When AI Gets Your Brand Wrong: Finding and Fixing AI Hallucinations

An AI hallucination is when a generative AI tool states something false as if it were fact, and when it happens to your brand, the model can invent policies you don’t have, prices you never set, or reviews that were never written, then deliver them to a buyer in a confident, authoritative voice.

For years the risk in search was being invisible. Now there’s a second risk: being misrepresented. ChatGPT, Google’s AI Overviews, Gemini, and Perplexity increasingly answer questions about your company directly, and when the underlying data is thin, stale, or contradictory, they fill the gap with a plausible-sounding fabrication. This article explains what AI hallucinations are, what they look like when they target a brand, why they happen, how often, what’s at stake, and the concrete steps to find and fix them.

What is an AI hallucination?

An AI hallucination is a confident, fluent output from a generative model that is factually wrong or entirely fabricated. The model isn’t lying in any human sense: it’s predicting the most statistically likely next words, and sometimes the most likely-sounding answer simply isn’t true. The danger is that a hallucination reads exactly like a correct answer: same tone, same certainty, same polish.

For a brand, that abstract problem gets specific fast. A “brand hallucination” is when the false output is about you. Your products, pricing, policies, leadership, locations, or reputation. Because most people now treat an AI answer as a quick, trustworthy summary rather than a draft to verify, an invented fact about your business can shape a buying decision before you ever know it was said.

What does an AI hallucination about a brand look like?

It usually looks like a small, confident, wrong detail, and the real-world cases show how costly “small” can get. Three documented incidents make the pattern concrete.

• An invented policy (Air Canada). Air Canada’s website chatbot told customer Jake Moffatt he could apply for a bereavement discount after booking — a policy the airline did not actually offer. When Air Canada refused the refund, Moffatt took it to British Columbia’s Civil Resolution Tribunal, which ruled in February 2024 that the airline was liable for what its chatbot said and ordered it to pay damages of about CA$650. The tribunal rejected the argument that the bot was a separate entity responsible for its own words.
• Fabricated sources (Deloitte Australia). A roughly AU$290,000 report Deloitte delivered to an Australian government department was found to contain references to academic papers that didn’t exist and a fabricated quote attributed to a federal court judgment — hallmarks of AI hallucinations. After a researcher flagged the fabricated references, Deloitte refunded part of the contract, reported at about AU$97,000, and a revised version disclosed that a generative AI system had been used. Source: Fortune and CFO Dive, 2025.
• A false claim on stage (Google Bard). In a February 2023 promotional demo, Google’s Bard confidently stated that the James Webb Space Telescope took the first image of an exoplanet — which is wrong. After the error was spotted, Alphabet’s shares fell about 7.7%, erasing roughly $100 billion in market value in a single day. Source: NPR and Reuters, 2023.

None of these were edge cases dragged out of the model by trickery. They were ordinary outputs (an answer to a customer, a citation in a report, a line in an ad) that happened to be invented.

The tribunal found Air Canada responsible for all the information on its website, “whether it comes from a static page or a chatbot.” The model’s confidence didn’t transfer the liability — it kept it with the brand.

Why do AI models hallucinate about your business?

They hallucinate because the way these models are trained rewards confident guessing over admitting uncertainty, and because the data they hold about most brands is incomplete. In a September 2025 paper, “Why Language Models Hallucinate,” researchers at OpenAI and Georgia Tech argued that hallucinations are a natural statistical consequence of standard training and evaluation: when a model is graded like a multiple-choice test, a confident guess scores better than “I don’t know,” so models learn to always produce an answer. By that analysis, some rate of fabrication is a structural feature, not a bug that a single update removes.

Two brand-specific factors make it worse. First, thin or inconsistent data: if your products, pricing, and policies aren’t described clearly and consistently across the web, the model has little solid ground to stand on and interpolates the rest. Second, stale data: a model trained or indexed months ago will happily report last year’s price or a discontinued product as current. The less authoritative, consistent, machine-readable information a brand has published about itself, the more room the model has to guess, which is the same mechanism behind how LLMs choose their sources in the first place.

