with this process.

Our methodology at FirstMotion is to align prompts by B2B buyer journey stages, building Prompt Matrix that maps prompts across ICPs, personas and stages.

By default we use the following buyer journey stages, but customise this for each client, ICP and decision making unit. Here is an example of a General Counsel in a mid size tech company exploring contract lifecycle management software solutions:

| Buyer Journey Stage | Prompt Example |
|---|---|
| Problem Identification | How do other GCs in mid-sized companies handle version control and contract visibility across departments without relying on email chains and manual tracking? |
| Solution Exploration | Can you compare leading contract management tools like Ironclad, LinkSquares, and ContractWorks in terms of ease of use, legal team adoption, and integration with Google Workspace and Salesforce? |
| Requirements Building | What features should a mid-sized tech company's legal department look for in a CLM solution if we want to automate clause fallback, ensure version history, and allow cross-functional access (e.g. sales, finance, procurement)? |
| Supplier Evaluation | Based on reviews from legal teams in mid-sized SaaS companies, how does Ironclad compare to LinkSquares in terms of implementation speed, usability, and legal team satisfaction? |

Notice that prompts are much longer and more contextual than 'SEO keywords' - and whilst the above are just examples, we would expect to see even longer prompts on a real client engagement.

## 3. Analyse Prompt Visibility

Once you've 'mined' your prompts, the question becomes: are we showing up in generative answers currently for these prompts?

This is where prompt visibility tools like Peec come in. Peec is our analytics and visibility monitoring partner at FirstMotion.

We use Peec to:

- Track brand's visibility across AI tools like ChatGPT, Perplexity, and Google AI Overviews
- Compare visibility against competitors for the same prompts
- Monitor how visibility shifts over time

Here's an example from one of the prompts relating to the contract lifecycle management software example above, showing the specific URLs the various models have cited and how frequently, according to Peec AI.

A key mindset shift for marketers to make here that there are no "rankings" in generative search - there are only probabilities.

## 4. Run Influence Intelligence

If your brand isn't showing up, you don't just need to know who is - you need to know why.

LLMs pull from sources they trust. And increasingly, that's not your homepage.

Using Peec we are able to analyse citations and sources - the content that is influencing the answers tools like ChatGPT may give users.

As we specialise in B2B software, we often see sources like Gartner Magic Quadrants, Forrester Wave reports, LinkedIn Pulse, G2 and Capterra reviews and niche forums like Quora or Reddit being referenced by LLMs. If you would like to know more about how these sources might be influencing your own AI SEO visibility, get in touch.

## 5. Create a Prompt Aligned Content Strategy

Aligning content with your prompt matrix and based on your influence intelligence is worthy of its own post that would be thousands of words long, so we're not going to go deep into the process here.

In the GEO era, the most effective content is shaped by the actual language and context your buyers use when interacting with AI assistants. So here are a few things to think about for now:

- Stay niche and really break down content by ICP, persona and buyer stage - journey context is key
- Don't just mirror the prompt - answer the users intent with scannable, citable, contextual content
- Think about how content can be easily parsed and summarised by LLMs and if summaries, clear headers, lists and schema markup might help
- Focus on semantic richness over keyword stuffing
- Ensure content remains fresh and up to date with your own citations and references
- Think about your offsite content strategy and where content could be republished

## 6. Define an Offsite Strategy

Our research has shown that, for certain types of prompt at certain buyer journey stages, LLMs often prefer third party citations over your own site's content. This is one of the most large and complex areas of a generative engine or AI search optimisation strategy.

Branded mentions in locations beyond your own website are a factor we're tracking carefully.

So some areas to think about might include:

- G2, Capterra, TrustRadius - high-quality reviews and clear positioning
- Reddit, Quora, LinkedIn Pulse - authentic content and commentary
- Analyst reports from Gartner and Forrester
- Integration directories, analyst blogs, product roundups

Tactics:

- Review campaigns on trusted platforms
- Create content for third party guest publications
- Comment and contribute to the discussion where LLMs learn in places like Reddit and Quora
- Think about content republishing and repurposing, and if it makes sense to build authority in other places beyond your own website

## 5. Monitor Visibility Continuously

GEO isn't static and models are updating regularly. Prompt phrasing shifts. Citations change. And monitoring visibility is therefore both more complex and more important than in the old school days of SEO, when Google was really the only search engine you had to worry about.

You need ongoing visibility monitoring to:

- Track how often your brand is showing up for your selected prompts
- See which models favour your content
- Spot shifts in competitors' inclusion rates
- Identify which content or source changes lead to visibility changes

## 7. Don't Abandon SEO Fundamentals

This isn't a binary choice between SEO and GEO. In lots of our research we see positive correlation between brands with historically good SEO also performing well in AI search.

But hopefully it's also clear from our post that there are lots of new things to think about and some mindset shifts that need to happen in the new era of AI search and generative engine optimisation. Maybe it's time to build the business case to invest in AI SEO.

One important note: OpenAI has a partnership with Bing through it's relationship with Microsoft - meaning when a ChatGPT prompt triggers a web search, it relies on Bing's index. So whilst your previous SEO strategy might have only focused on Google, it may be worth thinking about how your site is indexed in Bing too.

If you're a B2B software brand looking for help understanding the shifting landscape of SEO and AI search, we would love to talk.

---

# What really is a GEO / AI SEO Agency?

Source: https://firstmotion.com/insights/what-really-is-a-geo-ai-seo-agency

**Author:** Alex Price | **Date:** July 4, 2025

## LLMO. AIO. AI SEO. GEO.

The buzzword soup around 'AI search' is growing by the day, and lots of it is noise. So many acronyms... all describing the same thing?

At FirstMotion, our definition of a [Generative Engine Optimisation agency](https://firstmotion.com/services/ai-search-optimisation) is currently:

A GEO agency (Generative Engine Optimisation agency) helps brands become
- Visible in generative search tools like ChatGPT, Perplexity, Gemini and Claude
- Credible across trusted third party sources that LLMs rely on
- Influential at key moments in the AI-native buyer journey

## Why is it called GEO?

We like the term Generative Engine Optimisation because we feel it puts a strong lens on visibility + context being at the centre of the strategy, whilst feeling like a natural next iteration of SEO.

As the new category of GEO and AI search optimisation evolves, we expect to see multiple terms still being used to talk about a brands AI search visibility and performance.

## Is it really different from 'normal SEO'?

In much of our research so far, there are some areas of correlation between 'traditional SEO' performance and GEO performance, but also many areas where there is much less overlap. We've seen some brands with what may be considered strong SEO performance metrics perform much worse in AI search results than we might expect, and been able to identify how some underdogs are punching above their weight in AI search.

Another important consideration is just how many *different* AI search engines there actually are. When we talk about SEO, we really only mean Google usually. But when we talk about GEO, the list of tools is extensive and they're all releasing new features at enormous pace.

Lastly, with SEO the process of 'keyword research' is fairly straightforward. There are no shortage of tools that will tell you how many monthly searches a certain keyword query will get. But with GEO, this same data doesn't exist. Prompts are much more contextual than keywords, much longer and much more personalised. Which is why at FirstMotion, our ContextualJourney™ technology platform sits at the heart of our process, using millions of B2B buyer data points, intent data and data enrichment along with AI to power some very deep audience intelligence. It's our firm belief that in a keyword-less world, you can't nail an AI search strategy without this.

Marketers are having to go through a big shift in mindset from ['rankings' to 'probabilities'](https://firstmotion.com/insights/why-generative-engine-optimisation-isnt-about-rankings-its-about-probabilities).

So yes, some of the 'tactics' a GEO agency might use will often overlap with those we have used in SEO for a long time. But there are plenty of differences needing a change in approach, mindset and lots of smart technology.

## Rethinking the agency model

Part of building a [B2B GEO agency](https://firstmotion.com/services/ai-search-optimisation) proposition at [FirstMotion](https://firstmotion.com/) is also about rethinking what brands really need and want from their agency relationships.

We're building FirstMotion as a new breed of consultancy rather than agency - faster, smarter, more technology, fewer layers of inefficiency, deeper B2B specialism - and therefore better value - than any search agency that has come before us.

A [proper GEO partner](https://firstmotion.com/insights/how-to-choose-a-b2b-saas-geo-ai-seo-agency-with-evaluation-scorecard-download) should help you:
- Understand your audience's real AI search behaviour
- Map prompts across your buyer journey
- Optimise your presence on third party platforms LLMs trust
- Create citability ready content, both on-site and off
- Run regular visibility audits across tools like ChatGPT, Perplexity, and Claude
- Shift your attribution thinking - because not all influence leaves a trail

Check out our [FAQs page for your questions answered on GEO and AI search](https://firstmotion.com/insights/generative-engine-optimisation-ai-search-your-questions-answered).

