AI Search Optimization for Multi-Location Businesses — What We Learned from 11,705 AI Responses

We're all still figuring out how these AI search engines work — and how they'll reshape local search strategies for multi-location brands.

New data keeps arriving, and most of it confirms the same thing: The brands that show up in AI-generated answers aren't the ones with the best content. They're the ones with the cleanest, most consistent location data across the most platforms.

ChatGPT now has 900 million weekly active users, up from 400 million in 2025. And experts estimate $750 billion will flow through AI powered search by 2028. If that's where your customers now research local businesses, you've got to ask: What do you actually do about it — and how do you know which AI search optimization strategy is working?

We ran a study to help our clients figure it out. Over 14 days, we collected 11,705 AI-generated responses across 12 models — from ChatGPT and Claude to Gemini, Grok, and Perplexity — for local business prompts in San Francisco.

What we found should be the core of any AI search optimization strategy.

How Does AI Search Work?

If you're new to search (or just refreshing your knowledge), you're welcome here. We're going to break down the most important information before we move on to the AI search optimization steps multi-location brands need to take over the next few months.

Search Is Becoming an Answer, Not a Click

"AI search" refers to AI-generated answers that synthesize information from multiple sources to answer user questions directly.

This means that consumers get what they need directly in AI search results or summaries. While traditional search engines return lists of blue links, AI search delivers a single narrative response that can compare options, recommend local businesses, and shape consumer perception without requiring a click.

AI is also way more selective than Google about which businesses to recommend. In Google's Local Pack, about 35.9% of eligible businesses get surfaced. In Gemini, Perplexity, and ChatGPT, there are far fewer slots to compete for, which means the businesses that earn them tend to be the ones with the most complete, consistent, and well-structured location data. That's across dedicated location pages with address, hours, services; reviews; and local listings.

This shift is rattling SEOs. Their content is, after all, seeing fewer impressions and clicks — especially any informational top-of-funnel content. AI answer engines — like Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, Gemini, and Claude — summarize, compare, and recommend content without always assigning clear credit to the brand that created it.

AI visibility and its ROI is now about these brands being present, represented, and convincing customers within AI-generated responses. And doing all of that better than local competitors.

When these brands don't appear in AI-generated responses as an option or a recommendation — often because of inconsistent data about their brands or locations online,

1. they’ll rely solely on other traffic sources, missing the higher-intent audiences that AI search delivers
2. local competitors who do appear in AI responses will gain an advantage

Multi-location brands don’t want to remove themselves from consideration before the funnel begins. That's why more brands than ever are putting budget toward Answer Engine Optimization (AEO) — also known as Generative Engine Optimization (GEO).

The AI Search Fundamentals We Know About Now

AI search engines are evolving day by day. While we can't update this article daily, we can walk you through the foundational shifts in AI search — fundamentals that are unlikely to change.

AI Models Build Answers from Different Sources and They Don’t Always Agree

What our GEO/Analyst Katya Shishchenko found in various Uberall studies is that AI search recommendations are largely model-dependent. Across her San Francisco coffee study of 39 coffee shop brands tracked across 12 AI models and three distinct intent clusters, 51% were mentioned by all 12 models, and 92% appeared in at least 4. It's worth noting that coffee in San Francisco is a well-established industry with clear market leaders — in less concentrated categories like dining, beauty salons, or dental practices, the overlap between models would be significantly lower.

Here's a summary of what she found across 11,705 responses:

• Models like Claude Opus and ChatGPT o3 mentioned an average of 10 brands per response, giving visibility evenly to both established and lesser-known businesses. These models also gave the longest and most detailed responses. Smaller, faster models like Claude Haiku mentioned fewer than 5, concentrating heavily on well-known chains.
• Most AI models — ChatGPT, Claude, Gemini, Grok — generate answers from what they learned during training (parametric models). They recommend businesses based on how much was written about them before a fixed knowledge cutoff, which means they favor brands with a long, well-documented online history. Perplexity works differently: It searches the live web every time someone asks a question, pulling from what's being reviewed and discussed right now (search-grounded models). That's why a newer business with strong recent reviews can show up prominently in Perplexity but barely in ChatGPT.
• Day of the week doesn't matter at all: Weekday and weekend recommendations were nearly identical across all 12 models over 14 days. AI models pull from training data or current web results — neither source changes based on the day of the week. If you're an established brand, tracking prompts daily won't surface more brands or meaningful shifts than the weekly or monthly cadence GEO Studio is built around.

