Published September 9, 2026
Does having multiple locations make it harder for AI to recommend my business?
Yes, and the gap is bigger than most owners expect. A 2026 analysis of nearly 350,000 business locations found that ChatGPT named just 1.2% of them in its answers, compared with a 35.9% appearance rate in Google's local 3-pack. If you run more than one location, that gap compounds: each address has to independently earn the same trust, and a strong flagship location does not carry the rest.
What this means
A Google local pack ranks locations against a fixed radius and query, so proximity and relevance do most of the work. An AI answer works differently: the engine treats each address as its own entity and asks whether it has enough corroborated, consistent information to vouch for that specific location by name. Ranking well in one city does not transfer to a location twenty miles away with a different phone number format, an unclaimed Google Business Profile, or unanswered reviews. Across the research described below, AI platforms recommended only 1.2% to 11% of the locations analyzed, versus 35.9% average visibility in Google's local 3-pack, a gap the report's authors put at three to thirty times harder depending on the platform (Search Engine Land, 2026).
Photo: "Outlined map location pointer icon on transparent background. Map pin for target or destination." by suzannademey, CC BY 2.0
Who this applies to
Any Central Florida business operating more than one office or service area, including multi-office law and dental practices, contractor companies covering several counties, med spas and clinics with a flagship and satellite locations, and multi-location franchises. The more locations you run, the more entities an AI engine has to separately verify, which means the same inconsistency that costs one location a citation can cost you several. Single-location businesses face a version of this problem too, but multi-location businesses face it at scale, address by address.
How we'd evaluate it
A proper evaluation checks each location independently rather than treating the business as one entity. That means confirming name, address, and phone number match exactly across every location's Google Business Profile, website location page, and directory listings; checking whether each location's profile is fully filled out with current hours, categories, and photos; and reviewing star ratings and response rates per location, since the same research found AI-recommended locations averaged 4.3 stars on ChatGPT and 3.9 to 4.1 stars on Gemini and Perplexity. It also means testing the actual customer question, such as "best [service] near [neighborhood]," separately for each location, because a strong result at one address does not predict the next.
Available options
- Run the DIY version. Test each of your locations individually across ChatGPT, Perplexity, and Google AI, and note which ones the AI cannot confidently name.
- Fix the mechanics yourself at scale. If you have ten or more locations under one business name, Google's bulk verification and location-group tools let you manage and correct listings at scale rather than one at a time (Google Business Profile Help, n.d.).
- Get a per-location audit. Below that ten-location threshold, or when the gaps are more than a data-entry problem, a location-by-location audit finds which specific listings, reviews, or missing details are excluding each address from AI answers.
Benefits, limitations, and tradeoffs
The benefit of testing per location is that it catches a quiet, compounding problem before it costs you customers at more than one address, since a single mismatched phone number can be a rounding error for one location and a pattern across five. The limitation is the same one that applies to any AI visibility work: no one, including AnswerFoundry, can guarantee that a given location gets named, because these are third-party systems that change without notice. What is controllable is the underlying data and reputation signals each location presents, not the outcome in any single AI session.
What we know
The clearest public data here comes from SOCi's 2026 Local Visibility Index, which analyzed performance across nearly 350,000 locations belonging to 2,751 multi-location brands. It found AI platforms recommended locations far less often than Google's local pack surfaced them: 1.2% on ChatGPT, 11% on Gemini, and 7.4% on Perplexity, against 35.9% in Google's local 3-pack, and that fewer than half of the brands leading in traditional local search also led in AI recommendations. In retail specifically, the overlap between the two was 45% (SOCi, 2026; Search Engine Land, 2026). The same research found AI systems treat reviews as a pass-or-fail filter rather than a ranking input: locations with ratings near 3.4 stars and review response rates under 5% were excluded from AI recommendations entirely, not merely ranked lower (Search Engine Land, 2026). Google's own guidance on managing multiple locations confirms the practical fix starts with consistent, complete data across every listing (Google Business Profile Help, n.d.).
Next steps
Pick your two or three highest-revenue locations and test them today, separately, on ChatGPT, Perplexity, and Google AI, using the phrasing a real customer would use. Note which locations the AI names confidently, which it skips, and which it gets wrong. That short exercise usually reveals whether you have a one-location problem or a systemic one, and which locations to fix first.
Orlando considerations
Central Florida businesses with locations spread across Orlando, Winter Park, Kissimmee, and the surrounding counties face a version of this problem even at a small scale. Two or three offices is enough for inconsistent NAP data or an under-reviewed satellite location to quietly disappear from AI answers while the flagship location still shows up fine. The market's density in categories like med spas, dental practices, law firms, and home services means AI engines have more competing options nearby, which raises the cost of any one location's inconsistency.
Frequently asked questions
Does this apply if I only have two or three locations, not hundreds?
Yes. The research behind these numbers covered large multi-location brands, but the mechanism, each location gets verified independently, applies at any scale. Two locations means two entities that each have to earn the same trust; there is no volume discount.
Which AI platform is easiest to get recommended on?
Based on the 2026 data, Gemini recommended locations more often than ChatGPT or Perplexity, largely because it is grounded directly in Google Maps data, which was about 100% accurate versus roughly 68% on ChatGPT and Perplexity. That is a reason to prioritize Google Business Profile accuracy first, not a reason to ignore the other platforms.
Do I need at least 10 locations to use Google's bulk verification tools?
Yes. Google's bulk verification and location-group features require at least 10 locations under the same business name and category. Below that threshold, each location's Business Profile is managed and corrected individually.
Can one bad location hurt the AI visibility of my other locations?
Not directly, since AI engines evaluate each location as its own entity. But if one location's poor reviews or inconsistent data reflect a pattern across your listings, such as the same outdated phone number or the same unanswered reviews, that pattern can affect how confidently an AI engine treats your other locations too.
References
Goodwin, D. (2026, January 28). AI local visibility is up to 30x harder than ranking in Google: Report. Search Engine Land. https://searchengineland.com/ai-local-visibility-report-2026-468085
SOCi Inc. (2026, January 28). In AI-driven discovery, few brands are chosen, most disappear. SOCi. https://www.soci.ai/news/in-ai-driven-discovery-few-brands-are-chosen-most-disappear/
Google Business Profile Help. (n.d.). Bulk location management overview. Google. https://support.google.com/business/answer/3217744
Last updated: September 9, 2026
If your flagship location shows up in AI answers but your other locations don't, that's exactly the gap a per-location AI visibility audit is built to map, with a prioritized list of what to fix first at each address.