Your local 3-pack does not carry into AI

Local AI visibility is whether an assistant names your specific location when a nearby customer asks for a business like yours, and it is a far narrower outcome than ranking in Google's local 3-pack.

Most multi-location teams treat the two as one funnel. They audit their Google Business Profiles, see healthy 3-pack coverage, and assume the assistants draw from the same well. The 2026 Local Visibility Index, which analyzed 2,751 brands and roughly 350,000 locations in the United States, does not support that assumption: brands appear in Google's local 3-Pack 35.9% of the time on average, but only 1.2% of locations get recommended by ChatGPT and 11.0% by Gemini (SOCi).

#The gap is not a ranking gap

Perplexity sits between the two, recommending brand locations 7.4% of the time, which makes the AI surfaces roughly 3 to 30 times more selective than traditional local search (SOCi). Selectivity is the right word. These are not the same contest scored more harshly.

The 3-pack answers a bounded question: of the businesses near this user, which three best match? Google names relevance, distance and prominence as the factors behind that, and says plainly that there is no way to request or pay for a better local ranking (Google Business Profile Help). Distance does a lot of work there. It caps the candidate set to something small and geographic before quality is weighed at all.

An assistant answering "best sushi in Fishtown" is not running that scoped comparison. It assembles a short list from whatever it can reconcile across sources, and a short list of three or four names drawn from a national corpus is a much harder seat to win than a slot in a proximity-filtered pack. Your 3-pack coverage is evidence that Google's index has you. It is not evidence that an assistant can defend naming you.

#There is a rating floor, and the 3-pack does not have one

Locations that ChatGPT recommended averaged a 4.3 star rating, and Gemini's averaged 3.9 (SOCi). Treat those as floors rather than targets.

A location can hold a 3-pack slot at 3.6 stars because proximity and prominence carry it. Nothing carries a 3.6 into an assistant's answer. The assistant is composing a recommendation it has to stand behind in prose, and a mediocre rating is the most legible disqualifier available to it. This is the mechanism behind the pattern practitioners keep reporting: locations below roughly four stars are underrepresented in AI recommendations relative to where they rank in search.

Review responses are the other half of that signal, and they are thin. Businesses respond to 46.9% of their Google reviews, at an average response time of 4.3 days, and to just 3.1% of their Yelp reviews (SOCi).

#One in three AI local facts is wrong

The accuracy split across engines is the most useful number in the whole index. ChatGPT's local business data came back 68.3% accurate, Perplexity's 68.0%, and Gemini's 100.0%, because Gemini is grounded in Google Maps (SOCi). Roughly one in three AI-generated recommendations carries an incorrect address, phone number or set of hours.

Gemini's perfect score is not a sign that Gemini is smarter. It tells you the mechanism. When an assistant is grounded on one authoritative record, local accuracy is solved. When it is synthesizing from scattered sources, your facts are only as good as the worst copy of them in circulation.

That changes where the work pays off. Google Business Profile hygiene is already close to saturated across the industry: 97.9% of locations are claimed, with 86.6% profile completeness (SOCi). The non-Google surfaces are where the holes are. Only 80.0% of locations present on Google are also found on Yelp, and only 53.4% on Facebook (SOCi). Nearly half of the locations a brand operates simply do not exist on Facebook. The two engines that are not grounded in Maps are reading exactly those thinner, patchier sources, which is consistent with the broader pattern that most of your AI citations do not live on your own site.

#The recommendation is a doorway, not a decision

Consumer behavior moved fast here. The share of consumers using AI tools to find local business recommendations went from 6% to 45% in a year, measured across 1,002 US adults (BrightLocal). Among the 455 respondents who had actually used AI this way, 63% said they trust the recommendations (BrightLocal).

They also verify. 88% of AI users check the sources behind a recommendation, either to confirm the business is legitimate or to see where the claim came from, and 59% go read the review profile itself (BrightLocal). ChatGPT was used for this by 31% of consumers and Google AI Mode by 23% (BrightLocal).

Put the accuracy figure next to the verification figure and the failure mode is obvious. The assistant names you, quotes hours that closed two years ago, the customer clicks through to a profile that disagrees, and you lose them at verification rather than at discovery. That loss never shows up in any AI visibility report, because the report counts the mention as a win.

#What this means for multi-location brands

Four things follow, in order of payoff.

Fix the rating floor before anything else. A location parked at 3.7 stars is not going to be optimized into an assistant's short list by better copy on your local pages. It is below the bar the engines appear to apply.

Reconcile the surfaces Gemini is not grounded on. Yelp and Facebook coverage gaps are not a listings-hygiene footnote for AI visibility. They are the input to two of the three major engines.

Audit hours, phone numbers and addresses as facts in circulation, not as fields in your GBP dashboard. The question is not whether your record is right in one place. It is whether every copy an engine can reach agrees, which is the same entity consistency problem that governs non-local brand mentions.

Measure per location, never per brand. A brand-level AI visibility score is close to meaningless when 1.2% of locations are getting recommended. SOCi notes that LLMs often favor single-location businesses because their data, reputation and activity signals are simpler and more unified, while multi-location brands operate at a complexity the models struggle to interpret consistently (SOCi). Your aggregate is hiding the fact that a handful of locations carry your entire AI presence.

The uncomfortable version of all this: local AI visibility is not a content problem and mostly not an SEO problem. It is a data reconciliation problem wearing a marketing hat. The brands that win it will be the ones that treat every third-party copy of their location data as production infrastructure.