AI Search Engine Optimization: How It Works and What Actually Moves It

AI search engine optimization is optimizing a brand and its content so that AI search systems retrieve it, describe it accurately and recommend it when buyers ask questions. It covers two surfaces at once: the classic results page and the generated answer that increasingly sits above it or replaces it.

You will see the same work sold under several names. Generative engine optimization (GEO) and answer engine optimization (AEO) are the most common, and some vendors say LLMO or AIO. The labels differ in emphasis more than substance. If you want the combined Google-plus-AI program we run for clients, that is our AI SEO work; if you want the short version of how the terms compare, read GEO vs SEO.

One thing it is not: using AI tools to write SEO content. That is a separate topic, and a lot of the search results for this phrase mix the two up.

Every major AI search product follows roughly the same four steps. Knowing them tells you where you can intervene.

• The question gets expanded. A single question is split into several searches. Google calls this query fan-out; one of its engineering directors described AI Mode as doing a dozen searches in the time it takes to do one.
• Pages get retrieved. Those searches run against an index. Google uses its own; ChatGPT search relies on OpenAI's search crawler, OAI-SearchBot; Perplexity runs its own crawler too.
• An answer gets written. The model reads what came back and synthesizes one response, often naming a few brands and explaining when each one fits.
• Sources get cited. Some of the pages it read are shown as links. Those citations are the one part of the process you can observe from the outside.

The practical consequence is blunt. If your page is not retrievable for the searches the system runs, nothing else on this list matters. That is why the foundation is still ordinary crawlability, indexing and relevance.

A lot of AI search advice contradicts the one company that publishes rules for its own AI features. Google's guide to optimizing for generative AI features in Search (last updated July 10, 2026) says its AI Overviews and AI Mode are rooted in the same core ranking and quality systems as regular Search, and that normal SEO best practices still apply. It also lists things you do not need to do:

• Create llms.txt files or other special machine-readable files. Google Search ignores them.
• Add special schema.org markup for AI features. There is none.
• Break your content into small "chunks" or rewrite it specifically for AI systems.
• Chase inauthentic mentions of your brand across the web.

That does not mean structure is irrelevant. A page that answers a question plainly near the top, under a heading that matches the question, is easier for anyone to use, human or machine. It means you should write for the buyer and stop paying for tactics built around a model's imagined preferences.

These six moves account for most of the results we see. They are listed roughly in the order you should check them.

1. Rank and get indexed

Because Google's AI features sit on its core ranking systems, weak organic visibility usually means weak AI Overview visibility. Fix indexing gaps, thin commercial pages and poor internal linking first. Our organic search work starts here for exactly that reason.

2. Let the right crawlers in

Check robots.txt against each company's published crawler rules. OpenAI recommends allowing OAI-SearchBot if you want to appear in ChatGPT search, while GPTBot is used for model training and blocking it does not remove you from search. Google-Extended controls whether your content is used for Gemini models; it does not affect inclusion or ranking in Google Search. Blocking the wrong one by accident is one of the most common problems we find.

3. Answer the questions buyers actually ask

AI answers are built from pages that state things plainly: what a product is for, who it suits, what it costs, how it compares. In our payroll teardown, Paychex was chosen in only 5 of the 29 answers that named it, while specialists with a clearly stated use case won their lane. Scale gets a brand into the table; a stated use case gets it picked.

4. Show up in the sources the model reads

A large share of the evidence behind AI recommendations lives on other people's pages: review sites, comparison articles, publishers and communities. In our help desk teardown, 255 of 336 recommended brand mentions went to brands whose own website did not rank in Google's top ten for that query. Your own content can be one of those sources too: gusto.com was cited in 65 of 100 payroll answers, 48 of them for questions that never named Gusto.

5. Write with evidence

The research paper that coined the term GEO tested nine ways of rewriting content and found that adding quotations, statistics and citations to credible sources raised visibility in generated answers by up to 40 percent, while keyword stuffing did little. It was a lab benchmark, so treat the exact figures as directional, but the lesson matches what we see: specific, sourced claims get used.

6. Measure recommendations, not mentions

Being named is not being chosen. Track a fixed set of commercial questions across each AI surface, code whether your brand was recommended, listed or absent, and compare that with Google's top ten for the same questions. AI visibility tools sample answers rather than observe them, so read their numbers as trends, never as hard counts.

• Counting mentions as wins. A brand listed in a comparison table and never selected has not gained much.
• Treating llms.txt as a strategy. It is optional housekeeping, not a ranking lever.
• Rewriting the whole site for AI. Google says you do not need to. Fix the pages your commercial questions point to.
• Buying mentions. Google explicitly lists inauthentic mentions as something not to chase, and fake reviews carry legal risk of their own.
• Checking one prompt once. Answers vary between runs. A single screenshot is an anecdote.
• Abandoning classic SEO. It is the layer the answers are built from.

A workable measurement setup has four parts, and none of them requires an expensive platform to start:

• A fixed query set. 50 to 100 questions your buyers ask, split across category, comparison, alternative and recommendation phrasing.
• A schedule. Run the same set monthly on each surface you care about, with the same settings.
• A coding rulebook. Decide in advance what counts as recommended versus listed, and apply it consistently.
• A control. Pull Google's top ten for the same questions so you can see where the two surfaces agree and where they do not.

This is the method behind our published AI search research, and each teardown includes its dataset if you want to see the coding in practice.

Is AI search engine optimization different from SEO?

Partly. The foundation is the same, because AI search features retrieve from the same web index that classic search ranks. What changes is the finish line: you are trying to be cited and recommended inside an answer, not only ranked in a list, and that has to be measured separately.

Do I need an llms.txt file for AI search?

No. Google says Search ignores llms.txt, and server-log studies show the major AI crawlers rarely request it. It is harmless to publish one, but it will not move your visibility. Our llms.txt guide covers the evidence.

Does structured data help with AI Overviews?

Google says there is no special schema.org markup required for its generative AI features. Structured data still helps with rich results and with describing your organization clearly, so keep it accurate, but do not expect it to earn AI citations on its own.

How long does AI search engine optimization take to show results?

Technical fixes such as unblocking a search crawler can show up within weeks. Changes that depend on other sites, such as earning a place in the comparison pages and reviews that answers cite, usually take months. Measure against a fixed baseline so you can tell the difference between progress and noise.

Can a small brand win in AI answers against big competitors?

Sometimes, and more often than in classic rankings. In the original GEO study, lower-ranked sources gained more from evidence-rich rewrites than top-ranked ones. In our own teardowns, brands tied to a clearly stated use case were chosen for that case even when a bigger brand was named in the same answer.

How do I know if ChatGPT recommends my brand?

Build a fixed list of the commercial questions your buyers ask, run them on a schedule, and record whether your brand is recommended, merely listed, or absent, next to your competitors. Answers vary from run to run, so one screenshot proves very little. A repeated sample does.

AI search engine optimization is mostly disciplined SEO plus two additions: earning a place in the third-party sources answers are built from, and measuring whether you are actually recommended. Skip the tactics Google has already said it ignores. Make sure the right crawlers can reach you, state plainly who you are for, and track a fixed set of buyer questions over time. Everything else is detail.

• Google Search Central: Optimizing your website for generative AI features on Google Search
• Google: How AI Mode search works (query fan-out)
• OpenAI: Overview of OpenAI crawlers
• Aggarwal et al., GEO: Generative Engine Optimization (KDD 2024)

Every High Salience engagement starts with the Category Salience Brief: your commercial questions run across Google and the AI surfaces, competitors side by side, and a ranked list of what to fix first.