Answer Engine Optimization: How to Get Recommended by AI
Most AEO advice gets you retrieved, not recommended. The two are different games, and the one that wins is distinctiveness, not schema.
By Firoz Azees · Updated · Published · 6 min read
Getting cited by AI and getting recommended by AI are two different games. Most answer engine optimization advice wins the first one, and the first one sells nothing.
Here is the split. Ask ChatGPT "who should I use for X" and the model does two things in one breath. It retrieves a set of sources, then it chooses which brand to name. Retrieval is findability: is your content crawlable, structured, resolvable to a clear entity. Choosing is distinctiveness: of the options it found, is there a reason to name yours. Nearly every AEO checklist solves the first and skips the second. That is why brands do all the schema work and still never get named.
Findability is the entry fee
The mechanics are real and worth doing:
- Clean, stable URLs, and content that lives in the raw HTML. Most AI crawlers do not run JavaScript, so a client-side app can rank on Google and stay invisible to ChatGPT and Perplexity at once.
- Answer-shaped passages that stand alone, plus tables for anything comparative.
- A consistent entity the model can resolve: one description everywhere, a named founder, organization schema, sameAs links to LinkedIn and Crunchbase.
Do all of it and you have earned one thing: a seat in the retrieved set, not the recommendation. Treating findability as the finish line is the most common mistake in the field.
The part almost nobody works on
Two facts reset the strategy.
Your own website is a rounding error. AirOps found roughly 85% of what AI says about a brand comes from third-party sources1: listicles, communities, reviews, press. A strategy built on publishing more to your own blog fights for the smallest slice on the board. The work is off your site, which is why your website is source material now, not a destination.
The concentration is brutal. Hexagon found 3% of brands capture 71% of AI recommendations2. A small set of brands wins because a small set of sources gets cited, and those sources are rarely a brand's own domain. The engines also barely agree with each other. In cross-engine analysis, only about 11% of the domains one engine cites overlap with another's for the same prompt. Gemini leans on your owned site and the knowledge graph, ChatGPT leans on third-party directories, Perplexity leans on community and reviews. There is no single "AI" to optimize for. Where the match is really played is the away game, on grounds you do not own.
| What you control | Retrieval (get in) | Recommendation (get chosen) |
|---|---|---|
| Lever | schema, structure, entity clarity, freshness | a distinct, defensible point of view |
| Where it lives | your own site | third-party sources, plus your voice |
| Measured by | are you cited | are you named, per engine |
The uncomfortable part
Even with the technical work done, there is a wall. If your brand reads the same as every competitor, the model has nothing to recommend. It lists you, maybe. It does not choose you. Recommendation is a decision, and a decision needs a difference to turn on. AI is the new chooser, and brands it cannot tell apart do not get named.
This is measurable, which surprises people. Take your content and a vanilla model's draft of the same brief, and measure the distance between them. Two plain AI drafts sit almost on top of each other. If your brand's content sits there too, you published the category average, the same voice a model flattens every brand into, and the average never gets picked. It is the same reason a model hands you a generic answer when you ask it who leads a category. Distinctiveness is not a brand nicety here. It is the input that decides whether being findable ever converts into being named.
What to do
Do the technical work. It is the price of entry, not the strategy. Then spend the real effort on the two levers that move recommendation: earn third-party mentions on the sources each engine trusts, per engine rather than in aggregate, and build a point of view the model cannot get from anyone else. Then change your scoreboard. Stop counting traffic and start counting share of recommendation: the share of buyer queries where the model names you, per engine, against your competitors.
At Ivanooo, Firoz Azees built the instrument to measure exactly that, because traffic flatters you and share of recommendation is the number that predicts whether the engine names you. See where your brand sits across the engines: paste your URL, get the read, with the evidence.
Questions
What is answer engine optimization?
Is AEO just SEO with a new name?
Why does my brand get cited but never recommended?
Does my own website still matter for AEO?
How do I measure AEO success?
What makes AI recommend one brand over another?
About the author
Firoz Azees, founder of ivanooo. Fifteen years running growth for companies in Dubai, Singapore, London and Silicon Valley. Runs a 62-question AI answer panel and publishes what it measures.
Sources
Updated 23 Sept 2026. First published 10 Jul 2026.
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