What is GEO (generative engine optimization)?

Definition

GEO (generative engine optimization) is the set of practices that make a brand more likely to be named, and its pages more likely to be cited, in answers generated by AI engines, on the questions its buyers ask.

The AI engines that matter for B2B today are ChatGPT, Claude, Gemini, Perplexity and Google’s AI Overviews and AI Mode. Each answer can have three outcomes for a brand: it is named in the text, it is cited as a source without being named, or it is absent. GEO works on the first two.

Where the term comes from

“Generative Engine Optimization” was introduced in a 2023 paper, presented at the KDD conference in 2024.

The authors built a benchmark of 10,000 queries and tested how changes to a page affected its visibility in generated answers. They report that GEO methods “can boost visibility by up to 40%”, with the best results from adding quotations, statistics and source citations, while keyword stuffing did not help ^[1]. Before that, “answer engine optimization” (AEO) described optimizing for featured snippets and voice assistants.

Why it matters for B2B

Buyers increasingly use AI engines to build their first shortlist, and fewer of them click through to websites when an AI summary is shown.

• In G2’s March 2026 survey of 1,076 B2B software buyers, 51% start research with an AI chatbot more often than with Google, up from 29% in G2’s 2025 report ^[2].
• In Pew Research’s US panel, users who saw a Google AI summary clicked a traditional result in 8% of visits, against 15% without one ^[3].

If the answer names three vendors and you are not one of them, you may never know the buyer existed.

How AI engines decide what to cite

They combine what the model learned in training with pages retrieved live for the question. Pages that can be crawled, answer precisely and are corroborated by other sources are easier to reuse.

• Retrieval. Google describes a “query fan-out” technique for AI Overviews and AI Mode, “issuing multiple related searches across subtopics and data sources”, and says there are no special requirements beyond being indexed and eligible for a snippet ^[4].
• Access. Each engine has its own crawlers. For example, sites that block OpenAI’s OAI-SearchBot “will not be shown in ChatGPT search answers” ^[5].
• Corroboration. A brand described consistently on several credible third-party sources is easier for an engine to name with confidence.

More detail in how AI engines choose which brands to name.

GEO vs SEO

SEO ranks pages in a list. GEO gets the brand into the answer. They share foundations but differ in goal and measurement.

The full comparison: GEO vs SEO for B2B buyers.

How to measure GEO

Ask a fixed set of buying questions several times on each engine, count named and cited separately, and compare against a noise floor.

AI answers vary: in one study, ChatGPT and Google’s AI almost never returned the same list of brands twice in 100 tries ^[6]. A single screenshot proves nothing. From the Core plan upward, our protocol asks 50 buying questions 5 times on each of 5 engines, keeps branded questions separate, attaches a margin of error to every rate, and hands over the raw answers. See the methodology and the KPIs worth reporting.

How to start

• List your buying questions: the ones a prospect asks before contacting a vendor.
• Check access: make sure the search crawlers of each engine can read your key pages.
• Measure a baseline: where you are named, cited, or absent, and who is named instead.
• Fix, produce, place: technical fixes first, then answer-first content on your site and on the sources the engines reuse.
• Re-measure with exactly the same protocol.

The fastest first step is a free Quick Scan of up to five of your buying questions.