Why AI Recommends Businesses in Other Cities

That 1.2% number deserves a moment to sink in. Out of every 100 local businesses, ChatGPT recommends roughly one. The other 99 are either invisible or replaced by businesses from different cities, different states, sometimes different categories entirely. And with 45% of consumers now using AI to find local services, this is no longer a theoretical problem. It is a revenue problem.

If you have noticed your phone ringing less or your website traffic dipping despite solid Google rankings, this may be the reason. AI is not ignoring your business on purpose. It simply does not understand where you are, or more precisely, it does not have enough location signals to prioritize you over a competitor with stronger overall authority in a different city.

Traditional search engines like Google have two decades of infrastructure built specifically for local results. They know your IP address, your GPS coordinates, your search history, and the physical boundaries of every city and neighborhood. AI search has almost none of that.

AI does not think in geography. When you ask ChatGPT for the best accountant in Phoenix, the model is not querying a map database. It is searching its training data and web results for pages that score highest on relevance, authority, and trustworthiness. If a CPA firm in Chicago has more online authority, more citations, more structured content, and more third-party mentions, the AI may surface it instead of a Phoenix firm with a thinner digital presence.

City economics drive AI recommendations. Research shows that AI recommendations shift based on the economic profile of a city, not just proximity. Businesses in larger markets tend to dominate AI results because they generate more web content, earn more reviews, receive more press coverage, and appear in more directory listings. A business in a metro area with 5 million people will naturally produce a louder signal than one in a market of 50,000, even if the smaller-market business is objectively better at what it does.

Prompt ambiguity amplifies the problem. When a user asks “best plumber near me,” AI does not always know what “near me” means. Unlike Google, which can access device location, most AI chat interfaces have limited or no location awareness. The model guesses based on context clues, and its guesses frequently land on larger, more data-rich cities. If you have experienced this yourself, you know how frustrating it is. Our piece on businesses disappearing from AI search covers the broader visibility crisis this creates.

In traditional local search, proximity is king. Google Maps will always show you the nearest options first, with distance as a primary ranking factor. AI search inverts this hierarchy. Authority comes first, proximity comes second, and sometimes proximity does not factor in at all.

Think of it this way. Google Maps is a compass that points to the nearest businesses. AI search is a reputation engine that points to the most referenced businesses. When those two things align, AI gets the answer right. When they do not, AI confidently recommends a business that may be hundreds of miles away.

This imbalance explains why a roofing company in Houston might appear in AI results for “best roofer in San Antonio” if the Houston company has a stronger content strategy, more directory listings, and deeper review history. The AI does not know or care that Houston is 200 miles away. It only sees which business has the strongest composite signal. Understanding how these composite signals work is central to optimizing your business profile for AI discovery.

If your business is in a smaller market, the location bias problem is significantly worse. Rural ZIP codes produce more AI recommendation errors than urban centers, and the gap is widening as AI platforms scale.

Data density determines AI accuracy. Urban businesses exist in data-rich environments. They have hundreds of reviews, dozens of directory listings, press mentions, social media activity, and competitor comparison content. AI models trained on this data have a clear picture of the business landscape in major metros. Smaller markets have a fraction of this data, which means AI has less to work with and makes more mistakes.

The default-to-metro effect. When AI lacks sufficient local data, it defaults to the nearest large city. A customer in Waco asking for an electrician may receive recommendations for businesses in Dallas or Austin, simply because those markets have richer data that the AI finds easier to parse and trust. The Waco electrician may be excellent, but if they have 30 reviews versus a Dallas competitor with 500, the AI treats the Dallas business as more authoritative.

Not all AI platforms fail at location equally. Each one has different strengths and weaknesses when it comes to understanding where your business is and where your customers are. Knowing the differences helps you understand why results vary so wildly across platforms.

Google Gemini stands apart because of its direct connection to Maps and Business Profile data. For every other platform, location is secondary to authority. This means that if you are only optimizing for Google, you are leaving the other platforms, the ones with high cross-city risk, completely unaddressed. For a deeper comparison, see our analysis of why AI gives outdated information about your business.

The location problem goes beyond just recommending distant businesses. Some AI platforms actively surface businesses that no longer exist. Research has documented cases where LLMs recommend businesses that have permanently closed, particularly outside major metropolitan areas. In some instances, the recommended business is not even in the correct category.

Closed business recommendations erode trust. When a customer follows an AI recommendation to a business that turns out to be closed, they do not blame the AI. They blame the entire category. They question whether any of the recommendations are reliable, which makes them less likely to follow through on legitimate suggestions, including yours. If AI is spreading wrong information about your business specifically, our guide on outdated AI information covers the mechanics of how that happens.

Category mismatches compound the problem. AI platforms sometimes conflate similar-sounding services or pull businesses from adjacent categories. A search for a personal injury lawyer might surface a family law attorney in another city. A search for an HVAC repair company might return a general contractor. These mismatches happen more frequently in smaller markets where AI has less category-specific data to work with.

Fixing the cross-city problem requires understanding which signals AI platforms actually use to determine location relevance. This is where many businesses go wrong. They assume that having an address on their website is enough. It is not even close. AI needs a dense, consistent web of location signals across dozens of sources to accurately place your business.

Structured data is the foundation. Schema markup that explicitly declares your service area, address, and geographic coordinates gives AI parseable location data. Without it, the AI is guessing based on text mentions, which is inherently unreliable. But structured data alone does not solve the problem, it is just one layer in a multi-signal strategy.

Citation consistency across directories matters enormously. When your business name, address, and phone number match perfectly across 50 directories, AI models detect a strong location signal. When there are variations, misspellings, or outdated addresses in even a handful of sources, the signal weakens and the AI becomes less confident about your location. That uncertainty is exactly when it defaults to a competitor in a larger market.

Location-specific content tips the scale. Pages that reference your city, neighborhoods, landmarks, and service areas give AI platforms textual evidence of your geographic relevance. Generic service pages that could apply to any business in any city produce zero location signal. The difference between “We offer plumbing services” and content that contextualizes your services within your specific community is the difference between visibility and invisibility.