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Key Takeaways
- Even a little bit of PR can go a long way. This is especially true for smaller geographies: medium-large cities, neighborhoods in metropoles and smaller countries where the competitive field is thinner.
- Brand name and geographic association have a major influence. AI systems appear to connect businesses to queries more easily when the name itself signals a location.
- Not every local industry is recommended in the same way. In some sectors, structured presence appears to matter more. In others, place association matters more.
- Dual-language content is a must for non-English markets. It helps AI systems connect the same business to multiple ways of asking the same local question.
In 2026, AI visibility, or generative engine optimization, has made its way into virtually every board meeting across tech, finance and B2B companies. Interestingly, though, the lowest-hanging fruit in GEO is not in these hypergrowth digital industries, but in good old boring local businesses.
And it’s not because a ton of people are starting to ask their AI of choice who’s the best barber in their area — which is actually happening too. BrightLocal found that consumer use of AI for finding local businesses jumped from 6% to 45% in March 2026 alone.
No, the reason is that showing up in these recommendations is actually more straightforward for local businesses than for virtually any other industry. In a recent study that I completed with my team, I looked at various local business sectors across three countries and six cities, analyzing close to 500 prompts and search results in the process.
Here’s what I learned.
Even a little bit of PR can go a long way
A surprisingly large number of local businesses have almost no earned media footprint at all. They may have a website, a Google Business Profile and a handful of directory listings, but nothing in local news, no inclusion in roundup articles and no third-party mentions that give a model more context to work with.
That matters because AI systems don’t rely only on business websites. They also appear to draw heavily from articles, rankings, listicles and other published references when deciding which businesses are worth surfacing. In practical terms, that means even a small amount of coverage can go a long way for a local firm that otherwise has very little public corroboration online.
This is especially true for smaller geographies: medium-large cities, neighborhoods in metropoles and smaller countries where the competitive field is thinner. If you’re a lawyer in New York, then you can obviously expect extremely strong competition even in terms of PR. Solicitors in Denver, on the other hand, basically have an open playing field.
Brand names have a major influence
If your name is somehow connected to your location, that may translate into a big advantage. For example, in the Denver neighborhood of Cherry Creek, dentistries such as Cherry Creek Dentistry, Cherry Creek Family Dentistry and Cherry Creek Dental Spa appear repeatedly in AI recommendations. In Leeds, Leeds City Dentalcare reflects the same pattern.
This doesn’t mean that every business should rename itself in order to satisfy a model. But I do think the finding is important. In local AI discovery, explicit place association appears to help systems connect the business, the geography and the query more easily.
Crucially, AI is less forgiving than a Google search. If only a very small share of firms is recommended, ambiguity becomes much more costly. A generic name may be manageable for a company that already dominates its market. For a firm still trying to establish clear local relevance, a name that directly reinforces place can do useful work before the AI even reaches the website or supporting citations.
Not every local industry is recommended in the same way
AI visibility is not uniform across sectors.
Among the sectors I tested, lawyers showed the strongest alignment between AI recommendations and traditional search. That likely reflects the structure of the legal category itself. Law firms tend to have stronger directory coverage, more standardized profiles and clearer practice-area labeling. In other words, the supporting data around them is often easier for both search engines and AI systems to interpret.
Accountants were the opposite. This category showed the biggest divergence between AI recommendations and traditional search, which to me suggests the widest strategic opening. When AI and search disagree that much, local visibility is less locked in by conventional rankings and more open to businesses that make themselves easier to recognize through clearer signals, better coverage and stronger local relevance.
Dentists sat somewhere else again. There, the naming effect was especially noticeable. Geographic association seemed to do more work, and the businesses that surfaced repeatedly often made their location legible directly in the brand.
The broader lesson is that there is no single local AI playbook. In some sectors, structured presence appears to matter more. In others, place association matters more. In others, the opportunity comes from the fact that the recommendation layer is still much less settled than search. That is why I think local businesses should stop asking whether they are “optimized for AI” in the abstract and start asking which signals matter most in their specific category.
How AI and traditional search diverge for local businesses
Our study also reinforced something that many operators are only beginning to appreciate: Local AI discovery is not just search with a chatbot interface.
This has two practical consequences. First, being visible in search does not mean a business will be visible in AI. Second, AI recommendation slots are much more concentrated. Search still gives many businesses some chance of being seen. AI often collapses that broader field into a very short list.
I think this is why ambiguity becomes so costly in local AI. Search can tolerate some messiness because it presents a wider set of options and lets the user do more of the filtering. AI tends to pre-filter on the user’s behalf. If the system cannot quickly connect a business to a place, a category and some external validation, it may simply move on.
For local businesses, this means the familiar local SEO checklist is still useful, but it is no longer sufficient on its own. The businesses most likely to benefit will be the ones that give AI systems a reason to feel confident, not just a reason to index them.
Dual-language content is a must for non-English markets
To see how AI recommendations of local businesses differ between the US, the UK and non-English speaking markets, I also looked at cities in Germany.
Surprisingly, German-language queries often produced much better local results than English-language ones. In some cases, the quality of local relevance dropped sharply when the exact same category was queried in English.
For instance, Gemini occasionally returned American firms for an English-language Berlin accountant query. This means that local intent can break down easily — at least for now — when the language signal does not match the market well enough.
But this doesn’t mean a local business only needs content in the local language. In fact, as other GEO specialists have noted, ChatGPT regularly performs background searches in English even if the query was asked in another language.
When asking AIs about local business recommendations in two German cities, virtually all recommended businesses had websites in both English and German.
The implication: Dual-language content is not just helpful for users. It helps AI systems connect the same business to multiple ways of asking the same local question. In markets where residents, expats, tourists and international professionals may all search differently, that matters.
The bigger point from the study is that local AI visibility is still shaped by a relatively small set of strong signals. Place association, third-party validation, category-specific structure and language clarity all seem to matter more than many businesses realize. That is exactly why local companies have an opening here. The field is still early, and in many categories, the bar isn’t nearly as high as people assume.
Key Takeaways
- Even a little bit of PR can go a long way. This is especially true for smaller geographies: medium-large cities, neighborhoods in metropoles and smaller countries where the competitive field is thinner.
- Brand name and geographic association have a major influence. AI systems appear to connect businesses to queries more easily when the name itself signals a location.
- Not every local industry is recommended in the same way. In some sectors, structured presence appears to matter more. In others, place association matters more.
- Dual-language content is a must for non-English markets. It helps AI systems connect the same business to multiple ways of asking the same local question.
In 2026, AI visibility, or generative engine optimization, has made its way into virtually every board meeting across tech, finance and B2B companies. Interestingly, though, the lowest-hanging fruit in GEO is not in these hypergrowth digital industries, but in good old boring local businesses.
And it’s not because a ton of people are starting to ask their AI of choice who’s the best barber in their area — which is actually happening too. BrightLocal found that consumer use of AI for finding local businesses jumped from 6% to 45% in March 2026 alone.
No, the reason is that showing up in these recommendations is actually more straightforward for local businesses than for virtually any other industry. In a recent study that I completed with my team, I looked at various local business sectors across three countries and six cities, analyzing close to 500 prompts and search results in the process.