---

# The GEO Glossary: Key Terms B2B Marketers Should Know About AI Search Optimisation

Source: https://firstmotion.com/insights/the-geo-glossary-key-terms-b2b-marketers-should-know-about-ai-search-optimisation

Trying to get your head around the new world of AI search and generative engine optimisation? Our glossary is here to help.

If you're still talking about keywords, rankings and SERPs, it's time to catch up.

[Generative Engine Optimisation](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) (GEO) is reshaping how B2B buyers discover, compare, and decide - the next phase of AI SEO.

We've put this glossary together to help B2B marketers get their head around all the AI search optimisation lingo they need to know.

These are the terms that matter now, in a world where prompts replace queries, AI assistants replace search engines, and visibility doesn't always equal traffic.

Let's get fluent.

## Foundational concepts of GEO

| Term | What It Means |
|------|--------------|
| **GEO (Generative Engine Optimisation)** | The practice of making your brand visible inside generative engines like ChatGPT, Perplexity, and Claude. Think SEO, but for AI tools or LLMs. |
| **LLMO (Large Language Model Optimisation)** | A subset of GEO focused on influencing the output of LLMs directly, but often used interchangeably with GEO. |
| **Answer Engine Optimisation** | Structuring website content to directly answer user queries within search engines and AI-powered tools - also used interchangeably with GEO / LLMO. |
| **AI Search** | Search experiences powered by large language model, often conversational and generative, not link-based. |
| **Prompt** | The new search query or keyword. It's what a buyer types into ChatGPT or Perplexity. Google research shows often 2-3x longer than a typical Google search. |
| **Prompt Visibility** | Your brand's likelihood of showing up in a generative response to a specific prompt. |
| **PromptPath™** | A FirstMotion framework for defining an GEO or AI Search optimisation strategy in AI-native B2B buyer journeys. |
| **ContextualJourney™** | FirstMotion's own technology platform for AI search intelligence, including audience research, data enrichment & AI to define winning AI search strategies. |

## How AI models work when it comes to search

| Term | What It Means |
|------|--------------|
| **LLM (Large Language Model)** | AI trained on massive datasets to generate human like text and answer complex questions. |
| **Training Data** | The information used to train a model before it's deployed. This includes web pages, forums, docs, YouTube videos etc. |
| **Citations** | The sources an AI model references in its answers. Sometimes clickable/linked to, sometimes not. |
| **Retrieval Augmented Generation (RAG)** | A method where LLMs pull in real time or external data to improve responses. |
| **Temperature** | A setting that controls how "random" or "creative" a model's output is. |
| **Memory** | The model's ability to remember context across sessions - crucial for longer B2B journeys. |

## Generative Engine Optimisation techniques & tactics

| Term | What It Means |
|------|--------------|
| **Structured Content** | Content formatted for clarity and semantic cues - more digestible by LLMs. |
| **Third Party Optimisation** | Ensuring your brand is visible on sources LLMs love to cite (G2, Forrester, Gartner, Reddit, LinkedIn etc). |
| **Prompt Mining** | Researching and mapping the kinds of prompts your buyers are likely using. |
| **Content Mapping** | Crafting content to align with common prompts and maximise citation potential. |
| **AI Citability** | How likely your content is to be referenced by an LLM, based on structure, authority, and clarity. |
| **Zero Click Journey** | A buying process that happens entirely within an AI assistant, with no site visit at all. |
| **LLMs.txt** | An llms.txt file is a proposed standard for websites to guide Large Language Models (LLMs) in understanding and navigating site content. |
| **Influence Intelligence** | FirstMotion's approach to understanding _why_ certain AI models choose to cite certain sources of 'influence' in their answers. |

## Challenges & limitations

| Term | What It Means |
|------|--------------|
| **Dark Funnel 2.0** | Buyers discover and evaluate your brand via LLMs - but you never see it in analytics. See our [recent blog post](https://firstmotion.com/insights/how-ai-search-is-making-the-b2b-dark-funnel-even-darker) here. |
| **Attribution Collapse** | The breakdown of traditional attribution models in the face of AI discovery. |
| **Visibility ≠ Traffic** | Just because you're mentioned doesn't mean they click - or that you can track it. |
| **Answer Theft** | When your content trains or informs an LLM's response - but you don't get the credit or link. |

## Bonus: some SEO terms you should rethink...

| Term | Why It's Outdated |
|------|-----------------|
| **Keywords** | LLMs don't think in keywords, they understand context, concepts, and prompts. Prompts are much longer than keywords. |
| **SERP rankings** | There's not really a '1st position' in generative search, as every prompt can produce a different outcome for different users. |
| **Traffic = Success** | Influence matters more than clicks. Some of your most valuable B2B buyer touch points won't show up in Google Analytics. |
| **SEO-first** | We don't believe SEO is dead - but GEO is the long game. |

Check out our [FAQs page for your questions answered on GEO and AI search](https://firstmotion.com/insights/generative-engine-optimisation-ai-search-your-questions-answered).

---

# Why Generative Engine Optimisation isn't about rankings - it's about probabilities

Source: https://firstmotion.com/insights/why-generative-engine-optimisation-isnt-about-rankings-its-about-probabilities

**By Alex Price | July 2, 2025**

Let's cut straight to it: if you're still chasing 'position one', there's a chance you might be left behind.

GEO - [Generative Engine Optimisation](https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care) - doesn't care about rankings. Because whilst AI tools like ChatGPT might make recommendations in a certain order, rankings in the traditional SEO sense don't really exist any more.

B2B marketers need to shift mindset - from rankings to probabilities - as the [rise of AI search engines](https://firstmotion.com/insights/6-statistics-showing-the-rise-of-ai-search-over-google) takes place.

## The old world of SEO: deterministic, trackable, predictable

Traditional SEO was a closed circuit.

You targeted a keyword.  
You optimised a page.  
Google showed ten blue links in a fixed order.  
Your goal: get to position one and stay there.

The whole ecosystem - from Ahrefs to Semrush to every SEO agency on the planet - was built around _rankings_ as the metric of visibility. You could track them. You could report on them. You could screenshot your client's "#2 position" and pat yourself on the back.

That world has changed.

## The new reality: GEO is probabilistic

Generative search doesn't serve up fixed, repeatable, consistent results.

It generates answers in real time - often different answers for different people, or even different ones for the same person, depending on context, model memory, and randomness.

You might have noticed that the more you engage with ChatGPT, the more it remembers. That's because it has a [memory function](https://openai.com/index/memory-and-new-controls-for-chatgpt/).

Ask ChatGPT:

> _What are the top alternatives to Salesforce for a 500-person SaaS company?_

Then ask Perplexity.  
Then Claude.  
Then Microsoft Copilot.  
You'll probably get different answers. Different vendors. Different justifications.  
Sometimes links. Sometimes none. Sometimes citations (often very different citations depending on the LLM). Sometimes even hallucinations.

The results are not necessarily ranked, even if they are presented to you in a certain order.

They're composed by models with constantly shifting training data and context sensitivity.

So what are you really optimising for?

You're optimising for the **probability** of inclusion.

## One prompt ≠ one output

In the world of SEO, there was broadly one search results page per keyword. OK, maybe it would change a bit depending on location or device (such as mobile) for certain search terms - but usually the ranking results would be consistent.

In GEO, the permutations are much wider:

*   The exact prompt wording changes the outcome
*   The user's history/AI memory can influence the result
*   The model's "temperature" setting (i.e. randomness) affects what it says
*   Forms of Retrieval Augmented Generation (RAG) pulls in live info - or doesn't
*   Plugins, web access, or third party integrations may further skew results

Every prompt is a dice roll.  
  
Every answer is a composition.  
  
Every mention of your brand is a probabilistic event, not a deterministic position.

## Why AI visibility monitoring is key

In the old world, you checked your keyword rankings once a week or received an email alert from your rank tracker.

In GEO, things are dynamic in that models are retrained, updated, tuned regularly and at increasing pace.

And diverse, because there are so many AI tools currently being used (ChatGPT, Claude, Gemini, Perplexity, Slack copilots, custom GPTs etc etc).

And many of these tools now include memory.  
  
That means if a buyer asked about CRM platforms three weeks ago, looked at some options and gave some feedback, ChatGPT might remember it and suggest different follow ups depending on that prior context. It might know about the buyer, their purchase history, current tools, preferences and more.

