AEO vs. SEO: How I'm Optimizing for AI Answer Engines Instead of Just Google

Corey Fox — Mon, 13 Jul 2026 18:06:05 +0000

For most of the last decade, my job as an SEO specialist had a clear north star: rank on the first page of Google. But over the past year, the way people actually find answers has shifted under our feet. They're asking ChatGPT, Claude, Perplexity, and Google's own AI overviews - and those tools often answer the question without anyone ever clicking a blue link.

That shift is why I've been rethinking my whole approach and leaning into what people are starting to call AEO - Answer Engine Optimization. Here's how I think about the difference, and what I've actually changed in my workflow.

SEO vs. AEO: what's the real difference?

Traditional SEO optimizes for a ranked list of links. The goal is to earn a position, win the click, and land the visitor on your page.

AEO optimizes for being the answer. When someone asks an AI assistant a question, you want your content to be the source that gets synthesized, cited, and surfaced - even when there's no click involved.

They overlap a lot. Good SEO fundamentals still matter. But the optimization targets are different:

SEO cares about rankings, CTR, and sessions.
AEO cares about being retrieved, quoted, and cited by a model.

What I actually changed

Here are the concrete shifts I've made in how I structure and audit content.

1. I write for extraction, not just ranking

AI answer engines pull discrete, self-contained facts. So I structure content in clear question-and-answer blocks, use descriptive headings that mirror how people phrase questions, and put the direct answer first before the supporting detail. Front-loading the answer makes it far easier for a model to lift a clean, accurate snippet.

2. I lean even harder on structured data

Schema markup (JSON-LD) was always part of technical SEO, but for AEO it's become essential. FAQPage, HowTo, Article, and Organization schema give machines an unambiguous map of what your content means. I treat schema as the machine-readable summary of every important page now, not an afterthought.

3. I optimize for entities and clarity, not keyword density

Answer engines reason about entities and relationships, not keyword counts. I make sure key terms are defined plainly, that context is explicit rather than implied, and that a page can stand on its own without the reader needing five other tabs open.

4. I audit citations, not just rankings

My new KPI question isn't only "where do we rank?" - it's "does the model cite us?" I regularly ask ChatGPT, Claude, and Perplexity the questions my clients want to own, and I note who gets cited. If a competitor keeps showing up as the source, that's a content gap to close.

5. I built tooling to make this repeatable

Because I run this across multiple clients, I've been building automation around it - a project I'm calling claude-aeo - to help run answer-engine audits, test prompts against real content, and flag pages that need better structure. You can check out the claude-aeo project on GitHub

What hasn't changed

I want to be clear: AEO is not a replacement for SEO. Crawlability, site speed, Core Web Vitals, internal linking, and genuinely useful content still form the foundation. A page that a model can't crawl or trust won't get cited any more than it would rank. AEO is a layer on top of solid technical SEO, not a substitute for it.

Where I think this is going

The traffic model is changing. "Zero-click" answers will keep growing, and being the cited source inside an AI response is becoming as valuable as ranking #1 used to be. The teams that win won't abandon SEO - they'll extend it, structuring their content and data so that both search crawlers and answer engines can understand, trust, and surface it.

If you're working on this too, I'd love to compare notes - what signals are you seeing move the needle for AI citations? Drop a comment below.

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