AI optimization: be the brand that AI recommends

For twenty years, the deal was simple. Someone typed a query, Google returned ten links, and your job was to be one of them. Being fourth was survivable. Being on page two was not great, but the door was still technically open.

That model is being replaced by something far less forgiving.

Ask an AI assistant which accounting firm to use in Vancouver and you do not get ten options. You get a paragraph, two or three names in it, and a confident tone that most people do not think to question. The shortlist has already been made. You were either on it or you were not.

The numbers behind this shift moved faster than almost anyone forecast. In a single year, consumers using generative AI for local recommendations went from 6% to 45%. Meanwhile, research into local business visibility found ChatGPT surfacing just 1.2% of business locations, against 35.9% appearing in Google’s traditional three-pack for the same searches.

Read those two figures together and the picture is stark. Demand for AI recommendations is climbing steeply. Supply of businesses that AI will actually name is vanishingly small.

That gap is the opportunity. It will not stay open indefinitely.

Every major assistant works through roughly the same sequence, though the details vary enormously between them.

1. The question gets reinterpreted. Your prospect types six words. The model expands that into several underlying questions, often searching for each separately. Google calls this query fan-out. It means the prompt you think you are competing on is rarely the query that actually runs.
2. Sources get retrieved, or not. Some answers come from live web retrieval. Others come purely from what the model absorbed during training, with no search at all. Which path fires changes what you can influence and how quickly.
3. Candidates get filtered. Retrieved content is assessed for relevance, corroboration and trust. Most of it is discarded here.
4. An answer gets synthesized. The model writes prose from what survived, naming the businesses that appeared consistently across sources it trusted.
5. Citations get attached. Sometimes. Perplexity averages nearly 22 citations per response. Claude averages closer to six. ChatGPT sits lower still. Being cited and being named are not the same thing, and both matter.

The critical point is step 3. The model is not ranking you. It is deciding whether enough independent sources agree about who you are and what you are good at. That is a fundamentally different test from the one Google’s index applies, and it is why sites that rank beautifully can be entirely absent from AI answers.

Your website is about 4.5% of the answer

This is the finding that changes how you should think about the whole discipline.

Omniscient Digital analyzed 23,387 branded citations across the five major AI engines and broke down where they came from:

Sit with that last row for a moment.

You can rewrite your homepage forty times. You can add schema to every template, restructure your headings, fix every Core Web Vitals warning. All of it is worth doing, and none of it addresses ninety-five percent of the evidence the model is actually reading.

AI models do not take your word for it. They ask around.

Which means the work that moves AI visibility looks less like traditional on-page SEO and more like reputation engineering: getting onto the listicles, correcting the directories, earning the press, building the review base, and making sure that everywhere your business appears, it is described in consistent and specific terms.

Any agency selling AI optimization as a set of on-page changes has either not read the data or is hoping you have not.

Every AI model works differently. Dramatically so.

There is no such thing as “optimizing for AI”. There are five or six distinct retrieval systems, each with its own index, its own source preferences and its own temperament.

Two rows in that table deserve a second look, because they cut against intuition.

Gemini rewards your own website more than any other engine. Over half its citations point at brand-owned domains. If your content is genuinely good, Gemini is where that shows up fastest.

Google’s own two surfaces barely agree with each other. AI Overviews and AI Mode reach semantically similar conclusions 86% of the time while citing the same URLs only 13.7% of the time. They are built on the same index and they still disagree about who to credit.

If Google cannot agree with itself, the idea that one strategy covers all platforms does not survive contact with the evidence.

Multiple independent studies have landed on the same uncomfortable conclusion from different directions.

• A five-engine study covering 22.7 million citations across 1.1 million questions found that around 80% of cited websites appeared on only one engine. At page level, 85%.
• Kevin Indig’s analysis across three engines put the figure at 91%.
• BrightEdge measured overlap between any two engines’ cited sources running as low as 16%.
• Ahrefs studied 15,000 prompts and found only 12% of URLs cited by AI assistants ranked in Google’s top 10 for the same query.
• Semrush found 62% brand disagreement across ChatGPT, AI Mode and AI Overviews. No single brand dominates everywhere.

Put plainly: winning on one platform tells you almost nothing about the others. And ranking on Google, which remains valuable for its own reasons, transfers to AI answers far less than anyone assumed it would.

This is the single strongest argument for treating AI optimization as its own workstream with its own measurement rather than something bolted onto an SEO retainer.

Here is where we have to be straight with you, because a lot of the industry is not.

No AI company publishes ranking factors. There is no AI equivalent of Google’s Quality Rater Guidelines. Anyone handing you “the twelve GEO ranking factors” has assembled them from inference and confidence, not documentation.

What we do have is a growing body of citation research and our own testing. That supports a set of signals with real evidence behind them:

And the things we will not claim to know: the actual weightings, how recency trades off against authority, or why a model names you in one run and not the next.

We measure outcomes instead of asserting mechanisms. Named rate across a fixed prompt set, who gets named instead of you, which sources were cited, and whether those numbers move. That is provable. A score invented against an unpublished algorithm is not.

The terminology is still settling and a lot of it is marketing noise. Two distinctions are worth understanding.

Generative Engine Optimization (GEO) covers how content gets retrieved, chunked and synthesized into generated answers. It is the technical heart of the work: retrieval mechanics, passage structure, crawler access.

Answer Engine Optimization (AEO) predates the current AI wave. It grew out of featured snippets, People Also Ask and voice search, and covers winning the direct answer in all its forms.

They overlap. Anyone insisting on a hard boundary is selling something. We use AI optimization as the umbrella because it is the term that actually describes the outcome: being recommended.

If you want the full breakdown, we wrote one: SEO vs GEO vs AEO.

Each of these works differently enough to warrant its own approach. We track and optimize for all six.

ChatGPT SEO

Bing index, reviews, directories, Wikipedia

Read the guide →

Gemini SEO

Gemini, AI Mode and AI Overviews, three distinct surfaces

Read the guide →

Claude SEO

Entity clarity, no listing to claim, the strictest examiner

Read the guide →

Perplexity SEO

Freshness, community, the most citation-generous engine

Read the guide →

Copilot SEO

Bing, enterprise context, the cheapest to win

Read the guide →

Grok SEO

Real-time retrieval from X

Covered in every program

Fair scepticism, and we would rather address it than dodge it.

Some of what we do is recognizably the work of an SEO agency. Technical foundations, content quality, authority building. Those fundamentals did not stop mattering.

But three things are genuinely different, and they are not cosmetic.

The evidence base sits outside your site. Traditional SEO is largely about what you publish and who links to you. AI recommendation is about what independent sources say about you. That 4.5% figure is not a tweak to the old model, it is a different model.

Measurement is completely different. There is no rank position. No Search Console. You measure by running prompts, repeatedly, and scoring the spread, because the same question returns different answers on different days.

The target moves per platform. Six systems, minimal citation overlap, different source preferences. One strategy does not cover them.

What we will not do is pretend this is a solved science. Anyone quoting you exact ranking factors is guessing. We measure outcomes, we report the numbers honestly, and we tell you when something is not working.