What is Answer Engine Optimization (AEO)?

Search behaviour has shifted faster than most marketing plans have. Around 900 million people now use ChatGPT every week (OpenAI, via TechCrunch, Feb 2026), and when Google shows an AI summary, people click through to a website on just 8% of visits, down from 15% without one (Pew Research Center, 2025). In B2B, 45% of buyers said they used AI tools during a recent purchase (Gartner, 2025).

The practical consequence is simple: when the answer comes back as three names and yours isn't one of them, you never enter the consideration set. The buyer acts on what the assistant said, and you never see the visit you lost.

SEO optimised a page to rank in a list the user then chose from. AEO optimises content so a model extracts and repeats it as the recommendation. The shift, in plain terms:

• Keywords become questions, the exact way a buyer phrases it to an assistant.
• Rankings become mentions and citations, are you named, and is your page cited as a source?
• A blue link becomes a named recommendation, the assistant does the choosing.

SEO doesn't disappear, the pages an AI reads are often the pages that already rank. But the goal moves from “be on the page” to “be in the answer.”

Four levers do almost all the work, and you control every one of them on your own site:

1. 1

   Authoritative content that answers the question

   The single biggest lever. Publish the page that directly answers the buyer's question, in the format the question implies, a “best X for Y” query wants a guide that names the field, not a brochure. Write it so a model can lift a clear, correct answer from it.

2. 2

   Structured data the model can read

   Schema.org markup (Organization, FAQPage) states your facts in a machine-readable form. FAQPage here exists to be extracted by LLMs, it is not about winning a Google rich snippet. State real facts; never invent ratings.

3. 3

   Clean crawlability

   A sitemap and a robots.txt that allows the AI crawlers, GPTBot, ClaudeBot, PerplexityBot, are the load-bearing pair. An llms.txt file is low-cost hygiene that points them at your priority pages. If the bots can't read you, none of the above counts.

4. 4

   Owned profiles, kept complete

   Claim and complete the profiles you genuinely control, G2, Trustpilot, LinkedIn, Crunchbase, Wikidata. You can't buy your way into editorial coverage, but you can keep your own entries accurate so the model has good facts to draw on.

Honestly, it depends on the engine. Retrieval engines (Perplexity, and the web-search modes of ChatGPT and Gemini) pick up new pages within days of crawling them. A model's built-in training memory only shifts when it's retrained, so some wins are quick and some build over time. The right move is to publish, then re-check the answers on a cadence to watch the recommendation move.

1. 1

   Find out where you stand

   Ask the engines the exact questions your buyers ask, and see who gets named. You can't fix a gap you haven't measured.

2. 2

   Publish the answer you're losing

   For each question you're absent from, publish the page that answers it best, sized to your brand.

3. 3

   Make it readable, then measure

   Add the schema, keep the page crawlable, then re-check the answers to confirm the recommendation moved.