So if you're not monitoring visibility constantly, across multiple tools, across multiple prompts, you are flying blind.

This is why platforms like [Peec](https://peec.ai/), our analytics partner at [FirstMotion](https://firstmotion.com/), are starting to emerge. And why we believe ongoing visibility auditing will become as fundamental as technical SEO audits used to be.

## But you can still influence how AI tools decide to talk about you

Here's the good news: while you can't force an LLM to list your brand, in the same way you couldn't force Google to, you can increase the probability that it does.

How?

*   **Third-Party Influence:** Make sure you're highly rated and well described on sites LLMs trust (G2, Capterra, Reddit, LinkedIn, Gartner, etc.)
*   **Structured, Citable Content:** Use clear headers, concise answers, and semantic structure that models can extract easily
*   **Deep Audience Intelligence**: It's fundamental to understand your buyers, their pain points, drivers, goals, buying triggers etc - otherwise effective prompt mining isn't possible
*   **Prompt Based Content Planning:** Build content for prompts, not just keywords. Think: "How do I evaluate XYZ tools?" or "What should I ask a vendor in a demo?"
*   **Frameworks like PromptPath™:** Map prompts across your buyer journey and shape visibility at every stage
*   **Ongoing Visibility Audits:** GEO is not a one and done exercise - it's a system you need to operate continuously

If you're looking for a [generative engine or AI search agency](https://firstmotion.com/services/ai-search-optimisation) to help you understand how your B2B software brand is currently visible alongside competitors in tools like ChatGPT currently, [get in touch](https://firstmotion.com/contact).

---

## Author Bio

**Alex Price**

I dropped out of university to turn my part time freelance business that I started as a teenager in my bedroom into an award winning digital agency. I grew the business organically, with no debt or investment, from just me as a 20 year old sole founder to a team of ~35 people and multi-million £ annual revenues and ~25% net profit margins - winning clients like Amazon. I also founded FINITE, a B2B marketing media brand and global membership community for software CMOs. In April 2022, aged 29, I sold the business to a US headquartered strategic buyer, realising the value of lots of hard work and achieving a life changing outcome.

---

# How AI Search is Making the B2B Dark Funnel Even Darker

Source: https://firstmotion.com/insights/how-ai-search-is-making-the-b2b-dark-funnel-even-darker

There's a new villain in the B2B dark funnel. It's not Slack. It's not peers DMing on a LinkedIn. It's not some analyst behind a paywall.

It's ChatGPT.

And it might be recommending your software right now… Without a single link. Without attribution. Without any signal hitting your analytics.

## What was the original dark funnel?

This one's worse.

The old dark funnel was made of whispers. Buyers talked in private Slack groups. They asked peers. They read analyst reports. It was messy, but at least we understood the shape of it.

Now? They're typing prompts into LLMs.

And those prompts are replacing Google. Replacing "best of" lists. Replacing TOFU content. Replacing your beautifully optimised case study that no one is clicking anymore.

And what do they get back?

A list of tools. Some summaries. A few alternatives. And ideally: your brand name.

But not always a link and therefore no click. No source. No attribution.

You're in the conversation. You're influencing the buyer. But maybe you're not capturing it.

## AI search has become the ultimate dark funnel

Let's break it down:

* A buyer asks ChatGPT: _"What are the top onboarding tools for B2B SaaS?"_
* It replies: _"Products like Appcues, Userpilot, Pendo and WalkMe are commonly used…"_

No links. No clicks. No tracking. Just a brand name floating in a black box.

Here's an example - not a link in sight.

## So is Google / Organic Traffic taking the glory for AI native discovery moments?

If the user decides to search for you later, the visit might show up as "Direct" if they somehow make it to your site by themselves or "Organic" in your reports if they come via Google. But if tools like ChatGPT aren't providing direct links to your site, then your content team gets no credit. Your paid team gets no insight. And your attribution is even more broken than you thought.

It's clear that adoption of AI tools like ChatGPT is on the rise, and that use of 'traditional' Google searches is falling. AI assistants are now buyer enablement co-pilots supporting the length of the B2B buyer journey, and they are trusted by buyers as sources of truth.

## What's Happening

**Why It's a Problem**

* You're getting mentioned in AI search responses. But often with no click or traceable visit
* Buyers are discovering you through prompts. But you have no way of knowing when or why
* You're influencing consideration. But your CRM, GA, and attribution tools don't show it
* Your "Direct" traffic is rising. But no one can explain why
* Your 'Organic' might look like it's staying the same. But it might be lying to you

## So what do you do?

You fight back. Smartly.

1. **Audit Your Prompt Visibility**: Use AI tools like Peec to find out if and where you're showing up in AI responses.
2. **Optimise for LLMs, Not Just Google**: Structure your content so it's prompt friendly – clear answers, concise summaries, comparative content, semantic markup.
3. **Track Branded Search as a Proxy**: Start mapping when your branded search spikes after known prompt exposure - it's not perfect, but it's something.
4. **Double Down on Name Recall**: If the link's not coming, your name needs to stick. Make sure you're the tool they remember (and spell correctly) when they leave ChatGPT.
5. **Rethink Attribution Entirely**: The buyer journey is nonlinear and AI-assisted now. Build models that reflect influence, not just clicks.

If you're not actively managing how your brand shows up in generative search - and understanding that visibility ≠ attribution then you're flying blind - maybe now is the time to start building the [business case for investing in AI search](https://firstmotion.com/insights/how-to-build-the-marketing-business-case-for-investing-in-ai-search-generative-engine-optimisation).

Want to see if your brand is already showing up in AI search? [We can tell you](https://firstmotion.com/contact).

---

# Why a16z is betting on GEO, and what it means for B2B marketers

Source: https://firstmotion.com/insights/why-a16z-is-betting-on-geo-and-what-it-means-for-b2b-marketers

**Author:** Tom Batting | **Date:** June 27, 2025

When the most powerful VC firm in tech says Generative Engine Optimisation (GEO) is overtaking SEO, marketers should pay attention. A16Z's recent piece titled ['How Generative Engine Optimization (GEO) Rewrites the Rules of Search'](https://a16z.com/geo-over-seo/) confirms many of own thoughts: AI search is not just a trend, it's a fundamental shift in how discovery works.

## a16z Doesn't Bet on Fads

Andreessen Horowitz doesn't waste time writing trend pieces. When they publish a 2,000 word piece exploring GEO eclipsing SEO, it signals a seismic shift. These are the same people who spotted the mobile wave, backed Facebook before it had a revenue model, and invested early in Coinbase while regulators were still catching up.

Now they're betting on Generative Engine Optimisation. Not as a side channel or speculative bet but as the next foundational layer of digital discovery.

## GEO: What It Means and Why It's Bigger Than You Think

GEO isn't a buzzword. It's about becoming discoverable, influential and quotable inside large language model (LLM) environments like ChatGPT, Claude, Perplexity, and Google AI Overviews.

After all, that's where many B2B buyer journeys are now beginning.

Unlike SEO, which optimises for rank, GEO optimises for _answers_. It's conversational, context rich, and increasingly personalised. Models don't care about who paid for the keyword, they care about giving their users the most specific, structured, trustworthy information.

## GEO Is Driving Conversions

Is ChatGPT already an attributable channel responsible for driving pipeline? It appears so.

Buried in the A16Z post is a stat that should make every growth leader sit up straight: **10% of Vercel signups now originate from ChatGPT recommendations.**

Vercel's CEO shared that just a month prior, that number was only 4.8%.

Our view at FirstMotion is that AI tools are not just search and discovery tools. They are buyer enablement co-pilots that sit in the decision making unit and support B2B buyers from start to finish - searching and discovery sure. But also comparing, evaluating, interrogating, analysing, negotiating - and pretty much everything else that happens in a B2B buyer journey.

And a nice piece of data from a [SEMRush AI Search Traffic Study](https://www.semrush.com/blog/ai-search-seo-traffic-study/):

> We have seen that the average AI search visitor (tracked to a non-Google search source like ChatGPT) is 4.4 times as valuable as the average visit from traditional organic search, based on conversion rate.

*Source: [Semrush](https://www.semrush.com/blog/ai-search-seo-traffic-study/)*

## From SEO to GEO

It's pretty clear the change is already here. Certain niches and industries may be impacted by others, but the data is evident - AI search poses both a big change, and a big opportunity (for those with a plan).

We're heading for a world where buyers aren't browsing your homepage - they're prompting their assistant.

They're not looking for your CTA. They're looking for the best answer.

