What is GEO (Generative Engine Optimization)?

Generative Engine Optimization (GEO) is the practice of getting your company cited in the answers AI engines — ChatGPT, Gemini, Claude — generate when a buyer asks them a question. It is to AI search what SEO was to the blue-link era: the discipline of being the source an answer is built from, rather than the tenth result nobody scrolls to.

GEO and AEO, defined

Two acronyms describe the same shift. GEO — Generative Engine Optimization — is the broad term, popularized by a 2023 research paper from Aggarwal et al. AEO — Answer Engine Optimization — is often used interchangeably, with a slightly narrower focus on direct-answer surfaces. Both mean the same thing in practice: optimizing to be referenced inside an AI-generated answer, not merely ranked in a list of links below it.

How an AI answer actually gets built

When you ask a modern engine a question that needs current facts, it does not answer from memory alone. It runs a live search, pulls candidate pages into a pool, writes an answer grounded in a subset of them, and attaches citations to the pages it actually used. Roughly, three things happen in sequence.

Those two failure points — being absent from the pool versus being in the pool but never chosen — are different problems with different fixes, and conflating them wastes effort. We pull them apart in discoverability vs selectability.

Fan-out: one prompt becomes many

Here is the part that breaks most people's mental model. The engine rarely searches your literal question. It rewrites one prompt into several narrower sub-queries — current ChatGPT commonly fans out into roughly eight or nine — and each sub-query retrieves and cites its own sources. So your visibility for a single buyer question is decided across a spray of sub-queries you never see, not by one tidy search you can inspect.

Most of the citations are not your website

The pages engines cite skew heavily toward third parties. Independent analyses of AI citations — including Ahrefs' study of AI Overview sources — find the cited set is dominated by independent domains: review sites, comparison articles, documentation, and forums rather than vendors' own marketing pages. Across the engines we track, the pattern lands around 90–95% third-party. Treat that as a consistent observation, not a law — the exact share moves by query type and over time — but the direction is unmistakable: a large majority of the sources that build an answer about you are written by someone else.

Notice what the answer is made of: every citation is a page the vendor does not own. To influence that answer, you do not just polish your homepage — you work to be present and well-represented on the third-party sources the engine reaches for. Which sources those are is the subject of which sources AI engines cite.

Why B2B should care

B2B buyers now ask AI engines exactly the questions that used to start on Google — shortlist questions, comparison questions, "alternatives to X" questions. The stakes are higher than in consumer search for three reasons.

• The answer is a shortlist. If the engine names three vendors and you are not one, you are out of the consideration set before a human ever evaluates you.
• The loss is invisible. There is no ranking report and no lost-click log. A buyer who never sees you also never appears in your funnel — you cannot fix what you cannot measure.
• It is winner-take-most. One synthesized paragraph replaces a page of ten links, so the gap between being cited and being omitted is far wider than the gap between rank 3 and rank 5.

Where to go deeper

If you want the full picture, the GEO guide walks the concepts end to end, and the glossary keeps the terms straight. From there, which sources AI engines cite and discoverability vs selectability go one layer deeper into the mechanics that decide whether you get cited.