Aside from Katya's study, we know that AI models can also look at what people say about a business. Positive, detailed reviews give AI the context it needs to understand and recommend specific locations.

This source diversity is why location data management matters so much for both search-grounded and parametric models — any inconsistency in your business data reduces an AI model's citation confidence.

Advice: Get Your Location Data and Review Management Right First

• Standardize core location data: Keep name, hours, addresses, services, and attributes consistent across all platforms.
• Validate geospatial accuracy: Correct map pins and coordinates are essential — AI weighs proximity and navigation heavily for high-intent queries, and incorrect coordinates in schema markup are a known cause of suppressed visibility.
• Create dedicated, indexable location pages: These connect brand entities to local context, services, and neighborhoods, and they're what AI models prefer to pull from over generic pages. Include image alt text and descriptive filenames so AI can validate visual signals too.
• Set up LocalBusiness schema at scale: Schema markup helps AI models read your location data — hours, services, pricing, coordinates. Our Locator & Local Pages tool autogenerates it based on business type, so there's no manual work per location.
• Encourage positive, detailed reviews: The more high-quality reviews your multi-location brand has on platforms like Google, Apple, and Yelp, the more context AI has to understand and recommend your business — and search-grounded models like Perplexity weight recent review activity more heavily than historical presence.

The Same Brand Gets Completely Different AI Visibility Depending on the Prompt

Katya's coffee study revealed that the same business can even have wildly different AI visibility depending on how customers frame their questions.

Three distinct patterns showed up across our 11,705 responses:

• Discovery prompts ("best coffee shops," "top-rated cafes"): AI models recommend the brands with the strongest overall reputation. The top names in our study had mention rates of 79 to 86%. If a brand is well-established and well-reviewed, this is where it'll show up.
• Functional prompts ("best to work from," "business meeting spot"): AI models match businesses to a specific use case, not overall reputation. One brand jumped into the top 5 exclusively in this category because it was known for its local laptop-friendliness.
• Neighborhood-specific prompts ("coffee near Union Square," "best in the Mission District"): AI prioritizes physical density and location association over reputation. The results looked very different here in comparison with the two prompt types we've described above. Starbucks, for example, went from a 0.2% mention rate in discovery prompts to 32.6% in neighborhood prompts. A specialty coffee brand that dominated the discovery prompts dropped to 1.3% in neighborhood prompts — likely AI models need more than a physical address to connect that business with the neighborhood. Reviews, listings, and content that explicitly mention that neighborhood are important.

These differences aren't about how competitive your local market is; they're built into how each AI model works.

You can find all the types of prompts your customers are asking in GEO Studio's Prompt Center, and from there, build content clusters that answer those questions directly.

Advice: Speak Like Your Customers and Build Authority Across Platforms

• Structure and update location content for AI interpretation: Create and update intent-aligned FAQs, service descriptions, and other content to cover different intents. And keep this content fresh.
• Create in-depth content that directly answers prompts: AI tools scan beyond relevant keywords on your website and look at phrasing, context, and intent to deliver conversational, relevant results. Address specific prompts and doubts comprehensively, so AI models have quality material from your brand to include in its generated answers. The core principles of SEO — E-E-A-T (experience, expertise, authoritativeness, trustworthiness) — still matter in AI search — creating content of value for the questions asked.
• Maintain presence on authoritative directories: Help AI models make that local connection by getting listed on trusted third-party platforms — local guides, "best of" lists, and features.
• Align social content with location entities: Connect social profiles and posts to specific locations so engagement reinforces AI models' understanding of where and why each location is relevant.

Bring Your Multi-Location Brand to the AI Search Party

At the top of this article, I said the question worth asking is concrete: What do you do about AI search, and how do you know which AI search optimization strategy is working across different models?

Brands need a sharper version of the search strategy they already have. Clean location data, complete schema markup, fresh reviews, and content that answers the varied prompt intents their customers are asking AI models.

It's our job at Uberall to help multi-location businesses adapt confidently to this new customer journey — and continue to optimize their locations' performance with GEO Studio. More than that, it's our job to reframe this disruption not as "another thing to optimize toward," but as an opportunity to connect with customers via the AI tools that are proven to drive revenue — $750 billion's worth.