And if you're not in the model's context window, you're not part of the decision.

GEO is no longer optional. It's the front line of:

* Buyer research
* Product comparison
* Objection handling
* Feature comparisons
* Demo prep
* Onboarding planning
* Negotiation

And more...

## Strategic Steps for B2B Teams

So what now? Here's how to get ahead:

1. **Audit your AI search visibility**: Prompt tools like ChatGPT, Perplexity and Claude. Are you mentioned? Which of your competitors are winning? Why?
2. **Map prompts to the buyer journey** - What are buyers doing at each stage? We use data enrichment & lots of AI to supercharge this process.
3. **Turn those into prompt informed content briefs** - map content, spot the gaps, and understand where certain prompts might be falling short.
4. **Optimise** - from here, the plan really depends on what the data tells us. There's no one size fits all, LLMs all work differently, each category is different and the pace of change is rapid.

Or, get in touch with us at [FirstMotion](https://firstmotion.com/), where it's our job to help B2B software marketing leaders navigate this period of change with clarity and confidence.

---

# Reddit: a critical part of an AI search visibility strategy?

Source: https://firstmotion.com/insights/reddit-a-critical-part-of-an-ai-search-visibility-strategy

Reddit isn't just for memes.

Did you know there is a subreddit on Reddit called [LegalTech](https://www.reddit.com/r/legaltech/)? It has ~10,000 members, and describes itself as being for 'those in the legal field interested in improving the legal profession through the use of technology'.

Recently at FirstMotion, we were doing some AI search optimisation strategy work for a B2B software brand in the 'contract lifecycle management' software category. Basically software that helps you manage contracts - either as an in house legal team, or in a law firm.

After lots of audience intelligence work, we developed our prompt map. One of those prompts that we established could be used towards the middle or end of a B2B buyer journey was a comparative analysis of two products in the space:

_I'm comparing Ironclad and LinkSquares for legal contract automation – which is better? Who else should I consider?_

For this prompt, ChatGPT used Reddit as the most frequently used source - being cited in 57% of chats relating to this prompt.

Thanks to our analytics tools, we can see the various Reddit discussions being cited by tools like ChatGPT and Perplexity when they are responding to users prompts.

Of course Reddit wasn't the only source - ChatGPT also drew on a range of other sources for this same prompt.

So if you thought Reddit was just for investing in meme coins - think again. There are very serious professional B2B buyers, like Jennifer, the General Counsel above, using Reddit in very niche communities which are extremely highly regarded by AI search tools.

LegalTech is just one example. For B2B software companies in technical niches like developer tools, security, cloud etc - Reddit is likely to be even more active around these topics. Maybe it's a cliché, but it's still the case that more 'techy' buyer personas might be more likely to be spending time on Reddit.

## Why is Reddit often cited by LLMs?

There's a clear logic to why Reddit dominates:

* **Authenticity:** The posts are written by real users in real situations.
* **Nuance:** Reddit threads don't just compare features - they surface gotchas, unfiltered opinions integration headaches and actual outcomes.
* **Freshness:** Active threads are updated, revisited and built upon. LLMs prefer live ecosystems over static blog content.

Reddit's chaotic, unfiltered nature is exactly what makes it valuable. It provides a context and depth that AI models crave.

## Reddit is feeding LLMs - and brands are reacting

Recently, [Reddit Chief Executive told the Financial Times](https://www.ft.com/content/a86fb03a-8781-40b5-a077-1d677e546ecf) "20 years of conversation about everything" and so it's no surprise that LLMs love Reddit.

Multiple advertising and agency executives speaking during this month's Cannes advertising festival told the FT that brands were increasingly exploring hosting a business account and posting content on Reddit to boost the likelihood of their ads appearing in the responses of generative AI chatbots.

In 2024 [Reddit and OpenAI announced a partnership](https://openai.com/index/openai-and-reddit-partnership/) - one of only a few examples of an AI company not just crawling the web and helping themselves to content, but actually investing in a properly licensed commercial relationship. This is a clear sign of how LLMs like ChatGPT value Reddit's content.

There's never been a better time for a B2B software brand to invest in a Reddit strategy.

---

# G2.ai: The Future of G2 in AI Search & B2B Buyer Journeys

Source: https://firstmotion.com/insights/g2-ai-the-future-of-g2-in-ai-search-b2b-buyer-journeys

**By Alex Price | June 25, 2025 | Generative Engine Optimisation | 4 min read**

G2 has recently launched G2.ai, an "AI driven discovery" bolt on that sits awkwardly on top of its once dominant software marketplace. It promises faster conclusions, smarter suggestions and personalised shortlists. That all sounds lovely, until you remember one awkward fact: we can already get that - and more - from the AI assistants living in the tabs we keep open all day. G2 is trying to claim a piece of an AI search future that has already rushed past it.

## The numbers do not lie: G2's traffic is in retreat

Public traffic estimates from tools like Similarweb show G2's monthly visits sliding. While the exact figures vary depending on the data source, the direction of travel is brutally clear: fewer people are actually using legacy reviews/comparison marketplaces to compare software.

Meanwhile Google - the gateway G2 still relies on for visibility and referral traffic - has started injecting AI Overviews directly into results. Ask a buying question like "best CRM for SMB" and you will increasingly see a generated answer that summaries multiple sources, pushes the organic listings halfway down the page and removes the need to click through. Every time that happens, G2 bleeds a little more search driven oxygen.

## AI search tools have already eaten G2's lunch (and data)

G2.ai frames itself as a revolutionary layer, but in reality it is feeding on the same corpus the wider AI world hoovered up months ago. Crawl restrictions were too little, too late. Every major model in market has already ingested millions of G2 reviews, comparison tables and category descriptions. Whether or not that wholesale scraping was ethical or legal is a fascinating legal sub plot - but it has already happened, whether you like it or not.

The result is simple: when I ask ChatGPT, Claude or Perplexity for a software recommendation, they reply with a blended, perspective rich answer that references G2, Capterra, Gartner Magic Quadrants, Forrester Wave reports, analyst commentary and user forums. G2.ai can only echo one of those sources - itself.

## Narrow data, narrow answers

G2.ai insists it provides a "trusted, expert" shortlist. Let us decode that in the context of AI search: it is a shortlist constrained by a single data silo. Even if you believe every G2 review is squeaky clean and free of vendor gaming (unlikely), the most it can offer is a statistical view of its own walled garden. The wider context - total cost of ownership analysis, integration pitfalls, regional support quality, roadmap credibility - lives outside G2's perimeter, and therefore outside G2.ai's reasoning.

Contrast that with your personal AI assistant of choice. It remembers the systems you have already ruled out, the tools you already use, the budget ceiling finance slammed on your last project, the tech stack your architects refuse to touch and even your colleague in IT security that doesn't want any more software tools. That context shapes the next recommendation instantly. G2.ai cannot do that because it does not know you, and because its creators are still dragging legacy review workflows into an age of conversational search.

## A patch on a leaking hull

G2 is not the first incumbent to slap an AI sticker on its product in hope investors can breathe easier. The problem is deeper than branding. Marketplaces that rely on capturing search intent between keyword and vendor website are being disintermediated. AI search answers that intent directly and instantly, inside the viewport where the question was asked. Adding a chat search box on G2's own site does nothing to reverse that macro shift.

G2.ai is, at best, an undersized plug jammed into the hole below the waterline. It might slow the flooding briefly. It will not pump the water back out, and it definitely will not stop the ocean creating new holes tomorrow.

## What does it mean for B2B marketers?

G2 can still be a highly valuable source for visibility. But not for the reason that it used to be.

LLMs love training on review data from sites like G2. We know from lots of our own research that G2 often will be referenced by AI search tools like ChatGPT as an influential source for a B2B software search.

So the value of being on G2 is now that it indirectly plays into your AI search visibility strategy. A strong G2 profile may result in more likelihood of being referenced by an AI tool for a search relevant to your category. So it's still worth investing in your G2 presence - not for direct clicks and referrals, but to optimise for AI search (AEO, LLMO, AIO, GEO - whatever the process of optimising for visibility in AI search is actually called).

## Final thought: An AI Band-Aid on a Sinking Ship

We are not writing G2's obituary - yet. There is still a role for independent reviews of B2B software - but the landscape has changed massively. But for now, it seems G2 is trying to claim a piece of an AI search future that in many respects has already rushed past it.

