"AEO vs SEO" gets asked a lot, and answered badly a lot too. Most explanations amount to "it's the same thing, but for AI", which isn't wrong exactly, but it isn't useful either. It doesn't tell an agency what to actually change about how it audits, sells or delivers.
We had a large enough dataset to answer the question properly. For our AI Visibility Report, we pulled 10,000 US local businesses from the Insites 360-degree audit platform and re-ran them through a clean, standardised audit, analysing each one through ChatGPT and Perplexity alongside traditional SEO, local SEO, listings and review data. Four real differences showed up consistently. Here they are.
1. From pages to entities
In SEO, the unit of competition is the page. A single well-optimised page can rank on its own, even if the rest of the site is thin.
AI doesn't work that way. It doesn't return a ranked list of pages, it evaluates the business as a whole entity, pulling together signals from the website, Google Business Profile, directories, reviews and third-party sources into one combined view. A strong page on an otherwise inconsistent, sparsely-listed business won't carry the same weight it would in classic SEO.
The practical shift: stop asking "which page should we optimise" and start asking "how complete and consistent is this business's footprint everywhere it appears". Insites' AI visibility audits are built around that same shift, scoring entity-level completeness rather than grading pages one at a time.
2. From optimisation to validation
Traditional SEO rewards optimisation. Fix the technical issues, tighten the metadata, add the structured data, and rankings tend to move.
Across our dataset, that relationship was far weaker for AI. Missing titles were the one technical factor with a strong correlation to visibility; almost everything else, from duplicate meta descriptions to heading structure to Open Graph completeness, showed weak or no meaningful relationship at all. Core Web Vitals didn't move the needle either: businesses surfaced by AI averaged a performance score of 59, against 62.4 for those that weren't, direction and all, a finding we tested on its own and confirmed in does website speed matter for AI search?.
What did correlate strongly: review volume, local pack presence, and consistency of business data across directories. AI isn't scoring technical polish, it's asking a blunter question: is there enough evidence out there that this business is real, active and legitimate?
3. From ranking signals to signal accumulation
SEO is built around discrete, improvable levers: fix the title tag, earn the backlink, target the keyword, watch the ranking move. Cause and effect are traceable.
AI recommendation doesn't behave like that. No single factor dominates. Businesses that get recommended tend to have more of everything at once: more reviews (304.2 average for the most recommended tier, versus 117.5 for those never recommended), more directory listings, more search visibility, more local pack presence. The effect is cumulative rather than attributable to any one change.
This is the traceability problem baked into AEO. You can't point at one fix and say "that's what got us recommended". You can only say the business now has more corroborating evidence across more places, and that, in aggregate, moves the odds. It's exactly the dynamic we unpack in the scale advantage, where bigger brands compound this kind of evidence by default.
4. From precision to presence
One of SEO's quiet advantages for small businesses is that precision can beat scale. A niche local page targeting a specific service and location can outrank a much bigger competitor, because relevance to that one query matters more than overall size.
That advantage shrinks under AI. Because recommendation rewards breadth and volume of corroborating signals, businesses with more reviews, more listings, more content and more visibility hold a structural edge, independent of how precisely targeted any single asset is. The gap in traditional search visibility between recommended and non-recommended businesses illustrates the scale of this: 29,044 average monthly organic visits for businesses surfaced by both platforms, against just 661.8 for those surfaced by neither. That's not a ranking gap, that's a different category of business.
Why this makes GEO harder to measure than SEO
Put the four together and a practical problem falls out: AI visibility is genuinely harder to track than a keyword position.
There's no fixed rank to monitor. Responses vary by phrasing and context. And the factors that move outcomes (overall presence, consistency, review volume) are diffuse, not attributable to a single action the way a technical fix is.
That means reporting has to change too. Instead of tracking position for a keyword, track:
- Whether the business is being surfaced at all, across a consistent set of representative queries
- How it's being described when it is surfaced (sentiment, accuracy of details)
- Whether the underlying signals (reviews, listings consistency, local pack presence) are trending up over time
AEO isn't SEO with a new acronym. It's a shift from optimising discrete assets to building and proving a business's overall credibility across the web, and that changes what agencies audit, sell and report on.
This analysis is drawn from Insites' AI Visibility Report, based on a clean audit of 10,000 US local businesses across ChatGPT and Perplexity. Download the full report, or run a free AI visibility check on any business in seconds.
Andrew is the co-founder and CEO of Insites, the AI visibility platform that agencies, telcos and media companies use to show local businesses how search engines and AI tools like ChatGPT and Gemini find, trust and recommend them. He has spent more than 15 years in digital marketing and UX, and co-hosts The Unusable Podcast on UX design.
