How Agentic AI Is Changing the B2B Buying Unit

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 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 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

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 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.

Here's how AI agents distribute across the buying unit today:

Role in buying unitWhat the AI agent doesProcurement managerScans workflows, drafts RFPs, monitors supplier riskTechnical evaluatorRuns simulated tests, generates unbiased feature matricesFinance leadValidates contract pricing, defines spend thresholds, flags anomaliesEnd userSubmits natural language queries, receives tailored recommendationsComplianceEmbeds ESG criteria, preferred supplier lists, and regulatory guidelines

Agentic commerce and the new buyer journey

Buyer behavior has shifted decisively. Forrester's Buyers' Journey Survey 2025 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 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, 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 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 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

Salesforce's State of Sales 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.

AI tool typePrimary functionImpact on sales processAI assistantDrafts emails, summarises calls, automates follow-upsFrees reps from manual tasksAI sales agentMonitors buyer behavior, triggers outreach, manages lead engagementRuns sequences autonomouslyAgentic AI solutionAccount planning, territory design, quota setting, deal managementStrategic-level decision supportProcurement AI agentVendor discovery, RFP generation, order submissionRemoves humans from routine purchasing

Forrester predicted 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 and how citation rates translate into pipeline, the data makes the case clearly. It's also worth reading why a16z backs GEO to understand why the smartest capital in tech treats this as a structural shift.

What agent ready actually means

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 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.

RequirementWhat it meansWhy it mattersStructured product dataSpecs, pricing rules, technical details in machine-readable formatAgents can't evaluate what they can't extractAnswer-first contentBuyer questions answered directly in the first 100 wordsAgents score pages that lead with the answerThird-party validationBrand mentions on authoritative external pagesAI cross-references these to establish credibilityLive data feedsCurrent market indices, inventory, logistics, dynamic pricingAgents need real-time data to make accurate decisionsTech stack integrationReads and writes across ERP, CRM, supply chain softwareEnables end-to-end autonomous procurementESG and compliance logicCorporate ESG criteria and regulatory guidelines in agent policyEnsures compliant purchasing decisions at scaleAudit trailHuman-readable log of every vendor or purchase path decisionBuilds operational trust in ai outputs

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 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 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 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, 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: 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 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 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.

Ready to make your brand agent ready?

At FirstMotion we build AI search visibility for B2B software companies through VC partnerships, combining our PromptPath™ framework with deep buyer journey intelligence to ensure your brand is present when AI agents build the shortlists your buyers rely on. If you want to understand where your brand stands in AI procurement workflows right now, book a discovery call and we'll show you exactly where the gaps are.

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 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. Book a call to see where your brand stands today.

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 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

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.

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.

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.

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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. ining 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

FactorTraditional SEOAI search optimisationPrimary goalRank on search results pagesEarn citations in AI-generated answersSuccess metricKeyword rankings, CTR, organic trafficAI visibility, share of answer, citation frequencyContent formatKeyword-optimised pagesStructured, modular, answer-first contentAuthority signalsBacklinks and domain authorityE-E-A-T, named experts, cited sourcesQuery typeShort keyword phrasesLong-tail, conversational promptsStructured dataHelpful but optionalEssential 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.

Ready to audit how your content stacks up for AI discovery? See our content strategy approach.

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' 548,534-page analysis 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 typeBest used forAI search benefitFAQ schemaQ&A content, help pagesDirectly feeds AI answer extractionHowTo schemaStep-by-step guidesStructured process content AI engines preferArticle schemaBlog posts, editorial contentSignals content type and authorityProduct schemaProduct pages, comparisonsEnables AI to reference specific offeringsPerson schemaAuthor pages, biosStrengthens E-E-A-T and named entity signalsOrganisation schemaAbout pages, home pagesBuilds 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 citationsand 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. Understanding our GEO consultancy services is one of the most important steps any B2B marketing team can take right now. If you're not sure where your content stands, our AI search visibility audit is a practical starting point.

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. You can read more about building your brand's authority signals in our GEO strategy guide.

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

MetricWhat it measuresWhy it mattersAI citation frequencyHow often AI tools reference your contentPrimary indicator of AI visibilityShare of answerYour brand's presence in AI responses for target queriesTracks competitive AI search positionReferral traffic from AI platformsVisits arriving from ChatGPT, Perplexity, GeminiQuantifies AI search as a traffic source in Google AnalyticsBranded search volumeSearches for your brand nameSignals awareness driven by AI summaries and mentionsGoogle AI Overviews appearancesPresence in Google's AI-generated summariesTracks visibility in the most widely used AI search formatTraditional rankingsPosition in standard search resultsRemains 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.

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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, 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 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 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 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, 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 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.

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.

What good looks like: a GEO Score benchmark

Beyond individual metrics, a GEO Score 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 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 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 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% 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 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, we help portfolio companies make this shift with confidence. Book a call 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.

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 to discuss what's achievable in your category.

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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), 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, 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.

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, 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, buyers are already around 70% through the decision-making process by the time they first reach out to a vendor. G2's research 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 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, 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.

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, including Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO).

Understanding GEO

Generative Engine Optimisation 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 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 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, 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 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.

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 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 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.

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 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 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.

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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 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: 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: 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, 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.

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: 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:

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.

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 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 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?

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 optimisation audits: 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.

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, 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
• 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, which focuses on ranking signals and backlinks rather than how large language models select and synthesise information.

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.

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. 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 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 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 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 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 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 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 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 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 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 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 AIai 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 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.

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

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

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

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.

This article was updated on 18th March 2026

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 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 has written a new blog post, claiming that AI within its search products is resulting in happier users and better quality traffic for website owners.

The post published on Google's search blog is written by Google's VP, Head of Google Search, Liz Reid. 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, 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, 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 clients, and will be sharing some of our own data soon.

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.

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 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 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: 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.

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 quality of traffic coming from AI - read more here.

The increase in use of AI powered tools like ChatGPT, Perplexity, Claude, and Google’s AI Overviews and AI Mode 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, 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.

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 (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 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, 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 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.

This article was updated on 18th March 2026

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.

What are 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

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.