The B2B GEO playbook: getting named in the answers your buyers ask AI

What is a generative engine optimisation strategy and how do you build one?

A generative engine optimisation strategy is a programme that moves your company from absent to named in the AI answers your buyers read. It runs in four steps: audit where AI places you today, build the content models draw on, earn the citations that build trust, then re-score monthly to track movement.

Most B2B teams treat this as a content project with a deadline. It is closer to a standing position you hold. The questions buyers ask shift, the models update, and a rival can surface in an answer you used to own without anyone telling you. A strategy that ships ten articles and stops will lose ground within a quarter. The four steps below are built to run on a loop, and the loop is the point.

The rest of this piece walks through each step applied to regulated B2B, gives you a 90-day shape to start from, and follows one anonymised programme through the full arc so you can see what movement actually looks like.

Isn't this just SEO with a new name?

No. The foundations overlap, the job and the metric do not. SEO earns a position in a list of links and is measured on rankings and clicks. Generative engine optimisation earns your name inside the AI answer that now sits above those links, and is measured on how often you are the recommendation. You can hold position one and still lose the buyer who stops reading at the summary.

Here is the line that matters for a strategy. The work that earns a high ranking is necessary groundwork, crawlable pages and structured, authoritative content. It is not sufficient on its own. Models recommend the name they have seen cited and trusted most often across the sources they read. Earning that takes a different kind of work, and very few teams in regulated categories are doing it yet. That gap is the opening.

If you want the full comparison, the honest answer for B2B teams sits in is GEO the same as SEO. The short version: ranking and citation are different jobs, and a strategy has to be built for the second one.

Step one: audit where AI places you today

The audit asks the AI engines your buyers use the exact questions they ask, then records where you stand on each one. Three outcomes per question: AI names you, AI names a competitor, or the question sits open with no clear leader. The open questions are the fastest wins. The competitor-owned ones are where the work is hardest and the stakes highest.

A regulated buyer rarely asks a single question. They ask a sequence. They want to know which providers are compliant with the relevant framework, whether those providers integrate with the systems they already run, and what track record each has in their sector. Your question set has to mirror that real sequence rather than a list of keywords. Forty to sixty buyer-shaped questions is a workable starting set for most B2B categories.

For the end buyer, this is invisible. They open ChatGPT, Gemini or Perplexity, ask which provider fits their situation, and act on the names that come back. The audit is the only way to see what they are being told about you before they ever reach your sales team. Treat the score as your baseline. Everything after is measured against it.

Step two: build the content the models draw on

Content for a generative engine optimisation strategy is written to answer real buyer questions in the format models extract from when they form a recommendation. That means self-contained answers near the top of the page, clear structure, named sources, and language that matches how your buyers phrase the question rather than how your marketing team would.

The audit tells you what to write first. You do not start with the questions where a rival is entrenched. You start with the open ones, where the model has no settled answer and a well-built page can become the source it reaches for. In regulated sectors that often means the specific compliance and integration questions generic competitors skip, because answering them properly requires sector fluency they do not have.

This is the content half of the work, and on its own it shifts your position slowly. A page can be the best answer to a question and still not get cited if the models have no external reason to trust the name behind it. That is why the next step matters as much as this one. For the mechanics of writing extractable pages, how to get cited in Google AI Overviews and AI answers goes deeper than there is room for here.

Step three: citation building

Citation building earns the external references and structured signals that teach models to trust your name. Models weight sources. A page on your own site is one signal. A mention in a publication the model already trusts counts for considerably more. So does clean structured-data markup it can parse, and a name that stays consistent across the places it reads. Those signals compound into the trust that turns a page into a cited source.

This is the step most "GEO" advice skips, because it is the hardest to do and the hardest to fake. There is no paid placement and no trick that puts your name in an answer. The model recommends what it has seen cited and trusted most often, so the work is earning genuine references over time and making your existing presence legible to a machine that reads structure before prose.

For a regulated B2B company, the raw material is usually already there: certifications, framework alignments, integrations, real customer outcomes. Most of it is written for humans and invisible to models. Citation building makes it parseable and gets it referenced where the models look, so the trust you have already earned starts counting in the answer.

Step four: re-score monthly

Every month, re-scoring re-runs the original question set and scores the movement question by question. You see which questions you have started winning, which are holding, and which a competitor has moved into since the last run. The metric is named-in-answer rate, the share of your buyers' questions where AI returns your name, tracked against the baseline from step one.

A monthly cadence is deliberate. Models update, buyers start asking new questions, and a position you held in March can erode by May without a single thing changing on your own site. Catching that while the slip is small is far cheaper than reclaiming a question a rival has settled into. For the full method and the scoring model, how to measure how often AI recommends your brand sets out the metric in detail.

The loop closes here and reopens. Re-scoring feeds the next round of content priorities, which feeds the next round of citation work, which the following month's score then measures. The strategy is this cycle running continuously, not a project that finishes.

