How AI search changes SEO: AIO, GEO and LLMO explained

For twenty-five years, search optimisation meant one thing: earn a position in a ranked list of links, then persuade someone to click yours. That model is now only part of the picture. A growing share of buying research never produces a click at all — the question is asked, an answer is synthesised, and two or three sources are cited beneath it.

This has produced a small flood of new acronyms. Most articles about them are vague, and a few are actively misleading. Here is a working definition of each, what evidence suggests actually influences them, and which parts of the current advice you can safely ignore.

The four terms, defined

These are not four separate disciplines requiring four separate budgets. They are four lenses on the same underlying problem: being findable, parseable and trustworthy enough to be selected as a source.

• SEO — optimising to rank in a traditional list of results. Still the foundation; AI systems draw heavily on the same index.
• AIO (AI optimisation) — a broad umbrella covering visibility inside AI-generated answers of any kind, including Google's AI Overviews.
• GEO (generative engine optimisation) — the academic term for optimising content specifically for generative answer engines.
• LLMO (large language model optimisation) — focused on being retrieved and cited by conversational assistants such as ChatGPT, Gemini and Claude.
• SXO (search experience optimisation) — the discipline of making the landing experience convert once someone does arrive.

What actually influences AI citations

You cannot control what a language model outputs. You can meaningfully influence what it retrieves, and retrieval is where the leverage sits. Four factors matter more than the rest.

1. Extractability

Answer engines lift self-contained claims. A paragraph that only makes sense after reading the three above it is far less likely to be quoted than one that states a complete, standalone fact. This does not mean writing in disconnected fragments — it means making sure each substantive claim can survive being pulled out on its own.

2. Entity clarity and consistency

Models need to resolve who you are with confidence. If your company is described differently across your site, your LinkedIn, your Google Business Profile and third-party directories, that ambiguity reduces the chance of being confidently cited. Consistent naming, consistent category description and structured data all reduce that ambiguity.

3. Independent corroboration

A claim that appears only on your own website is weaker than one echoed across independent sources. This is the same principle that has always underpinned digital PR, and it transfers almost directly to AI retrieval.

4. Machine readability

Clean semantic HTML, valid structured data, fast server responses and content that does not require JavaScript execution to be visible. Crawlers used by AI systems are frequently less capable than Googlebot at rendering, so a site that depends on client-side rendering to show its content is at a measurable disadvantage.

How to measure any of this

This is where most programmes fall down. Traditional rank tracking does not capture AI visibility, and the analytics picture is genuinely murkier because a cited-but-not-clicked appearance leaves no trace in your traffic data.

The workable approach is a fixed prompt set. Define fifty to a hundred questions your buyers genuinely ask — not keyword strings, actual questions — and check them on a fixed schedule across the assistants your market uses. Record whether you are mentioned, whether you are cited with a link, and how your offering is characterised. That last one matters more than people expect: being described inaccurately is its own problem.

What to ignore

• Anyone guaranteeing that ChatGPT will recommend you. Nobody can guarantee a model's output.
• Keyword-stuffing adapted for AI. Models are considerably less susceptible to this than 2012-era search engines were.
• Abandoning traditional SEO. Answer engines draw on the same indexed corpus; weakening your search foundation weakens AI visibility with it.
• Paying for 'AI-optimised content' that is simply generated text. Volume without substance is the fastest route to being neither ranked nor cited.

Where to start

1. 01Baseline your current visibility against a fixed prompt set, so you can prove change later.
2. 02Fix technical and structured-data foundations — this is the highest-leverage work and it is unglamorous.
3. 03Audit entity consistency everywhere your business is described publicly.
4. 04Publish genuinely substantive answers to the questions your buyers actually ask, written to be extractable.
5. 05Re-measure on the same prompt set monthly.

None of this is exotic. It is largely the discipline good technical SEO always demanded, applied with a clearer understanding of who is now reading the page — and increasingly, that reader is a machine summarising you for someone else.