---

# Is product documentation a gold mine for AI search optimisation & AI native B2B buyer journeys?

Source: https://firstmotion.com/insights/is-product-documentation-a-gold-mine-for-ai-search-optimisation-ai-native-b2b-buyer-journeys

AI assistants are part of the B2B decision making unit. They are buyer enablement co-pilots, supporting buyers from discovery through to contract negotiation.

When ChatGPT, Perplexity or Google AI Overviews answer technical purchase questions they reach past glossy top of funnel pages and often cite deep documentation and user generated content instead. Vendors with clean, structured, easily crawlable product docs could win a disproportionate share of these citations. Treating product/technical documentation as a marketing asset has sometimes been done by teams focusing on long tail SEO, and with great impact. But considering how it fits into large language model optimisation and a generative engine optimisation strategy could now be table stakes.

## The AI Search Shift

Picture this: your CTO opens ChatGPT and types "Which Kubernetes platform offers native GitOps, SOC 2 compliance and a Terraform provider?". In a few seconds the AI tool replies with a ranked shortlist, citations and integration caveats, all without a single Google click. Look at the footnotes or citations and you will see GitHub issues, vendor documentation and Stack Overflow threads outnumbering slick hero pages and keyword stuffed blog posts.

AI search tools are rewriting the visibility rulebook and an asset they prize that is particularly relevant to B2B software companies could well be deep, technically rich documentation that answers very contextual user queries.

## How AI Assistants Cite Product Documentation

We know from our own client research at FirstMotion that often technical product documentation or marketplace integrations are picked up and used as a citation source by LLMs.

Take for example some research into the Contract Lifecycle Management software category we undertook recently.

For an evaluation stage prompt: **_which CLM solution integrates best with Salesforce_**, we saw Salesforce's own AppExchange being referred to as a source by ChatGPT.

And for a similar prompt: **_CLM software that integrates well with Microsoft 365 for in house legal teams_**

Microsoft's own 'Learn' documentation and 'AppSource' integrations marketplace are referenced as sources by ChatGPT.

However, if you type these same prompts as searches into Google, these sources are much less visible on page 1 of the traditional results page - if visible at all.

## The Rise of Deep Pages

So traditional Google search results are still often preferring to surface blog content, whereas LLMs are relying more heavily on deeper, technical documentation.

It seems to be that large language models slice across the entire URL tree, surfacing /docs/, /api/, /help/ and even changelog fragments when those paragraphs align with the user's question. Google AI Overviews can perform similar deep linking, bypassing the homepage entirely. Shallow 'menu level' pillar pages, and sometimes even blog posts, simply do not contain the detail models need to answer certain user queries.

## Technical Documentation as an AI Search Visibility Cheat Code

Structured product docs hit the sweet spot between semantic density and crawlability. They are written for developers, packed with explicit feature labels, and sprinkled with code snippets that double as context tokens. That makes them perfect fodder for retrieval augmented generation.

When a late‑stage buyer asks about tile rendering speed or GDPR compliance the model can quote the exact paragraph that answers it – and Mapbox wins a seat at the shortlist.

## llms.txt – What, Why, How

llms.txt is a technical file that serves as a guide for large language models (LLMs), helping them efficiently discover and prioritise high value content on your site. Think of it as the inverse of robots.txt: rather than telling crawlers what to avoid, it actively tells them what to focus on.

For B2B software companies, this could include API references, integration guides, pricing breakdowns, SLAs, and security documentation. These pages are often buried in subfolders or gated behind obscure navigation paths, which means LLMs may miss them during general crawling. With llms.txt you create a clear index of evergreen, citation worthy content that LLMs can parse and embed into their knowledge. The practical benefits are twofold: it could increase the likelihood of being cited in AI generated responses, and it gives you control over the narrative touchpoints LLMs use when summarising your product.

## Rethinking 'noindex'

Some teams historically cloaked product or technical docs behind noindex for fear of SEO cannibalisation or spreading Google's crawler too thin. That reflex probably now belongs to a pre‑AI era. Today, blocking crawlers may not be as helpful, when the AI models themselves have the capability to consume much more data, and more easily understand what's relevant and what's not.

## Conclusion – The Existential Risk of Staying Invisible

B2B buying is converging around conversational search and AI tools are now buyer enablers in AI native buyer journeys. If a model cannot cite you, you're missing an opportunity.

---

# Who are the leading GEO agencies for AI Search Optimisation and B2B?

Source: https://firstmotion.com/insights/who-are-the-leading-agencies-for-ai-search-optimisation

**By Alex Price | June 18, 2025 | Updated March 18, 2026 | 4 min read**

The world of Generative Engine Optimisation (GEO) and helping brands be more visible in AI search engines LLM results is moving fast, with a number of agencies and consultancies specialising in the space.

This list was first published in June 2025 when the GEO agency space was nascent. Nine months on, the market has moved fast. We have updated where relevant below.

## What to look for in a GEO partner in 2026

The criteria for evaluating a GEO agency have sharpened considerably since mid-2025. Based on what the primary research shows drives AI citation, here is what to verify when speaking to any agency:

- Visibility tracking across ChatGPT, Perplexity, and Google AI Mode - not just AI Overviews, which behave differently (only 13.7% citation overlap between AI Overviews and AI Mode, [Ahrefs December 2025](https://ahrefs.com/blog/ai-overviews-reduce-clicks-update/))
- Methodology around referring domain building - [SE Ranking's study](http://seranking.com/blog/ranking-factors-for-chatgpt/) of 129,000 domains confirmed this is the single strongest predictor of ChatGPT citation (December 2025)
- Strategy for third-party platform presence, domains active on G2, Trustpilot, Capterra, and similar platforms earn 3x more ChatGPT citations per SE Ranking's research
- Content structure approach - sections of 120 to 180 words between headings earn 70% more ChatGPT citations than fragmented content. Q&A format nearly doubles citation probability
- How they handle content freshness - pages updated within the past three months average 6 citations versus 3.6 for untouched content

If first you're wondering: what actually is a 'GEO agency'? Then checkout [our recent post](https://firstmotion.com/insights/what-really-is-a-geo-ai-seo-agency) first.

## Here are some of the top agencies in the AI search / GEO / LLMO space - especially for B2B marketers

### 1. FirstMotion

[FirstMotion](https://firstmotion.com/), based in London but working globally and with a very deep B2B SaaS & software specialism. They've built an entire proposition around AI search and generative engine optimisation and their influence on B2B buyer journeys. They're building the next generation of Organic Growth agency - but don't be fooled by the fact they were founded 2025 - their founder previously built and successfully sold a B2B SEO agency after working with clients like Amazon.

Beyond the deep sector focus and innovative delivery model build, what makes FirstMotion genuinely unique as an [AI SEO agency](https://firstmotion.com/services/ai-search-optimisation) is their technology offering. Their ContextualJourney™ platform sits at the heart of their work, guiding the deep audience intelligence work needed to make an AI search strategy a success. This makes FirstMotion stand out in relation to legacy service driven SEO agencies, and their data led and unique insights into how AI search is changing B2B buyer journeys are a must read for B2B marketers.

### 2. Seer Interactive

Seer are a US headquartered digital marketing agency with a strong focus on SEO. They've been around for a long time, delivering SEO for a mix of clients including some B2B brands.

We're a fan of many of their insights, and the research they are doing into what really works when it comes to AI search.

Their founder wrote a great piece on the [future of search engines](https://www.seerinteractive.com/insights/thought-exercise-what-if-people-stopped-using-search-engines-tomorrow) recently that we recommend reading.

[seerinteractive.com](https://www.seerinteractive.com/)

### 3. iPullRank

Also based in the US, iPullRank are well known in the SEO and content world.

They focus on technical SEO, content engineering, and audience-focused strategies, with the goal of driving results through data-backed, strategic approaches.

The agency, founded by Mike King, has a strong reputation for delivering significant revenue growth for clients through organic search.

[ipullrank.com](https://ipullrank.com/)

### 4. Verto Digital

Verto Digital is a B2B growth-focused digital marketing agency based in Sofia, Bulgaria, that specializes in providing value-driven marketing solutions for growth-stage companies supported by leading technology venture capital funds.

They have been developing and sharing some strong methodologies when it comes to auditing brands in LLMs and to develop a GEO strategy.

[vertodigital.com](https://www.vertodigital.com/)

### 5. WebFX

WebFX are a common feature on lots of agency round ups. They have a broad proposition, but AI search is certainly now part of it.

They are a tech-enabled digital marketing agency that offers a wide range of services to help businesses grow, including SEO, web design, and PPC.

[webfx.com](https://www.webfx.com/)

### 6. SEO Locale

SEO Locale are based near Philadelphia, US.

