AEO vs. SEO: Why Your Google Rankings Don't Tell the Whole Story Anymore

Google rankings are a lagging indicator of discoverability. With AI engines now answering queries directly, the visibility model has split in two — and most businesses are only tracking half of it.

Two Different Questions, Two Different Engines

When someone opens Google and searches "accountants in Murray KY," they're browsing. They want a list of options. Google's job is to return ranked pages, and your job is to be on that list. That's the SEO game, and it's been the dominant model for about 25 years.

When someone opens ChatGPT and asks "who's a good accountant near Murray, Kentucky I should call," they're not browsing. They want an answer. One answer, maybe two. The engine's job is to surface a trusted entity and present it as a recommendation — and your job is to be that entity. That's AEO: Answer Engine Optimization.

These are fundamentally different optimization targets. SEO optimizes for page rank in a list. AEO optimizes for entity recognition in a recommendation. The signals that drive each one overlap in places, but they're not the same, and conflating them is why a lot of businesses with solid Google rankings are invisible in AI-generated answers.

How the Numbers Have Shifted

Here's the data signal I find most clarifying: in 2023, essentially zero commercial queries were answered by AI engines in a way that displaced a Google search. By mid-2025, estimates from multiple analytics firms put AI-assisted search interactions — where a user got their answer from an AI engine without clicking through to a website — somewhere between 25% and 35% of total search volume, depending on query type. For informational and recommendation queries, the percentage is higher.

That doesn't mean SEO is irrelevant. Google still processes enormous query volume, and organic search still drives significant traffic. But the ratio of "queries that end at a ranked page" to "queries that end at an AI-generated answer" is shifting — and it's shifting faster for the categories that matter most to local and service businesses: recommendations, comparisons, and "who should I hire" queries.

What AI Engines Actually Do Differently

Google indexes pages. It follows links, reads content, and ranks documents based on hundreds of signals. When you search, it returns those documents.

AI engines like ChatGPT, Perplexity, and Google's AI Overview do something different. They synthesize. They pull from their training data, from real-time retrieval in some cases, and from structured information sources to construct an answer. They're not returning your page — they're deciding whether your business is a recognized entity worth citing.

That distinction matters because the inputs are different. An AI engine assessing your business looks at: whether your entity is recognized across multiple authoritative sources, whether your website has structured data that explicitly declares what you are and what you do, whether there are citations of your business in contexts the AI considers credible, and whether your content answers the specific conversational questions your customers are asking.

A high-ranking website with thin structured data, inconsistent business information, and no external citations can rank well in Google and be completely invisible to an AI recommendation engine. I've seen this pattern enough times to call it a reliable failure mode.

The Practical Implication: You Need Both, Weighted Differently

I'm not here to tell you to abandon SEO. You shouldn't. The two disciplines share a foundation: quality content, clear site structure, and legitimate authority signals matter for both. But the AEO-specific layer — structured data, entity consistency, conversational content, llms.txt — is largely absent from traditional SEO work, and that gap is growing more costly as AI query volume increases.

My recommendation is a two-layer audit. First, establish where you stand on AEO signals — you can run a baseline score at citeddigital.com/aeo in about two minutes. Then prioritize based on which signals are highest-impact and currently missing. The businesses that build entity recognition now will have a compounding advantage as AI-powered discovery continues to grow.