Google's AI Overviews now sit above the blue links on most informational searches. Perplexity, ChatGPT, and Gemini do the same inside their own apps. Users get their answer at the top of the page, and the click that used to follow it often doesn't happen.
That changes what "winning" looks like. A page-one ranking still matters, because it is part of what models pull from, but it stops being the finish line. The finish line is getting cited inside the answer, recommended when someone asks for one, and recognized as a credible source when the model is composing on its own. Those are three separate problems, with three separate names.
1. AEO (Answer Engine Optimization): Becoming the Cited Source
Answer Engine Optimization is the narrowest of the three. The goal is for a model to pull your sentences into the box at the top of a query result, whether that's the AI Overview, a Perplexity citation, or the ChatGPT answer with a source link.
LLMs do not appreciate your writing. They extract from it. They want a clean span of text that resolves a question with low latency and high confidence. To get pulled, your page has to make that span easy to find.
- Frame headings as the question the user actually typed. "How much does a commercial HVAC retrofit cost?" beats "Commercial HVAC: Pricing Considerations."
- Put a 40-to-80-word direct answer immediately under the heading. Save the caveats, context, and edge cases for the paragraphs below.
- Repeat the same factual claims across your documentation, transcripts, and FAQ content. Models trust facts that show up in multiple places under your own domain.
Skip the keyword-volume tools for this layer. The phrasing you want is sitting in your support tickets, sales-call transcripts, and the questions that come up in your industry's subreddits and forums. People ask AI the way they actually talk, and that is the phrasing your headings should match.
2. GEO (Generative Engine Optimization): Earning the AI Recommendation
Generative Engine Optimization is what happens when a user asks the model for a recommendation rather than an explanation. "What are the best three CRMs for a 12-person sales team?" "Who builds aluminum extrusion presses for the food industry?" The model returns a short list. You are either on it or you are not.
Being cited inside an answer is not enough here. The model has to be able to defend the choice. That means a visible pricing page (or at least a public starting price), a comparison page that names alternatives instead of dancing around them, and case studies that attach a specific number (deployment time, cost reduction, throughput) rather than "delighted customers."
Format also matters more than people expect. Tables, spec sheets, and structured feature lists get parsed cleanly. Long marketing prose with the same information buried inside it does not. Two pages can rank identically in Google and still produce very different outcomes when an LLM is asked to choose.
3. AIO (AI Optimization): Building Your Global Brand Memory
AI Optimization is the layer underneath the other two. Before a model can cite you or recommend you, it has to have heard of you in enough places to treat you as a real entity. That recognition is built off your site, not on it.
Schema markup is the on-site piece, covering Organization, Product, FAQPage, and Person types with sameAs links to LinkedIn and Wikidata. It tells the crawler what you are. The harder work happens off your site, in trade publication mentions, podcast appearances, third-party reviews, and forum threads where your product gets named by users. AI models weight unlinked mentions on credible sites more heavily than the backlinks SEO has spent decades chasing.
There's a simple test for whether AIO is working. Ask Claude, ChatGPT, and Gemini to describe your company without letting them browse the web. If the descriptions are accurate and consistent, the brand is in the model's memory. If one of them invents a product you don't sell, or names a city you don't operate in, your off-site footprint is too thin and the model is filling gaps with guesses.
The Bottom Line
Ranking optimization assumed a single funnel of search box, results page, click, conversion. AI search splits that into three jobs that have to be done separately. AEO is content structure. GEO is competitive evidence. AIO is everything the rest of the web says about you when nobody from your team is in the room.
A team that ignores all three will keep producing pages that score well in old tools and disappear in new ones. A team that picks one and treats it as the whole strategy will get part of the way there. The teams that show up consistently in AI answers (cited, recommended, and recognized) are the ones treating these as three distinct problems on three different timelines.
Want to see where your site stands across all three? AI Ready audits each layer and shows you what models actually understand about your brand. aiready.cat