AI Hallucinations: The Risk for Multi-Location Brands

AI search is quickly becoming a default research step before a customer visits a store, books a service, or calls a support line. For multi-location and enterprise brands, that often means AI-generated answers are shaping decisions across hundreds or thousands of locations, and those answers aren’t always accurate.

When AI is asked about a brand, it pulls from training data and whatever it can retrieve in real time. When that information is thin, outdated, or conflicting, the model can fill the gaps with something that sounds plausible but isn’t true. That’s AI hallucinations, and at MULO scale, the risks are much bigger than a wrong address.

What Are AI Hallucinations?

AI hallucinations occur when generative AI states false information as confidently as accurate information.

When sources are sparse or contradictory, AI tends to guess rather than say it doesn’t know, because a confident answer reads as more useful than an uncertain one.

The model has no built-in way to verify anything against current reality, only patterns from training data and whatever it retrieves at the moment of the query.

Given a choice between admitting uncertainty and producing a wrong answer that sounds right, AI frequently chooses the latter.

For a single-location business, that guess might mean a wrong phone number. For a national or global brand, the same guessing behavior can get applied to policies, procedures, and other commitments, and misinformation gets relayed to customers as if official.

Why AI Hallucinations Look Different at Enterprise Scale

Large brands typically have the opposite problem from small businesses. A national retailer or a franchise network usually has plenty of press coverage, directory listings, and structured data for AI to draw from, so basic facts like a headquarters address are less likely to get mangled, but scale introduces a different kind of risk.

Enterprises have huge numbers of policies and offers, from return windows to loyalty terms to regional promotions, and those often vary by location, change frequently, or live in inconsistent places across a corporate site, franchise pages, and support documentation.

That inconsistency is exactly the kind of gap AI fills with a guess, and because these brands are the ones customers are most likely to ask AI about, an invented policy or offer is far more likely to reach a wide audience and get treated as fact.

The Consequences Go Beyond a Lost Customer

For a small business, a hallucinated address might send a shopper to a competitor down the street. For a multi-location or enterprise brand, a hallucinated policy could create a legal and financial mess.

A famous example of this isn’t from the local search world, but it should be a warning for anyone running a large consumer-facing brand. In 2024, Air Canada’s AI-powered website chatbot told a grieving customer he could apply for a discounted bereavement fare after booking his ticket. That process didn’t exist. Air Canada’s actual policy required the request before purchase.

When the airline refused to honor what its own AI had promised, the customer took the case to court and won. The court rejected Air Canada’s argument that it wasn’t responsible for its chatbot’s output, ruling that a chatbot is simply part of the company’s website and the company is accountable for everything on it.

Air Canada isn’t a multi-location business in the traditional sense, but picture the same failure at a retail chain with thousands of locations: AI confidently describing a return policy, a warranty term, or a price-match guarantee that the company never actually offers. Instead of one customer walking away, a fabricated policy like that can spread across support tickets, social posts, and news coverage before anyone at the company notices, and it can create huge headaches for the business.

Reducing the Risk for Multi-Location Brands

The fix starts with a simple principle: limit how much guessing AI has to do. For a MULO enterprise, that means keeping policy language, hours, pricing, and location-specific details consistent across the corporate site, individual location pages, franchise partner sites, and support documentation. A policy stated one way on the homepage and another way on a regional page is an open invitation for AI to merge the two into something wrong.

It also means treating your own customer-facing AI chatbots and support tools as a hallucination risk in themselves, not just a channel for delivering answers to FAQs. Test them regularly against your actual policies and offers, especially for anything with financial or legal weight.

From there, monitor what AI is actually telling people about your brand and its locations. A tool like Local Falcon can help track how AI platforms answer customer questions across a multi-location footprint, so discrepancies get caught before they create problems.

Beyond location data, enterprises should also run regular prompt audits on core brand and policy questions across leading generative AI platforms, track AI citations, and correct the source AI appears to be pulling from whenever something is wrong.

The Bottom Line

Large brands are less likely to have their address or phone number hallucinated by AI, but the stakes when something does go wrong are considerably higher.

A fabricated policy or procedure attached to a well-known name can spread fast and can carry significant consequences beyond a lost sale.

That’s why MULOs need to know exactly what AI is saying about their brand and take steps to ensure accuracy.