The four-step operating model at a glance

Step	What it does	The question it answers	How you know it worked
Audit	Ask AI your buyers' questions, record where you stand	Where does AI place us today?	A baseline named-in-answer score across 40 to 60 questions
Content	Build answers in the format models extract from	What do the models have to draw on?	Pages that answer open buyer questions, structured for extraction
Citation building	Earn references and structured signals models trust	Why would the model trust our name?	External citations and parseable signals across trusted sources
Re-scoring	Re-run the question set monthly, track movement	Are we climbing, holding, or slipping?	Month-on-month change in named-in-answer rate by question

A 90-day shape to start from

The first 90 days establish the baseline and prove movement on the easiest questions. They do not win the whole category. They show the loop working and tell you where to spend the next quarter.

• Weeks 1 to 2: build the question set and run the audit. Agree the 40 to 60 questions that mirror your buyers' real sequence, score where AI places you on each, and rank questions by opportunity. Open questions first, then competitor-owned ones by value.
• Weeks 3 to 8: content and citation work on the opportunity tier. Build answers for the open questions, make existing certifications and outcomes parseable, and start earning external references on the highest-value questions.
• Weeks 9 to 12: first re-score and reset priorities. Re-run the full question set, measure movement against the baseline, and set the next quarter's targets from what moved and what did not.

Ninety days is enough to move the open questions and to read early movement on contested ones. The entrenched, high-value questions a rival owns take longer, because you are displacing a trust signal the model has been building for months. That is the work of quarters two and three. "We're too small for this" usually means "we can't win the hard questions yet." You do not have to. The category sits open precisely because few are working it, and the open questions are winnable now.

A worked example: from absent to recommended

A UK digital identity platform came in with no presence in the AI answers its buyers were reading. Its monthly AI reach sat at roughly 39,000 when the programme started. Its category questions, the ones procurement and product teams were putting to AI about identity verification providers, mostly returned competitors or no clear name at all.

The audit set the baseline question by question. Content work targeted the open questions first, the specific compliance and integration queries where no provider had become the default answer. Citation building made the platform's real credentials legible to the models and earned references on the questions that mattered most. Each month the re-score showed which questions had started returning the platform's name.

Over six months, monthly AI reach grew from roughly 39,000 to 2.9 million, and the platform won a seven-figure contract directly through AI search. The arc is the four steps running on a loop: audit, build, earn citations, re-score, repeat. Movement on the open questions came first and fastest. The contested questions followed as the citation work compounded. (Outcome from a confirmed Forge programme, 2025; client anonymised by agreement.)

Why the timing favours early movers

Buyers have already shifted. According to G2's 2026 buyer study, a majority of B2B buyers now start supplier research with AI chatbots rather than conventional search, and a large share report choosing a different vendor on the strength of AI guidance. (G2, buyer survey, March 2026; exact figures pending final verification against the published release.) Separately, around one in four B2B buyers use generative AI more than conventional search for supplier research (Responsive / Digital Commerce 360, 2025). The shortlist is forming in the chat, and for most categories it is forming without a clear leader yet.

That open category is the reason to move now. Models settle on the name they see cited most often, and the answer hardens around first movers. A rival who starts the loop this quarter is building the trust signal you will have to displace next year. Waiting has a specific cost. A default answer settles around someone else while your category is still open, and displacing a settled trust signal takes far longer than earning an open one.

This is the strategy Forge runs for regulated B2B companies, as a programme rather than a project, under the promise that we make AI recommend you. If you want the ground-level definition before the strategy, start with what is generative engine optimisation.

Frequently asked questions

What is a generative engine optimisation strategy?

A generative engine optimisation strategy is a continuous programme that moves your company into the AI answers your buyers read. It runs in four steps: audit where AI places you, build the content models draw on, earn the citations that build trust, and re-score monthly to track movement on the questions your buyers actually ask.

How long does a GEO strategy take to show results?

Open category questions, where no provider has become the default answer, can move within the first 90 days. Contested questions a competitor already owns take longer, usually two to three quarters, because you are displacing a trust signal the model has been building over time. Monthly re-scoring shows movement question by question throughout.

Is GEO strategy just SEO with extra steps?

No. SEO earns a ranking in a list and is measured on position and clicks. A GEO strategy earns your name inside the AI answer above those links and is measured on named-in-answer rate. The foundations overlap, but the job and the metric differ, and you can rank first while losing the buyer who stops at the AI summary.

How do you measure a GEO strategy?

The core metric is named-in-answer rate: the share of your buyers' questions where AI returns your name, scored against a baseline from the initial audit and re-run monthly. You track which questions you have started winning, which are holding, and which a competitor has moved into since the last run.

Can a small B2B company run a GEO strategy?

Yes, and being small is rarely the barrier. The category sits open because few competitors are working it yet, so the open questions are winnable now without a large budget. Early movers build the trust signal rivals later have to displace, which makes starting while the category is unsettled the advantage.

Do I need new content for every question, or can I use what I have?

Both. Open questions usually need purpose-built answers in a format models extract from. Existing assets, certifications, integrations and customer outcomes, are often already strong but written for humans and invisible to models. Citation building makes that existing material parseable and gets it referenced where the models look.

---

See where AI places you today

Step one of the strategy is the audit, and it is free. Book a discovery call and we will run your category live on the call and show you exactly what AI says about you today.

30 minutes. No pitch deck. We'll run your category live and show you exactly what AI says about you today.

Book a discovery call / free AI-visibility audit