Usually because something on the web says it wrong first. ChatGPT and tools like it build their descriptions of your business from published sources — websites, listings, directories, reviews, and older pages — and when those sources are outdated or contradict each other, the AI repeats the error, often very confidently. The AI is rarely inventing the mistake from nothing; it is copying one, and the fix starts with finding the source it copied from.
Where the wrong facts come from
AI systems do not have a private, verified file on your business. Depending on the tool and the question, an answer may be drawn from information the model learned during training, from live web sources it retrieves while answering, or a mix of both. The details of each platform’s process are not fully documented publicly, so the exact path of any single wrong answer usually cannot be proven. But wrong descriptions consistently trace back to a handful of causes:
- Outdated pages still online. Your old website pages, an old location’s listing, last year’s hours on a directory — if it is still published, it is still a source. Closing a location or changing your hours in real life does not update pages nobody remembered to change.
- Listings that copied each other. Business directories frequently copy data from one another. One wrong phone number or one stale address can be duplicated across dozens of listings, and to an AI reading them, the repetition looks like confirmation: the wrong fact appears to be the well-supported one.
- Your own materials disagreeing. Your website says you offer a service you dropped in 2023; your Google profile says otherwise; a PDF price list from years ago still ranks in search. The AI merges conflicting sources into one description, and the result can be a blend that is wrong in a way no single source quite says.
- Someone else’s description of you. Review sites, local blogs, and aggregator pages describe your business in their own words. Errors there — a misnamed owner, a wrong specialty, a service attributed to you that belongs to a similar business nearby — get repeated like any other source.
- Old information in the model’s memory. Where an answer comes from training data rather than a live lookup, it can reflect how your business looked when that data was collected. A business that moved, renamed, or changed hands can be described as it was, not as it is.
The confidence is the confusing part. AI writes a stale fact in the same tone as a correct one, because it is summarizing sources, not checking them against your reality. It does not know your business; it knows the documents about your business.
A concrete example
Consider a dental practice that moved across town three years ago. They updated their website and their Google listing. But their old address still lives on: a health directory nobody claimed, a “top dentists” listicle from before the move, and — quietly — the location page of their old site, which their web host never took down. Patients start reporting that ChatGPT sends people to the old office.
Nothing mysterious happened. Three published sources still say the old address, they agree with each other, and one of them is the practice’s own forgotten page. The AI is doing exactly what it does: repeating what the web says most consistently. The fix was not to argue with the AI — it was to correct or remove each source, starting with the one the practice itself controlled, and then wait for the corrected information to be picked up.
What this means for your business
Wrong AI descriptions are a source problem, and source problems have a practical sequence:
- Write down exactly what is wrong. The specific claim — wrong address, discontinued service, closed location listed as open — and which tools say it. Vague frustration (“AI gets us wrong”) cannot be traced; a specific wrong sentence can.
- Hunt for the source. Search the exact wrong fact in quotes. Errors almost always trace back to a document somewhere: a listing, an old page, an aggregator. Finding it is the diagnosis, and it is most of the work.
- Fix what you control first, then request corrections elsewhere. Your own site and profiles immediately; directories and third-party sites through their correction processes. Expect a lag: sources are re-read on their own schedules, not yours.
- Re-check over time. AI answers vary from day to day, so a single corrected answer is not proof the problem is gone. Patterns across repeated checks are the real signal.
This tracing work is what our AI Visibility Audit does: it records what the major AI tools say about your business, checks the claims against reality, and identifies the sources most likely feeding each error. Where corrections need to be built into durable, well-structured source material, that is AI Citation Development, and AI Visibility Monitoring exists because answers drift — errors creep back when a stale listing resurfaces. Our pillar guides go deeper on how AI citations work and how AI visibility is measured.
Two honest limits: you cannot edit an AI’s answer directly, and no one can guarantee a correction will appear by a certain date, or at all. What you can do — and what reliably changes answers over time — is correct the record the AI is reading from.
Related questions
- What Does AI Say About My Business?
- Why Does AI Recommend My Competitor and Not Me?
- How Do I Get My Business on AI?
- How Can My Business Make Money With AI?
Published: November 1, 2026
Last reviewed: November 1, 2026
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