Their mission is to help businesses of all sizes grow with clear, results-driven digital marketing strategies.

Their website makes reference to lots of 'AEO' and 'SearchGPT' marketing services, so it's clear they've been doing some thinking about how to help brands be successful when it comes to the new era of AI search and the transition from legacy SEO.

[seolocale.com](https://seolocale.com/)

### 7. Passionfruit

Passionfruit are positioned as SEO/GEO agency, going all in on organic growth using AI. They understand B2B software, and know that B2B SEO is different, because B2B buyer journeys are different.

We like the fact they've built a proposition around B2B SaaS, talking to the pain points of B2B marketers.

[getpassionfruit.com](https://www.getpassionfruit.com/)

### 8. Webspero

Webspero are an India based SEO agency that has an AI driven proposition, so if you're looking for an outsourced or overseas SEO agency, they may be worth considering.

They describe themselves as a thriving full-stack digital marketing agency with over 80 efficient team members and have experience working with B2B brands.

[webspero.com](https://www.webspero.com/generative-engine-optimization-services)

---

**About the Author**

Alex Price dropped out of university to turn his part time freelance business that he started as a teenager in his bedroom into an award winning digital agency. He grew the business organically, with no debt or investment, from just him as a 20 year old sole founder to a team of ~35 people and multi-million £ annual revenues and ~25% net profit margins - winning clients like Amazon. He also founded FINITE, a B2B marketing media brand and global membership community for software CMOs. In April 2022, aged 29, he sold the business to a US headquartered strategic buyer, realising the value of lots of hard work and achieving a life changing outcome.

---

# Guide: Generative Engine Optimisation for B2B software & SaaS marketers in 2025

Source: https://firstmotion.com/insights/what-is-generative-engine-optimisation-and-why-should-b2b-marketers-care

## AI Search, Generative Optimisation & The New Search Landscape

For years, many B2B buyer journeys have begun with a Google search. All the way back in 2015, what feels like a lifetime ago now, Google released research stating that 71% of B2B buyer journeys began with a Google search. They also shared that 89% of B2B researchers use the internet during the B2B research process. If you want an indication of just how much things have changed, Google has now deleted this research entirely and we had to use the [Wayback Machine to access it](https://www.thinkwithgoogle.com/consumer-insights/consumer-trends/the-changing-face-b2b-marketing/).

Fast forward 10 years, and it's fair to say that we're going through an enormous shift in terms of how B2B buyers discover solutions thanks to AI. There's lots of statistics showing the rise of AI search tools potentially undermining Google's dominance.

The days of optimising for SEO keywords and chasing rankings, clicks, and traffic are not completely behind us - but it's fair to say things are changing quickly.

AI tools like ChatGPT, Claude, Perplexity, and Google's AI Overviews are in many respects the new web browser. They have become part of the B2B decision making unit. They sit alongside B2B buyers, acting as buyer enablement co-pilots across the entire length of the B2B buying journey - not just the initial search or discovery moment.

And so B2B marketers need to shift their mindset - unlike Google, AI assistants aren't just an acquisition channel. They are now trusted partners to B2B buyers as they discover, research, compare, procure and negotiate.

Across many clients and analytics sources, we're seeing clicks and traffic from Google's search results page falling, whilst referral traffic from AI tools begins to rise. In May 2025, [Google announced AI Mode in the US market](https://blog.google/products/search/google-search-ai-mode-update/), giving users an option to turn off the traditional list of blue links known as the Search Results Page entirely - and instead use AI as default. It's clear that the rate of change is faster than even lots of SEO experts imagined.

You don't have to look far to find many in the marketing and search world asking a big question: [is this the end of clicks and traffic to websites](https://searchengineland.com/google-ai-overviews-kill-click-456453)?

## Is Buyer Search Behaviour Shifting from Google SERPs to AI Assistants?

At Google's I/O 2025 event, they acknowledged that users are searching very differently in AI tools compared to the traditional search bar.

It appears a psychological value exchange has already been established. Users are searching much more deeply - on average providing [2-3 longer search queries in AI tools](https://searchengineland.com/google-ai-overviews-kill-click-456453). In return, they are expecting deeper, more contextual, more personalised search results from AI tools.

A few years ago, a B2B buyer searching for a new marketing automation software might have begun their search with a Google of a term like 'marketing automation software'. But now, they are turning to tools like ChatGPT and providing much more information in their search. They know that if they provide more context, they will get better results.

AI search tools like ChatGPT, Claude, Perplexity, and Google's AI Overviews are now acting as *research co-pilots* across the entire buyer journey – not just tools for initial discovery. These platforms are synthesising answers, recommending vendors, and shaping perceptions before a buyer ever lands on your website. And so it's fair to ask - are we heading for a future where websites aren't built for users - they are built for AI.

> By 2028, brands' organic search traffic will decrease by 50% or more as consumers embrace generative AI-powered search. [**Gartner**](https://www.gartner.com/en/newsroom/press-releases/2023-12-14-gartner-predicts-fifty-percent-of-consumers-will-significantly-limit-their-interactions-with-social-media-by-2025)

And so AI Search and a clear generative engine optimisation strategy has to be a priority for any B2B marketer. Clearly the traditional search results page is changing dramatically, along with buyer behaviour. And with it, B2B marketers must shift their strategy too.

## What's the value of Generative Engine Optimisation (GEO)?

Optimising for AI search tools and GEO can drive success for B2B SaaS & software businesses across:
- **Authority** in competitive, research led software categories
- **Speed** across complex sales cycles by answering objections earlier
- **Conversion** through prompt aligned content and message reinforcement
- **Revenue** by generating higher quality leads from better aligned buyer journeys

Unlike traditional SEO content strategies, we focus on real business outcomes - visibility that leads to opportunity creation, not vanity metrics.

In this guide, we'll unpack what GEO really means for B2B marketers, why it matters **now**, and how to approach it with a full funnel, pipeline first mindset.

We believe in a B2B marketing context, GEO is:
- Prompt driven, not keyword driven
- Built for LLMs, not legacy search engines
- A process of buyer journey orchestration, not just a measure of brand visibility

It builds on some foundations of SEO and AIO, but expands into a full funnel, buyer centric model designed to convert attention into pipeline and optimise the entirety of AI-native B2B buyer journeys.

## Why GEO Matters for B2B Marketers

Google itself is shaking up how it delivers results, despite being a business traditionally reliant on blue links and advertising revenue. AI Overviews are growing quickly in visibility at the top of the search results page now.

Many B2B purchase journeys are long, complex, and deeply research led. They often involve:
- Multiple decision makers
- Large average contract values
- Objection handling
- Competitive comparisons

AI tools are becoming buyer enablement co-pilots - used not just for discovery, but for:
- Comparing solutions
- Surfacing objections
- Exploring pricing models
- Analysing trade offs
- Running RFP processes
- Negotiating terms

This creates an opportunity for B2B marketers to:
- Reach buyers earlier in their journey
- Actively shape category narratives
- Build brand preference before outreach

We're also seeing that traffic and engagement from AI search can be higher converting. Why might referral traffic from AI tools like ChatGPT be more likely to convert than traffic from Google?
- Lower competition in responses if your brand is visible
- Higher buyer intent, carrying out deeper research
- More specific, context aware buyers

The team at Seer Interactive shared a website that was seeing referral traffic from ChatGPT converting at 15.9% - compared to 1.76% from Google.

In short, GEO may help you meet buyers before your competitors do and convert them faster.

## The Dark Funnel of AI Search

One of the biggest challenges for B2B marketers when it comes to AI search is analytics, attribution and tracking.

AI tools could well be making the so called 'dark funnel' even darker, because often AI tools might not include a link to a product they recommend.

## How LLMs like ChatGPT 'Decide' What Content to Show (B2B Software Context)

Generative engines don't really rank content in the same way search engines do. They synthesise answers, pulling from a mix of structured and unstructured data to produce coherent, context aware outputs.

