A growing number of people now ask AI assistants for recommendations the way they once typed searches into Google. Someone planning a weekend away might ask ChatGPT for a quiet inn near hiking trails, ask Google's AI Mode for the best breakfast spot in town, or ask Perplexity which outfitter rents paddleboards by the hour. Instead of a list of ten links, they get a short answer naming a few specific businesses, often with a sentence or two about why each one was chosen.
For local businesses, that shift raises a practical question. When an AI assistant recommends three places, how does it decide which three? The answer is less mysterious than it might seem. AI assistants draw on sources a business can influence, and many of the fundamentals of good local search visibility carry over directly. This post explains where AI assistants get their information, what makes a business more likely to be recommended, and how to measure whether it is happening. The practices involved are often called GEO (generative engine optimization) or AEO (answer engine optimization), terms that describe making a business easier for AI tools to find, understand, and cite.
Where AI Assistants Get Their Information
AI assistants answer questions using two broad sources. The first is the model's training data, the large body of text it learned from before it was released. Training data gives an assistant general knowledge, but it has a cutoff date and often contains little about individual small businesses.
The second source is live retrieval. When someone asks a question about current information, like which restaurants are open tonight or which inn has good reviews, most assistants search the web in real time and summarize what they find. The sources vary by assistant:
Google's AI Overviews and AI Mode draw on Google's search index and Google Business Profile data
ChatGPT's search feature draws on third-party search providers, including Bing, along with its own web crawling and map and business data partners
Microsoft Copilot draws on Bing's search index and Bing Places listings
Perplexity runs its own web crawler and search index
Siri and Apple Intelligence draw heavily on Apple Maps data, which businesses manage through Apple Business Connect
For local recommendations, live retrieval is what matters most. That means the same business listings, websites, reviews, and articles that shape traditional search results also shape AI answers, with some differences in emphasis.
Business Listings Carry More Weight Than Ever
When an AI assistant recommends a local business, it often relies heavily on structured listing data, the name, category, location, hours, rating, and review content found in business listings. That data is reliable, consistently formatted, and easy for an AI system to compare across businesses.
This makes listing management on every major platform more important than it was when most discovery happened through Google alone. A business that has a well-maintained Google Business Profile but an outdated Bing Places listing may appear in Google's AI results and be missing or misrepresented in Copilot or ChatGPT. The same applies to Apple Business Connect for Siri and Apple Maps. Accurate categories, hours, descriptions, and attributes across all of them give every assistant the same correct picture.
Reviews Shape How Businesses Are Described
AI assistants do more than count stars. They read review text and summarize what customers say. When an assistant describes a restaurant as "known for its wood-fired pizza and friendly staff" or an inn as "popular with couples for its quiet setting," those descriptions often come from patterns in reviews.
That has two effects. A business with a steady volume of recent, detailed reviews gives assistants more material to draw from, which makes it easier to recommend confidently for specific requests. Review content also influences which requests a business is recommended for. If customers regularly mention that an inn is dog friendly, the inn is more likely to appear when someone asks for pet-friendly lodging.
Responding to reviews contributes as well, since responses add business-provided context to the conversation, like clarifying what a restaurant serves or correcting a misunderstanding.
Mentions Across the Web Build Confidence
AI assistants tend to favor businesses that are mentioned consistently across multiple independent sources. A shop that appears in a local newspaper feature, a regional travel guide, a "best of" list from a tourism organization, and a few travel blogs gives an assistant several sources that agree the business is worth recommending.
These mentions work much like backlinks in traditional search, with one difference. For AI assistants, the mention itself can matter even when it does not include a link, because the assistant is reading and summarizing the content. Being described accurately and favorably in trusted sources is what builds that confidence.
Consistency across these sources matters too. When a business is described with the same name, location, and core offerings everywhere, AI systems can confidently connect all of those mentions to one business. Outdated names, old addresses, or conflicting descriptions make that harder.
Websites Need Clear, Specific Content
A business website contributes to AI recommendations when it states facts about the business clearly and in plain language. AI systems are better at extracting information from a page that says "We serve breakfast daily from 7 to 11 and offer gluten-free options" than from a page built mostly around images, vague slogans, or text embedded in graphics.
A few practices make websites easier for AI systems to use:
Answer common questions directly on the site, such as hours, parking, pricing, pet policies, reservations, and accessibility
Give each service or location its own page, with specific descriptions of what it offers
Keep key information in text, since details that only appear in images or PDFs are harder for AI crawlers to read
Add structured data, code added to a page that labels information like business type, address, hours, and menu in a standardized format that search engines and AI systems can read reliably
None of this requires writing for machines at the expense of readers. Content that answers customers' questions clearly tends to serve both.
Check That AI Crawlers Can Reach the Site
Websites use a file called robots.txt to tell automated crawlers which parts of the site they may access. Some AI companies operate separate crawlers for different purposes. OpenAI, for example, uses one crawler to collect training data and a different one, OAI-SearchBot, to find pages for ChatGPT's search results. Blocking the training crawler does not prevent a business from appearing in ChatGPT search, but blocking the search crawler can.
Many website platforms and security services now include settings that block AI crawlers by default or with a single toggle. It is worth checking these settings to confirm which crawlers are allowed. A business may have decided to block AI training, which is a reasonable choice, without realizing the same setting also removed it from AI search results.
Measure AI Referral Traffic
When someone clicks a link in an AI assistant's answer, the visit often shows up in GA4 as referral traffic from the assistant's domain, such as chatgpt.com, perplexity.ai, gemini.google.com, or copilot.microsoft.com. ChatGPT also adds a UTM tag identifying itself as the source on many of the links it provides.
Creating a custom channel group or a saved report in GA4 that groups these sources together makes AI traffic visible as its own channel. Watching it over time shows whether AI referrals are growing and which pages they land on. Visits from Google's AI Overviews and AI Mode are harder to separate, since they are generally reported as regular Google organic traffic.
Referral traffic only captures part of the effect. Many people read an AI recommendation and then search for the business by name or go straight to a map app. Rising brand search volume in Google Search Console, and more direct visits and calls, can reflect AI recommendations that never produced a click.
Test What the Assistants Say
The most direct way to understand how AI assistants see a business is to ask them. Running a set of realistic questions through ChatGPT, Gemini, Copilot, and Perplexity, the kinds of questions customers would ask, shows which businesses are being recommended and how each one is described.
Useful questions to test include broad category searches, like "best restaurants in [town]," specific requests, like "pet-friendly inns near [area]" or "where to rent kayaks in [town]," and direct questions about the business itself, like its hours or what it is known for. Repeating the same questions every few months shows whether visibility is improving and whether descriptions are accurate. When an assistant gets a detail wrong, the source of the error can usually be traced to an outdated listing, an old article, or a gap on the website.
Key Takeaways
AI assistants recommend local businesses mostly through live retrieval from search indexes, business listings, reviews, and web content
Listings on Google, Bing, and Apple each feed different assistants, so all of them need to be accurate and complete
Review volume, recency, and content shape both whether a business is recommended and how it is described
Consistent mentions in trusted independent sources build confidence, even without links
Clear, specific website content and structured data make business details easier for AI systems to extract
Check robots.txt and platform settings to confirm AI search crawlers are allowed
Track AI referrals in GA4 and test assistants directly with realistic customer questions