Generative Engine Optimisation, or GEO, is the work of making a brand easy for AI answer engines to discover, understand, verify and cite. It extends SEO rather than replacing it: strong crawlability, useful content and authority still matter, but the unit of visibility shifts from a ranked page to a cited answer, source or recommendation.
Why GEO has become a commercial problem, not a marketing trend
B2B discovery is moving upstream. A buyer can now ask an assistant to compare providers, explain a category, shortlist vendors and challenge a recommendation before a sales form is ever opened. That changes the moment at which a brand enters the buying process. If the assistant does not know enough about you to name you, your conventional funnel may never get a chance to work.
The term Generative Engine Optimisation was formalised in research published at KDD 2024. The researchers described a new optimisation problem: instead of competing only for a position in a ranked list, publishers also compete for visibility inside a generated answer assembled from multiple sources. Their experiments showed that content changes could materially alter visibility, but also that the effect varied by domain. That last point matters. There is no universal GEO trick.
For a B2B firm, the commercial question is simpler: when a buyer asks an AI system the questions that precede a purchase, are you part of the answer set?
GEO and SEO share a foundation
The strongest GEO programme still begins with ordinary search discipline. Google states that its existing SEO best practices remain relevant to AI Overviews and AI Mode, and that there are no special technical requirements or secret AI markup required to appear. Pages still need to be crawlable, indexable, useful and internally connected.
The difference is what you optimise the content to do once it is found. Traditional SEO asks whether a page deserves to rank for a query. GEO also asks whether a specific passage can be extracted cleanly, whether the brand and claim can be verified elsewhere, and whether the source adds something distinctive enough to cite.
That is why generic content becomes even less useful in an answer engine. A paragraph that merely repeats the consensus gives a model no reason to attribute the idea to you.
The five layers of a practical B2B GEO programme
- Prompt and demand mapping. Start with the questions a real buying committee asks, not a list of short keywords. Include category questions, comparison prompts, implementation questions, risk questions and vendor-shortlist prompts.
- Technical accessibility. Make sure search crawlers can access the pages. For ChatGPT search specifically, OpenAI advises publishers not to block OAI-SearchBot if they want their content discoverable and citable in search results. For Google AI features, the normal Search technical requirements apply.
- Extractable content. Put direct answers near the top of relevant sections. Use descriptive headings, explicit entities, short definitions, comparison tables where useful, and language that makes the subject of each claim unambiguous.
- Evidence and corroboration. A company saying it is excellent is marketing. Independent mentions, case studies, named proof, original data and credible third-party citations give an answer engine something it can verify.
- Measurement. Track both visibility and commercial outcomes. A mention that never creates qualified traffic or assists pipeline is not the same thing as a recommendation that sends an in-market buyer to the site.
What not to confuse with GEO
GEO is not a licence to produce hundreds of thin pages for every conversational variation of a query. Google explicitly warns against scaled content that adds little value. Nor is it a matter of adding an llms.txt file and declaring the site AI-ready. That file may be useful in some ecosystems, but Google says no special AI text file or schema is required for its generative Search features.
It is also not reputation management disguised as SEO. Third-party evidence matters, but only when the evidence is real. Manufactured forum posts, fake reviews and synthetic consensus create brand risk and are a weak foundation for a channel built around trust.
A simple GEO operating model
Choose twenty to forty commercially relevant prompts. Baseline which brands and sources appear across the AI systems that matter to your market. Group the gaps into three buckets: technical access, on-site answer quality and off-site evidence. Fix the narrowest, highest-intent set first, then repeat the same inspection monthly.
The objective is not to be mentioned for everything. It is to become a reliable source and credible option for the questions that occur immediately before a buyer creates a shortlist.
Frequently asked questions
What is Generative Engine Optimisation?
Generative Engine Optimisation is the practice of improving how often and how accurately a brand or source appears in AI-generated answers. It combines SEO foundations, machine-readable clarity, useful answer-first content and credible external evidence.
Does GEO replace SEO?
No. SEO remains foundational. Google explicitly says its existing SEO best practices continue to apply to AI Overviews and AI Mode. GEO adds an optimisation layer for extraction, citation, entity clarity and recommendation visibility.
Do I need llms.txt to rank in AI search?
Not for Google AI features. Google says there is no requirement for a special AI text file or special schema. For ChatGPT search, the more important technical check is that OAI-SearchBot is not blocked if you want pages discoverable.
How should a B2B company measure GEO?
Track prompt coverage, mentions, cited pages, sentiment and referral traffic, then connect those signals to leads, stage progression and pipeline. Visibility is useful only when it contributes to commercial outcomes.