### A. Third Party Signals and Data Sources

In B2B software, visibility is often influenced by brand mentions and references by sources like:
- **Review platforms** like [G2](https://firstmotion.com/insights/g2-ai-the-future-of-g2-in-ai-search-b2b-buyer-journeys), Capterra, and TrustRadius
- **Analyst citations** from firms like Gartner and Forrester (e.g. Magic Quadrant inclusion, Wave reports)
- **User generated content (UGC)** on platforms like [Reddit](https://firstmotion.com/insights/reddit-a-critical-part-of-an-ai-search-visibility-strategy), Quora, Stack Overflow
- **Media coverage and [backlinks](https://firstmotion.com/insights/do-backlinks-still-matter-for-ai-search-geo)** from high authority publications or industry blogs

These sources signal authority and credibility, shaping what LLMs synthesise when responding to prompts like:
- "Best procurement platforms for enterprise buyers"
- "[Vendor A] vs [Vendor B] in 2025"

### B. Your Brand's Own Content and Website

However, your own site still plays a critical role in GEO. Generative engines can pull directly from your domain, especially when:
- Content is well structured and answers specific, high intent prompts
- Pages are cleanly tagged, with clear headers, lists, tables, and schema markup
- You provide FAQs, comparisons, and product use cases that align to buyer intent
- Your content is kept fresh, unique, and crawlable

Examples of content types that boost visibility in AI responses:
- In depth solution pages answering niche buyer questions
- Industry specific landing pages talking clearly to segmented ICPs or buyer personas
- Comparison pages structured around real world decision making
- Pricing breakdowns and total cost of ownership explainers
- Customer success stories aligned to verticals or use cases

### C. Technical Site Optimisation for GEO

Optimising your site for generative engine retrievability can have some similarities with traditional SEO, but here is an overview:
- Structured data and schema markup to help LLMs extract and understand key facts, attributes, and comparisons
- Clear content hierarchy and internal linking, enabling better semantic context
- Use of tables, bullets, FAQs, and TL;DR summaries that are easily summarised or lifted into responses - content chunking
- Sitemap and crawl accessibility, including proper robots.txt configuration
- Deployment of a llms.txt file to signal AI access permissions (e.g. whether your content can be used for retrieval or training)
- Ensuring AI crawlers can reach your site and aren't blocked by tools like [Cloudflare](https://www.cloudflare.com/en-gb/press-releases/2025/cloudflare-just-changed-how-ai-crawlers-scrape-the-internet-at-large/)

## GEO vs SEO: What's the Difference for B2B marketers?

| Factor | SEO | GEO |
|--------|-----|-----|
| **Approach** | Keyword driven | Prompt driven |
| **Output** | Google rankings | Synthesised answers |
| **Metric** | Traffic & CTR | Buyer visibility & retrievability |
| **Scope** | TOFU focused | Full journey orchestration |
| **Tactics** | On page SEO & backlinks | Ecosystem visibility & source alignment |

## What is a B2B specific GEO / AI Search strategy? Aligning Prompts to the B2B Buyer Journey

GEO isn't just about ranking for "best software" prompts. It's about shaping how your brand shows up across the entire B2B journey, and that begins with deeply understanding your audience, their needs and pain points:

### Example Top of Funnel B2B buyer prompts:

- "Best [category] platforms for [industry]"
- "Best ways to automate [process] workflows"
- "How do companies manage [challenge] effectively?"
- "Alternatives to using spreadsheets for [process]"
- "What software could help us improve [challenge]?"

### Example Middle of Funnel B2B buyer prompts:

- "[Vendor A] vs [Vendor B] vs [Vendor C] – which is best for a [type] business with [specific challenge]?"
- "Best [specific software] for insurance companies"
- "What key features should we look for in a [category] platform?"

### Example Bottom of Funnel B2B buyer prompts:

- "What are pricing tiers and contract terms for [Vendor A]"
- "Does [Vendor A] integrate with [existing tool]?"
- "What are red flags to watch out for when buying [solution]?"
- "What contract terms should I look to negotiate for a [solution]?"

Each prompt is a chance to:
- Build trust
- Guide decisions
- Pre handle objections
- Accelerate pipeline velocity

And that's why we believe GEO is not just a visibility strategy - it's a way of optimising the full length of the buyer journey in the new era of AI native buyer journeys.

## Introducing the ContextualJourney™ Platform

At [FirstMotion](https://firstmotion.com/), we go beyond basic GEO. Our proprietary technology platform, ContextualJourney™, is designed for B2B software companies selling in complex, considered, competitive and often high value B2B sales journeys.

It includes:
1. **Audience Intelligence**: we believe deep buyer understanding has to sit at the heart of a successful GEO strategy
2. **Prompt Mining**: using lots of data enrichment, AI and billions of B2B buyer data points, we can mine prompts that B2B buyers might *actually* be using
3. **Content Mapping**: Aligning prompts to buyer journey stages, and design narrative architecture
4. **And a number of other features**: we're building lots of exciting functionality into our platform, redefining what the [B2B SaaS AI SEO agency](https://firstmotion.com/services/ai-search-optimisation) of the future looks like

We also use a number of other third party best in class analytics platforms for measuring brand visibility within AI tools.

## Common Misconceptions About GEO & AI Search

As Generative Engine Optimisation becomes a more recognised term, it's important to address some of the common misunderstandings that can lead B2B marketers astray.

**"It's just SEO for AI"** - This is perhaps the most common misconception. While [GEO borrows some ideas from SEO](https://firstmotion.com/insights/what-really-is-a-geo-ai-seo-agency) and there is certainly some overlap in terms of tactics, it requires a shift in both mindset and strategy. Generative engines are not search engines. They don't display a list of links or necessarily operate on classic ranking signals. Instead, they synthesise answers and present a single authoritative response. GEO is about orchestrating visibility throughout the buyer journey - not just at the top of it.

**"We just need more AI content"** - Flooding your site with generic, AI generated content won't make you visible inside LLMs. What matters is content that aligns with buyer intent and the prompts they're using. GEO requires structured, high quality, contextual content that is both retrievable and referenced by trusted sources, based on deep audience intelligence.

**"It's too early"** - Buyers are already using AI tools to explore options, compare vendors, and evaluate pricing - often in ways that never touch your website. If you're not thinking about how to be visible in those journeys now, you're already behind.

If you're a B2B marketing leader looking to drive real outcomes in the new era of AI native B2B buyer journeys, we think it's time to start thinking about a generative engine optimisation strategy.

Check out our [GEO terms glossary](https://firstmotion.com/insights/the-geo-glossary-key-terms-b2b-marketers-should-know-about-ai-search-optimisation) if you're still trying to get your head around the space.

---

# What types of AI prompts should B2B software companies optimise for?

Source: https://firstmotion.com/insights/what-types-of-ai-prompts-should-b2b-software-companies-be-aiming-to-appear-in

**By Alex Price | June 12, 2025 | Generative Engine Optimisation | 4 min read**

Generative AI tools like ChatGPT, Claude, and Perplexity are fundamentally reshaping how B2B buyers research and evaluate B2B software vendors. Traditional keyword based search is no longer the only gateway to discovery. Increasingly, buyers are turning to AI assistants for answers to their most pressing questions - not just for initial top of funnel research, but across the entire modern AI assisted buyer journey.

So prompt visibility is becoming as important as search visibility has been for a long time. For B2B software companies, it's not just about ranking on Google anymore. It's about being the answer when a buyer types a high intent question into an LLM.

But what kinds of prompts should you actually be aiming to appear in? How do you know which ones matter across the buyer journey? And what can you do to make sure your brand is part of those conversations?

## Why prompt visibility matters for B2B software marketers

Enterprise software buying journeys can be long, complex, and often involve multiple stakeholders across technical, commercial, and executive functions. These buyers aren't typing "CRM software" into Google and clicking the first few links anymore. They're asking AI tools nuanced questions like:

- "What's the best CRM for a fast-growing B2B sales team?"
- "How does HubSpot compare to Salesforce for integrations?"
- "What are the hidden costs of switching marketing automation tools?"

They might also include details of their company size, team size and other company information - B2B buyers know that the more information they provide in a search, the more contextual, personalised results they are likely to get back.

These prompts are replacing many of the early and mid stage searches that marketers used to target with blog content and SEO optimised landing pages. And because LLMs often return a smaller number of high confidence results compared to the traditional search results page, the cost of not appearing in those answers is growing.

Prompt visibility influences brand awareness, authority, and even conversion outcomes. It can shape a buyer's shortlist before they ever visit your website.

## The role of audience intelligence

To build an effective prompt strategy, you can't just guess what buyers might be asking. You need a deep, structured understanding of your ideal customers: who they are, what they care about, and how they think and speak. What their pain points are.

This is where audience intelligence becomes critical.

Different roles ask different questions. A CTO evaluating a data platform might focus on architecture and compliance. A RevOps lead might be interested in integrations, reporting, and ease of deployment. A procurement manager could be looking for value, contract flexibility, and proof of ROI.

That means your prompt strategy must be segmented and persona aware. The more granular your understanding of your ICPs (Ideal Customer Profiles), personas, pain points and use cases, the more accurately you can anticipate the prompts they're putting into tools like ChatGPT.

