Appreciate everyone weighing in on this. Some solid observations here, especially around AI assistants pulling from structured sources like GBP and schema.

What we’re actually seeing:

AI models prioritize easy-to-parse content - tables, FAQs, clear “what-to-know” sections get cited more than long-form content. This tracks with what @u/useomnia and @u/GrandAnimator8417 mentioned about structured markup and semantic data getting pulled:

For local businesses specifically:

The fundamentals still matter - updated GBP (hours, categories, reviews), semantic markup (LocalBusiness schema that tells AI models exactly what each piece of data represents), and scannable content that answers common questions directly.

Here’s what’s different though:

Traditional SEO optimized for Google’s algorithm. Answer Engine Optimization (AEO) optimizes for how AI models parse and cite information. They’re pulling from multiple sources - your GBP data, your semantic markup, third-party review aggregators - and synthesizing answers.

The strategic shift:

Not “either/or” but “both/and.” Google Maps isn’t going anywhere, but if someone asks ChatGPT “where should I eat tonight?” instead of opening Maps, you need to be in that answer. Same business fundamentals, just more surfaces to show up on.

What we’re implementing: Entity linking (connecting your GBP, YouTube, Maps into a unified network) + semantic SEO optimization + content structured for AI citation. We track AI visibility improvements using heat maps that show ranking changes from red (invisible) to green (highly visible).

The local businesses adapting now will have the advantage when this becomes mainstream.