What is GEO?
Generative engine optimization (GEO) is the practice of improving whether AI engines mention a brand when buyers ask them for advice, and what they say about it when they do.
The field goes by several names: answer engine optimization (AEO), AI search optimization, LLMO (large language model optimization) and, on the measurement side, AI visibility. They grew up in different places and overlap almost entirely. G2 files the software under "answer engine optimization". We write GEO for convenience.
Picture a buyer typing: "What's a good project-management tool for a 40-person design agency?" The engine replies with a short list and a paragraph on each product. Half of GEO is the familiar fight: getting onto that list. The other half is new: what the paragraph says once a brand is on it.
Why does GEO matter now?
Because buyers have moved. In a G2 survey from April 2026, 51% of B2B software buyers said they now start their research in an AI chatbot, up from 29% a year earlier. The same survey found that 69% had chosen a different vendor because of what a chatbot told them. G2 runs a software-review marketplace and has a stake in this story, so read the exact figures loosely. The direction is hard to argue with.
And the room in each answer is small. Asked for product recommendations with web search on, ChatGPT named about five products per answer, according to a Visibility Labs study reported in June 2026. An answer has no page two. A product outside those five is absent from that conversation.
How do AI engines decide what to say?
They draw on two sources: what the model remembers from training, and what it finds when it searches the web for the question in front of it. The two often disagree. In the same Visibility Labs study, ChatGPT's product picks changed by about 80% between search on and search off. Engines like ChatGPT, Claude and Gemini can search the web, and Perplexity always does.
An engine that searches splits the buyer's question into several searches of its own. A question about a CRM for small agencies might become one search about pricing and another about reviews. The pages it reads are often not the ones that rank. In an Ahrefs study updated in May 2026, only 12% of URLs cited by ChatGPT, Gemini, Copilot and Perplexity ranked in Google's top 10 for the same query.
What the web says about a brand seems to count for more than who links to it. In a December 2025 Ahrefs study of 75,000 brands, mentions on YouTube and across the web tracked AI visibility far more closely than backlinks did. That is correlation, not proof of cause. Nobody outside the engine makers knows the exact weighting, and we don't either.
Why does one AI answer tell you so little?
Because AI engines rarely give the same answer twice. In SparkToro's January 2026 study, engines asked the same question returned the same list of brands less than 1% of the time, and the same order about 0.1%.
The same SparkToro study found something steadier underneath: how often a given brand appeared across many runs was stable. The single list is noisy. The share is not.
That makes the useful number the mention rate: the share of answers that name a brand. A marketer asks ChatGPT once, finds the product listed third, and relaxes. A colleague asks an hour later, and the product is gone. Neither check means much. The mention rate across many askings and phrasings does.
Being named is also only half the reading. An answer can name a brand and warn against it in the same breath, and we think that does more damage than silence. So a useful count separates a recommendation from a mention, and a mention from a warning.
How is GEO different from SEO?
SEO earns a place in a list of links; GEO earns a place in the answer itself, and a say in how that answer describes the brand.
A search page listed you. An AI answer describes you — it picks the criteria, writes the comparison, and tells your story with or without you.
The description is not always accurate. Searchable, a vendor in this market, graded about 1 in 10 brand facts in AI answers as wrong in its July 2026 report, with pricing wrong most often.
Some SEO skill carries over. In an AirOps study from April 2026, ChatGPT cited headings that matched the question 41% of the time, against 29% for headings that didn't. Some popular fixes show no effect at all: in a May 2026 Ahrefs test, adding schema markup brought no measurable citation gain.
What does GEO work look like?
One way to run the work, and the one we use, is a loop of four steps: scan, trace, plan and re-scan. Its aim is not to trick a model. It is to give the engine a brand's true story, in the terms the engine uses to decide.
- Scan. Ask AI engines many different questions, each phrased the way a real buyer would, and read every answer. A useful question sounds like a person: "Which CRM works for a small agency that bills by the hour?"
- Trace. Follow each answer back to what the engine read, and to the criteria it used to choose. An engine comparing CRMs for agencies might weigh time tracking, price per seat and client portals, and cite a review page, a competitor's comparison post and a forum thread along the way.
- Plan. Decide what the brand can truthfully claim on those criteria, and where the engine will find the proof. The brand's own site comes first: it is what the engine reads when it searches, and it is the fastest thing a team can change. Then come the places others talk about the brand, such as reviews, comparisons, communities and video, where the evidence above says much of the weight sits.
- Re-scan. Ask the same questions again, so a change in mention rate reflects the answers and not the test.
No one can promise what an engine will say. We don't own the machine.
How it works shows each step in detail. For what happens once the engines do describe a brand, read Why ChatGPT describes your brand the way it does.
Sources
- G2, "Half of B2B software buyers now start their research with AI chatbots", April 2026 — source
- Visibility Labs via Search Engine Land, "80% of ChatGPT product recommendations change when search is enabled", June 2026 (20,000 responses) — source
- Ahrefs, AI assistants vs Google top-10 overlap, 15,000 queries, updated May 2026 — source
- Ahrefs, brand visibility correlations across 75,000 brands, December 2025 — source
- SparkToro with Gumshoe, AI recommendation consistency, 2,961 runs, January 2026 — source
- Searchable, AI brand misinformation report, 32,556 facts, July 2026 — source
- AirOps with Kevin Indig, "The fan-out effect", April 2026 — source
- Ahrefs, schema markup and AI citations, 1,885 pages, May 2026 — source