AI Search Optimisation (AEO/GEO) - Staying Visible in AI Search

Search is no longer just a list of blue links. Google now shows AI Overviews on a meaningful share of searches, and a growing number of people start with assistants like ChatGPT, Gemini and Perplexity instead of a traditional search box. When an AI answers the question directly and cites a few sources, being one of those cited sources matters a great deal.

This guide explains what AI search optimisation means, how AI systems surface and cite content, and the practical tactics that help. It is deliberately measured. This area is evolving quickly, much of the published advice is speculative, and anyone promising guaranteed AI rankings should be treated with caution.

What AEO and GEO mean

Two terms have emerged for this work, and they overlap heavily.

• AEO, or answer engine optimisation, focuses on being the source an AI uses to answer a direct question.
• GEO, or generative engine optimisation, is the broader practice of optimising content to be surfaced and cited within AI-generated responses across platforms.

In practice the goals are the same: be the trustworthy, well-structured content that an AI system chooses to summarise and credit. Google itself has framed optimising for its generative features as still being SEO rather than a separate discipline, which is a useful reality check. The fundamentals have not been replaced, they have been extended.

How AI surfaces and cites content

AI search systems do not work identically, but a few common patterns are worth understanding.

• Many systems retrieve relevant pages from a search index or the live web, then generate an answer grounded in what they find. This is why traditional discoverability still matters - if you cannot be found and crawled, you cannot be cited.
• They favour content that answers the question clearly and directly, because that is easier to extract and summarise.
• They tend to draw on sources that demonstrate genuine expertise and align with what other reputable sources say.
• They often cite a small number of pages, so the bar for being chosen is high.

The takeaway is that AI systems reward clarity, credibility and good structure, much as a careful human researcher would.

Practical tactics that work

None of these are magic switches. They are sound content and technical practices that happen to help AI systems understand and trust your pages.

Answer clearly and early

• Lead with a direct, self-contained answer to the question a page targets, then expand with detail.
• Use descriptive headings that mirror how people actually phrase questions.
• Keep key facts concise and unambiguous so they are easy to lift accurately.

Build genuine expertise

• Publish content written or reviewed by people with real knowledge of the subject.
• Show who is behind the content with clear author information and credentials.
• Be accurate and specific. Vague, padded content is easy to ignore and easy to misquote.

Strengthen entities and structure

• Be clear and consistent about who you are, what you do and where, so systems can recognise your organisation as an entity.
• Keep your details consistent across your site, profiles and reputable third-party sources.
• Use clean, logical page structure with headings, lists and tables that make relationships obvious.

Use schema markup

• Add structured data such as Organisation, Article, FAQ, Product and Breadcrumb where genuinely relevant.
• Treat schema as a way to describe your content accurately to machines, not as a trick. It helps systems understand context, even if it is not a guaranteed route into any answer.

Earn citations and mentions

• Aim to be referenced by other reputable sites, since being cited elsewhere supports your credibility.
• Cover topics thoroughly enough that you become a natural reference point others link to.

How it relates to traditional SEO

It is tempting to treat AI search as a brand new game requiring a separate strategy. The more accurate view is that AI search rests on the same foundations as good SEO. Crawlability, indexing, fast and stable pages, clear information architecture, genuine expertise and trustworthy content all still matter, arguably more than before.

What shifts is emphasis. Concise, well-structured answers become more valuable because they are easier for an AI to use. Brand authority and consistent entity information carry more weight because systems look for corroboration. And you can no longer assume that a strong ranking guarantees a click, because the AI may answer the question on the results page. The sensible approach is to keep doing fundamental SEO well and layer these refinements on top, rather than abandoning what works.

Measuring AI visibility

Measurement here is genuinely immature, so set expectations accordingly. There is no single reliable scoreboard yet. That said, you can build a useful picture.

• Track referral traffic from AI platforms in your analytics where it is identifiable, accepting that attribution is incomplete.
• Monitor whether your content appears as a citation in AI Overviews and assistant answers for your priority questions, through manual checks and emerging tools.
• Watch for the pattern of high impressions with softer click-through in Search Console, which can signal answers being satisfied on the results page.
• Keep an eye on branded search and direct traffic, since AI visibility can drive awareness that shows up indirectly.

Treat all of this as directional. The honest position is that the tooling is catching up, and any precise AI visibility score should be read with healthy scepticism.

Common mistakes and false promises

Because the field is young, it has attracted plenty of bad advice. A few things to be wary of:

• Chasing AI rankings as if they were a fixed, measurable scoreboard. They are not, and they vary by platform and query.
• Stuffing pages with statistics or keywords in the hope of looking authoritative. This reads as spam to both people and machines.
• Treating schema as a magic shortcut. It helps machines understand your content, but it does not buy you a place in any answer.
• Spinning up thin, AI-generated pages at scale. Low-quality content is exactly what these systems are built to filter out.
• Abandoning traditional SEO. If your site cannot be crawled and indexed, none of the AI tactics will help.

If a provider promises guaranteed placement in AI Overviews or assistant answers, treat that as a red flag rather than a selling point.

Where to focus your effort

Given the uncertainty, the smart move is to invest in work that pays off regardless of how AI search evolves. That means content that genuinely answers real questions, a technically sound and fast website, a recognisable and consistent brand presence, and a reputation built through being referenced by others. Every one of these helps with traditional search, AI search and your customers directly. You are not betting on a single platform or a passing trend, you are strengthening the foundations that all discovery depends on. Layer the AI-specific refinements on top once the basics are solid, and revisit your approach as the platforms and the evidence mature.

Bringing it together

AI search is changing how people find answers, but it has not torn up the rulebook. The businesses that stay visible will be the ones that publish genuinely expert, clearly structured, trustworthy content, keep their technical foundations sound, and describe themselves consistently so machines and people alike can trust them. Chase the fundamentals, add the refinements that help AI understand you, and stay measured about what can actually be promised in a fast-moving space.

If you want help adapting your content and technical setup for AI search without falling for the hype, Control Tower can help you build a strategy grounded in what actually works.