How often do AI tools get facts wrong?

Often enough that it can’t be treated as rare, and the rate depends heavily on the task. In a controlled 2024 study, Stanford’s RegLab and Human-Centered AI Institute found leading language models hallucinated on specific legal queries between 69% and 88% of the time. Rates fall sharply on simpler, well-grounded questions and climb on specialized, detailed ones, which is exactly the kind of question a serious buyer asks about your product.

The practical takeaway isn’t a single magic number: reported hallucination rates vary widely by model, prompt, and domain, and any one figure is a snapshot of a moving target. It’s that the error rate is high enough, on exactly the detailed questions buyers ask, that you should assume some AI tool is already getting something about your brand wrong.

Why brand hallucinations are a business risk now

Because buyers increasingly act on AI answers without checking them, and the liability still lands on you. Deloitte’s 2025 research found that 56% of US consumers planned to use AI chatbots to compare prices and find deals, and 47% to summarize reviews before purchasing. When that many people take an AI summary at face value, an invented price or a fabricated “known issue” reaches a decision-maker at the exact moment they’re choosing between you and a competitor.

The Air Canada ruling shows the legal edge of the same problem: a court treated the chatbot’s fabrication as the company’s own representation. Even setting liability aside, the reputational mechanics are unforgiving: the model states the wrong fact confidently, the customer believes it, and the gap between what was promised and what’s real becomes a support ticket, a refund, or a bad review. Knowing what AI is saying about you is now part of basic brand hygiene, which is why it belongs inside a recurring AI visibility audit.

How do you find AI hallucinations about your brand?

You find them by testing the AI tools the way your customers do, on purpose and on a schedule. Hallucinations about your brand are invisible until someone goes looking, so the detection process is deliberately simple and repeatable:

• Write the prompts buyers actually use. List the real questions a prospect asks — “Does [brand] offer refunds?”, “How much does [product] cost?”, “Is [brand] better than [competitor]?”, “What do reviews say about [brand]?” — in plain language.
• Run them across the major engines. Ask the same questions in ChatGPT, Google’s AI Overviews, Gemini, and Perplexity. The same prompt often produces different answers — and different errors — on each.
• Check every factual claim against the truth. Compare each answer line by line to your real pricing, policies, products, and locations. Flag anything invented, outdated, or attributed to you incorrectly.
• Log it and repeat. Record what each engine said and when, then re-run on a cadence. Models update constantly, so a clean result today doesn’t hold next month. Tracking the trend matters more than any single check.

This is the core of an AI audit: not a one-time screenshot, but a standing measurement of what every major engine says about you and where it’s wrong.

How do you fix and prevent AI hallucinations about your brand?

You can’t reach into the model and edit it, but you can change the evidence it draws on, which is where prevention actually happens. Since hallucinations grow in the gaps left by thin, stale, or contradictory information, the fix is to remove the gaps:

• Publish clear, consistent facts on your own site. State pricing, policies, specs, and key claims plainly and in one authoritative place. Consistency across your pages gives the model solid ground and reduces what it has to guess.
• Make the facts machine-readable. Use accurate structured data and direct, answerable phrasing so engines extract the right answer instead of improvising one. This is the same groundwork that makes content quotable in the first place.
• Fix inconsistencies across the web. Reconcile mismatched details on third-party profiles, directories, and old pages. Conflicting sources are a leading cause of confident wrong answers.
• Correct stale information at the source. Update discontinued products, old prices, and outdated policies wherever they live, so the next time a model re-indexes, it sees the current truth.
• Re-test after every change. Treat detection and remediation as a loop — fix the data, re-run the prompts, confirm the answers moved. Prevention is maintenance, not a one-time project.

The throughline is ownership: you are responsible for what AI says about your brand, even though you don’t control the model. The brands that get this right are the ones that monitor continuously and keep their published facts clean: the same discipline behind every AI audit we run.