AI Search Optimisation: The 2026 Field Guide

AI search optimisation is the work of making a site findable, extractable and citable by AI Overviews, AI Mode, ChatGPT and Perplexity, on top of whatever you already do for Google’s classic results. It sits above AEO, GEO and LLM SEO as the umbrella term for all three. The mechanics are structural: crawl access, answer-shaped content, structured data, then off-site mentions.

Quick links

• What is AI search optimisation?
• Which AI search surfaces actually matter right now?
• How does this fit with AEO, GEO and LLM SEO?
• What should you fix first?
• How do you know if any of this is working?
• FAQ

What is AI search optimisation?

AI search optimisation is the practice of making your site legible to AI-driven answer engines, not just to the traditional search index. The two disciplines overlap heavily but are not the same job.

Classic SEO optimises for a ranked list of ten blue links. A crawler indexes your page, an algorithm scores it against a query, and a human decides whether to click through. AI search works differently at almost every stage:

• The engine reads before it answers. Tools like AI Overviews, ChatGPT and Perplexity pull from multiple sources, then generate a single synthesised answer rather than a list of links.
• Citation replaces the click. You are not competing for position one. You are competing to be one of the two or three sources an engine actually names or links to.
• The unit of relevance shrinks. Engines extract a paragraph, a sentence, sometimes a single fact, not a whole page. A page can rank well in Google and still be invisible to an AI answer if the useful content is buried three paragraphs down.

None of this makes classic SEO obsolete. Google Search still sends most of the traffic most sites get. AI search optimisation is an addition to that work, aimed at a growing slice of research that never produces a click at all.

Which AI search surfaces actually matter right now?

Four surfaces are worth your attention this year: Google’s AI Overviews and AI Mode, ChatGPT, and Perplexity. Between them they cover the overwhelming majority of AI-assisted search behaviour.

Google AI Overviews now reaches over 2.5 billion monthly active users, according to Sundar Pichai’s keynote at Google I/O 2026. It sits inside ordinary Google search results, so if your content already ranks reasonably, this is the surface with the shortest path to visibility.

Google AI Mode is smaller but growing faster. It surpassed one billion monthly users within a year of launch, with query volume more than doubling every quarter since, according to Google’s own Search blog post from I/O 2026. Where AI Overviews sits above the results, AI Mode replaces them with a full conversational session, which raises the bar on how directly your content needs to answer the underlying question.

ChatGPT passed 800 million weekly active users when Sam Altman announced the figure at OpenAI’s DevDay in October 2025, reported by TechCrunch. It is the largest standalone AI audience and the one most buyers reach for when they want a shortlist of providers, not a ranked results page.

Perplexity is smaller in absolute numbers than any of the above, but it over-indexes on exactly the kind of high-intent, comparison-driven research a B2B buyer does before they contact anyone. Worth building for even though it will never rival Google or ChatGPT on raw volume.

Gemini sits somewhere between these, benefiting from Google’s own reach without yet having the standalone habit ChatGPT has built.

How does this fit with AEO, GEO and LLM SEO?

AI search optimisation is the umbrella. AEO, GEO and LLM SEO are the same underlying work, named by different corners of the industry at different moments as the category found its footing.

Answer engine optimisation (AEO) focuses on structuring content so an engine can lift a clean, direct answer from it. We cover the mechanics in detail in our AEO explainer, and the short version is that most of it comes down to writing the answer first and burying nothing.

Generative engine optimisation (GEO) is a near-synonym that leans slightly more towards the generative, conversational engines specifically. In practice, the work overlaps with AEO almost entirely.

LLM SEO is the newest label, used mostly by people trying to describe getting cited inside a large language model’s response rather than a search engine’s index. We go deeper on the mechanics of that in our LLM SEO guide.

You do not need three separate strategies. You need one: be crawlable, be answer-shaped, be structured, and be mentioned by other credible sources. Whichever term a given client, agency or platform prefers is mostly a branding choice rather than a technical one.

What should you fix first?

Fix crawl access before anything else. A perfectly structured page an AI crawler cannot read is invisible, regardless of how good the content is.

Work down this order:

1. Confirm the crawlers can reach your content. Most AI crawlers, including GPTBot, ClaudeBot and PerplexityBot, read the raw HTML a page returns and do not execute JavaScript, according to Vercel’s analysis of crawler behaviour. If your key content only appears after client-side scripts run, those engines may see a blank or half-empty page. Check your robots.txt is not blocking them by accident, and confirm your important content sits in the initial page source, not injected afterwards.
2. Make the content answer-shaped. Put the direct answer to the reader’s question in the first sentence or two of each section, then explain. Real questions as headings, short paragraphs, and specifics over vague claims. This is the single biggest lever for most sites, and we cover the practical checklist in our guide to AI website optimisation.
3. Add the structured data that matters. Organization schema on your homepage, Article schema on blog content with a real author, and FAQPage schema where you have a genuine FAQ section. None of this is essential, but it removes ambiguity engines would otherwise have to guess at.
4. Build the off-site signals. Consistent business details across your site and directories, a complete Google Business Profile, and genuine mentions on third-party sites. AI engines lean on this to decide who to trust, the same way a person leans on a second opinion before taking a recommendation seriously.

Do these roughly in order. There is little point polishing your schema if the crawlers cannot see the page it sits on.

How do you know if any of this is working?

You know it is working when AI engines start naming you unprompted, and you check that by asking them directly and by watching the traffic that shows up afterwards.

Two ways to measure, one manual and cheap, one more thorough:

• Prompt testing. Ask ChatGPT, Perplexity and Gemini the questions a buyer would actually type, not your brand name. “Best [service] providers in [location]” or “who should I use for [problem]”. Note whether you appear, whether the details are accurate, and who gets named instead of you. Repeat monthly, since answers shift as engines re-crawl and re-weight sources.
• Referral tracking. Most analytics platforms will now show traffic arriving from ChatGPT, Perplexity and similar sources. A rising trickle from these referrers, even a small one, tells you the mechanics are starting to bite. It rarely shows up as a flood. It shows up as a slow, compounding drip.

We rebuilt Hoc Paid Media’s site on a lighter, AI-readable stack earlier this year and watched the first inbound lead land within 24 hours of launch, off the back of the same structural fixes covered above: a crawlable build, answer-shaped pages, and a brand consistent enough for an engine to trust. The result is not typical of every rebuild, but it is a fair example of what fixing the fundamentals actually buys you.