Search “plumber near me” on Google, then ask ChatGPT the same thing. You’ll get two different shortlists. They’re built on two different sets of rules, and optimizing for one doesn’t guarantee a spot in the other.

This was the core of my local AEO talk at BrightonSEO in October 2026, and it’s the first post in a short series on winning local AI search.

Where the rules diverge

Google local results AI answer engines
Ranking driver Proximity, relevance and prominence Proximity first, above all else
Trust weighting Roughly even across business size Skews toward big, name-brand chains
Reviews Volume carries more weight Rating beats volume
Sources Google’s own index A blend of Yelp, Google Maps, forums and more
Based on my side-by-side testing, not a published ranking formula. Treat it as observed behavior.

Rating beats volume

In my headline example, a business with 40 reviews at 4.8 stars was picked by ChatGPT over one with 1,000 reviews at 4.0 stars. Google’s local results tend to favor the 1,000-review shop as the safer bet. When I asked ChatGPT why it recommended a business, the answer was close to a summary of this: nearby, and a strong review history.

Rating beats volume in AI answers Two plumbing businesses. Shop A has 1,000 reviews at 4.0 stars and is the type Google’s local results tend to favor. Shop B has 40 reviews at 4.8 stars and was the one ChatGPT recommended. Same query, two winners Shop A 4.0 ★ 1,000 reviews Google tends to favor Shop B 4.8 ★ 40 reviews ChatGPT recommended In AI answers, review quality outweighed a 25x larger pile of reviews.
An example from my BrightonSEO 2026 talk. One observed case, not a measured rate.

The practical read is that review quality is now worth more than review count. A steady stream of genuine five-star reviews does more for AI visibility than a bigger pile of mediocre ones.

No single dataset

ChatGPT’s map view runs on Mapbox, but the business cards underneath blend Yelp and Google Maps data into one shortlist. That means there’s no single listing to fix. Your Google Business Profile, your Yelp page and the rest of your citations all feed the same answer, so inconsistencies anywhere can cost you. Google’s own guidelines for representing your business are still the baseline for getting your name, address and categories right.

This lines up with the 2026 Local Search Ranking Factors report from Whitespark, which added an AI search visibility column for the first time. Its author noted that three of the top five AI visibility factors are citation factors, and put it this way: “in AI SEO, mentions (citations) are the new link.” The basics I called 2020-era tactics, like a consistent name, address and phone number, are the foundation the new signals sit on. (One caution: AI citations churn faster than most people expect, which I wrote about in citation decay.)

The trust gap

Because AI engines lean toward established names, franchises and big brands get a head start. Newer independents have to earn trust some other way: strong ratings, review text that names specific services, and mentions on other sites. My next post covers exactly how.

What to do this quarter

  1. Audit NAP (name, address, phone) consistency across Google, Yelp, Bing and the main directories for your category.
  2. Shift review asks from “leave us a review” to “tell people which service we did for you.”
  3. Check how your category’s shortlist looks in both Google and ChatGPT, from the neighborhoods you serve, and note who shows up in each.
  4. Track rating, not just review count, as a headline local KPI.

If you’re measuring this, tie it back to leads and revenue, not just visibility. I covered why in the AI search attribution problem.

Want to see how AI answer engines rank you against local competitors? We do this for businesses in Fayetteville, Bentonville and beyond. Get in touch.


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