At FirstMotion, we use our own audience intelligence technology platform, ContextualJourney™ to help us guide this work. With millions of B2B buyer data points, intent data, enrichment and AI integrations, it enables us to 'prompt mine' highly effectively as a generative engine optimisation agency.

Deep audience intelligence is the foundation for any generative optimisation or AI search visibility strategy.

## Mapping prompts to the B2B buying journey

One of the most effective ways to plan your prompt targeting is to structure it around the B2B buying journey. This helps you ensure you're visible not just when someone is learning, but when they're evaluating, comparing and deciding.

At FirstMotion, we use a process that translates traditional SEO keyword research into prompt archetypes tailored to each stage of the funnel. Here's a simplified view:

### Top of Funnel (Awareness & Exploration)

These prompts reflect early stage curiosity or problem awareness.

- "What are the best tools for managing spend in a SaaS business?"
- "Alternatives to [Vendor] for procurement software"
- "How can marketing teams measure content ROI?"

### Middle of Funnel (Consideration & Evaluation)

Here, buyers are starting to compare vendors and shortlist options.

- "[Product] vs [competitor] comparison"
- "Best [category] platforms for [sector] companies"
- "Must have features in [category] software"

### Bottom of Funnel (Decision Support & Objection Handling)

These prompts signal serious buying intent and final-stage decision support.

- "Customer reviews of [product] for global teams"
- "Common problems during [vendor] implementation"
- "Is [tool] worth it for companies under 500 employees in [sector]?"

Each of these prompt types maps to a specific kind of buyer intent. There are more opportunities than ever to be present and to have some kind of influence.

## Measuring prompt performance

Tracking prompt visibility is still in its early days, but it's evolving fast. We use a range of tools and data sources to help marketers measure which prompts they're showing up in, how frequently they're being mentioned across different LLMs, and how competitors are performing.

We recommend building a Prompt Map - a working database of high value prompts segmented by buyer stage and persona. Then, track where and how your brand appears, and use this insight to shape content creation, improve prompt conditioning, and even influence how your brand is described on third party sources. Peec AI is our preferred analytics and visibility platform.

Scoring prompt visibility is also a valuable way to demonstrate progress and ROI in an AI first go-to-market motion.

Prompt visibility isn't just a technical challenge - it's a strategic opportunity.

For B2B software companies, appearing in the right prompts means being present at the precise moments buyers are shaping their understanding of a category, evaluating vendors, or trying to make a final decision.

If you want to win in this new landscape, start with audience intelligence. Understand who your buyers are, what they ask, and how they think. Then, work systematically to translate those insights into a prompt strategy that ensures your brand is part of the conversation.

---

**About the Author:**

Alex Price is founder of FirstMotion. He previously built and sold a B2B digital agency, founded FINITE (a marketing community for software CMOs), and now helps B2B software companies build AI search visibility strategies at scale.

---

# Ranked #1 When ChatGPT Picks A GEO Agency

Source: https://firstmotion.com/work/firstmotion-ai-search-proof

## Why we're including ourselves

We don't love it when agencies make themselves the case study. It's usually a sign they don't have real client results to show you. We'd rather you judge us on the [client work elsewhere on this page](/work).

But there's one claim we make that's easy to check for yourself, and dishonest not to show: that we practise what we sell. So this is the one exception, presented the same way we'd present any client's results, with the same honesty about what did and didn't work.

## What we did

We ran our own playbook on ourselves, the same four steps we run for every client:

1. **Prompt mapping.** We didn't try to rank for every GEO-adjacent query out there. Using the same ICP work we run for clients, we identified the small set of prompts our actual ideal client types when they're close to a decision, not "best marketing agency", but the specific, comparison-shopping questions a B2B SaaS company asks when it's evaluating GEO agencies, like "best GEO agency for B2B SaaS in London." Narrower, on purpose.
2. **Daily tracking.** We track our own visibility against those prompts every day, across ChatGPT, Google AI Overviews and Claude, the same dashboard we run for clients.
3. **Diagnosis.** We looked at what was actually driving citations, our own site content, our Clutch review, third-party mentions, and where the gaps were.
4. **The fix.** We closed those gaps: clearer entity and schema signals on our own site, content that directly answers the comparison questions buyers ask, and building the third-party footprint AI models pull from.

No trick prompts, no cherry-picked model, no incognito tab gymnastics. Just the same process, pointed at ourselves.

## Why narrow, not everything

We could have gone after visibility for every GEO or AI search query going. We didn't. A brand that shows up for "what is GEO" attracts curious readers. A brand that shows up for "best GEO agency for B2B SaaS in London" attracts a B2B SaaS company in London who's already decided they need a GEO agency, our exact client.

That's the same targeting we do for every client: find the prompts your actual buyer types, not the ones that just get volume. It's also why the enquiries this drives tend to already be a strong fit before we've had a single call, and a real part of why we've been busy.

## The result

Ask ChatGPT a version of "what's the best GEO agency for B2B SaaS in London" today, and FirstMotion comes back as the top answer. Below are three separate, unedited screenshots, taken days apart, in different chat threads and a ChatGPT project, all landing on the same answer.

<img src="/proof/chatgpt-best-fit.png" width="1484" height="1110" alt='ChatGPT answering "What is the best GEO / AI Search agency for B2B SaaS brands in London?" with FirstMotion as its top recommendation' />

Asked to produce a ranked shortlist instead of a single answer, FirstMotion still comes out on top, ahead of seven other named London agencies:

<img src="/proof/chatgpt-ranked-list.png" width="1484" height="1470" alt="ChatGPT ranking FirstMotion #1 out of seven London GEO and SEO agencies for B2B SaaS" />

And inside a dedicated ChatGPT project built to research this exact question, the same result:

<img src="/proof/chatgpt-project-thread.png" width="1484" height="1394" alt='A ChatGPT project thread titled "Best B2B GEO Agency London" recommending FirstMotion as the strongest fit' />

## The honest caveat

Every one of those answers also noted, correctly, that we're a newer, smaller consultancy with a limited public case-study footprint. We're not going to pretend otherwise, it's true, and it's exactly why this page exists: to add to that footprint honestly, one real result at a time.

If you want the same process run on your own AI visibility, that's what we do for clients every day.

---

# #1 Share of Voice, 12 Months Running

Source: https://firstmotion.com/work/legal-tech-ai-search

## The problem

The client competes in one of the most contested categories tracked in AI search: well-funded rivals run aggressive content campaigns, comparison pages, "best of" listicles, rapid publishing bursts, specifically engineered to win the citations AI engines pull from. Standing still wasn't an option.

## What we did

We rebuilt the foundation first, leading strategy on the client's new website build and launch, then wiring in the technical SEO and entity/schema work that gives AI engines a clean, structured signal of who the client is and what it does. On top of that: daily content publishing, ongoing page optimisation, dedicated comparison pages, and off-site work to build the third-party citation footprint AI engines pull from.

## The outcome

Twelve months on, the client holds the #1 position in its category on every metric that matters, visibility, share of voice, and average mention position, and has held it every single month, in one of the most competitive categories we track. Share of voice grew from 7.8% to 10.3%, and average mention position improved to 2.29, the strongest placement of any tracked brand in the category.

---

# #1 Share of Voice in 5 Months

Source: https://firstmotion.com/work/wealth-manager-ai-search

## The problem

The client is a UK discretionary wealth manager, but AI engines didn't know that. ChatGPT, Perplexity and Google AI Overview were confusing them with an unrelated company of a similar name, and third-party sources were mischaracterising them as an automated "robo-advisor" rather than a discretionary wealth manager. On top of that, competitors simply had more citable content for AI engines to pull from: an early audit found the client accounted for almost none of the tracked AI citations in their category.

## What we did

We ran entity disambiguation work to separate the client from the confused entity, structured data and schema review, and a coordinated correction effort with the third-party sources AI engines were citing for the inaccurate "robo-advisor" description. Alongside that, we built a content programme structured specifically for how AI engines retrieve and cite sources: answer-first articles on the exact questions prospective clients ask, each backed by comparison tables, named reviewers and FAQ schema.

## The outcome

Within five months, the client held the #1 share of voice among its tracked category competitors, and hasn't been displaced since. Citation rate more than doubled, visibility in AI answers climbed from 15% to over 55%, and Google AI Overview impressions grew 116% in the first thirteen weeks alone. Eight articles are live, with two already ranking in the top 10 sitewide for AI Overview visibility from a standing start.