GEO for B2B Marketers: A Primer

What generative engine optimisation means for B2B content teams, and where to start.

Sometimes the impact of AI on your business arrives in ways you don’t expect. An unexplained uptick in signups or revenue with no campaign behind it, no traceable spike in web traffic. That’s the good version. The harder version: your SEO traffic has started to plateau or dip, and you can’t pinpoint why. A growing slice of search behaviour never reaches a results page anymore. Someone asks ChatGPT, Perplexity, or Google’s AI Overviews a question, gets a synthesised answer, and moves on. Your content may have informed that answer. You’ll just never see it in your analytics.

That’s the GEO problem, and it’s already happening. AI-referred sessions jumped 527% year-over-year in the first five months of 2025, and the gap between what your content earns and what your analytics captures is only widening.

What is GEO?

Generative Engine Optimisation (GEO) is the practice of structuring content so that AI-powered search surfaces (ChatGPT, Perplexity, Google’s AI Overviews, Microsoft Copilot) are more likely to surface, cite, and accurately represent it.

It is not a replacement for SEO. Some would call it an evolution. Your organic rankings still matter, but GEO adds a new layer because these AI surfaces don’t work the way a search results page does. They don’t send you a click and walk away. They summarise, synthesise, and sometimes cite. Getting cited is the new benchmark.

The distinction that matters for B2B marketers: AI search surfaces are increasingly where buyers do early-stage research. Not “what is this product” research, but the deeper kind. “What should I consider when evaluating a CRM for a 50-person sales team?” That’s a GEO opportunity, and a well-structured content library can own it.

Why GEO matters more for B2B than you might think

B2B has a few characteristics that make GEO more relevant, and more winnable, than it might seem.

Buying cycles are long and research-heavy. B2B buyers aren’t impulse purchasing. They’re building a case, comparing options, and looking for signals that a vendor understands their problem. AI-assisted research is a natural fit for that process, giving your content more chances to appear across a longer timeline.

Your buyers are already using AI assistants for work. If your audience is in marketing, sales, finance, or operations, they’re using ChatGPT or similar tools regularly. 58% of users have already replaced traditional search engines with AI tools for product and service discovery. That’s not a projection; it’s the current baseline. The question is whether your content is shaping those conversations.

Specificity wins. Generic content that covers a topic broadly gets homogenised into “here are some things to consider” answers. Content that takes a clear position, provides a concrete framework, or gives a specific example is more extractable and more citable. One practice I’ve added: a short FAQ section at the end of posts, written around the specific questions buyers are likely to ask an AI.

How AI search surfaces decide what to cite

This is still evolving, and anyone claiming certainty here is overselling. But consistent patterns have emerged.

Clarity of claim

AI models extract content more reliably when the claim is clearly stated, not buried in hedging. “B2B content attribution is hard because most CRMs don’t capture first-touch data” is extractable. “There are many challenges in measuring content ROI” is not.

Source authority

Backlinks and domain authority still matter. Not because AI is crawling PageRank, but because high-authority sources are more likely to be in the training data and crawl sets these systems pull from. Building links to your best content isn’t a deprecated tactic.

Structured, well-formatted content

Headers, clear section breaks, and content that answers one question per section make it easier for a model to locate and attribute specific information accurately. Long-form walls of text are harder to parse. This applies to heading hierarchy too: H2s and H3s carry semantic weight that bold text alone doesn’t.

Freshness

Perplexity in particular surfaces recent content. 50% of content cited in AI answers is less than 13 weeks old. If your content library hasn’t been updated in two years, it will lose to a well-structured post published six months ago, even if yours is more thorough.

What B2B content teams can do now

You don’t need to rebuild your content strategy. These are additive adjustments.

1. Audit your best-performing content for extractability. Take your top five posts and look for clearly stated, quotable claims. If you can’t find a sentence that directly answers the post’s core question in one or two lines, add one. Explicitly. At the top, or as a summary after the intro. I built a small tool to help with this: it audits a given URL for SEO and GEO performance and flags what needs fixing. More on that in a separate post.

2. Add structure to older cornerstone content. If you have a high-quality post that’s dense and unformatted, break it up. Add H2s, make the key takeaways scannable, and ensure the most important claim in each section is stated plainly rather than implied. Early on, I had posts loaded with H3s and H4s because they looked cleaner visually. What I’ve since understood is that heading hierarchy isn’t just for readers — it’s a structural signal to AI about how your content is organised. That’s changed how I approach everything now.

3. Write for the question, not just the keyword. SEO trained us to optimise for search terms. GEO rewards content that directly answers a well-formed question. For B2B, think about what buyers are actually asking at each stage — “how do I compare enterprise CRM options,” “what’s a realistic B2B content attribution model” — and write pieces that answer those directly, not tangentially.

What we still don’t know about GEO

GEO is genuinely new, and the honest answer is that the signals are still being mapped. Google hasn’t published a GEO equivalent of its Search Central documentation. Perplexity’s crawl and citation logic isn’t publicly detailed. What works for ChatGPT’s browsing mode may differ from what surfaces in AI Overviews.

What’s clear is the direction: AI-assisted search is not a fad, and B2B buyers doing vendor research are already using it. Building content that’s clear, well-structured, and genuinely useful isn’t a GEO hack. It’s just good content that also happens to be GEO-ready.

Start there. The rest will get clearer as the tools mature.

FAQ

What is the difference between GEO and SEO? SEO optimises content to rank in traditional search engine results pages, driving clicks to your site. GEO optimises content to be cited inside AI-generated answers, where the user may never visit your site at all. Both matter — SEO gets you found, GEO gets you quoted.

Does GEO replace SEO for B2B marketers? No. The best approach treats them as complementary layers. Your organic rankings still drive direct traffic and build domain authority, which in turn supports GEO citability. Dropping SEO to focus purely on GEO would be a mistake; the two compound each other.

How do I know if my content is being cited by AI search surfaces? This is still an evolving measurement problem. Tools like Perplexity allow you to test queries directly. More robust tracking (monitoring brand mentions across AI engines) is emerging from platforms like Profound. For most lean teams, the practical starting point is manual spot-checking: ask ChatGPT and Perplexity the questions your buyers are asking, and see whether your content appears.

How quickly does GEO work? Faster than you might expect in one sense — freshness is a strong signal, so well-structured new content can be cited quickly. But building the domain authority and citation history that makes AI surfaces consistently trust your content is a longer game, measured in months rather than weeks.