Why does AI get your business information wrong, and how do you fix it?
Two different failures produce the same symptom, and the fix depends on which one you have. Either the assistant is reciting a stale memory from training, or it is retrieving your details live from sources you do not control. Ask the same question twice, once plainly and once with an instruction to search and cite, and the answer tells you which. The scale is larger than most teams expect. According to Seer Interactive's test of 178 branded phone-number queries across 7 models, only 27% of the numbers returned matched the business's own Google Business Profile. SOCi's 2026 Local Visibility Index, covering roughly 350,000 locations, found ChatGPT and Perplexity reproducing basic profile facts correctly 68% of the time. You cannot edit an assistant, so the work is correcting what it reads, starting with your own pages and the third-party profiles beside them.
Why does AI get your business information wrong?
AI gets your business information wrong for two separate reasons, and telling them apart is the whole job. The first is memory. A model has absorbed a compressed version of a web that has since moved on, and when it answers from that memory it reports the company you were when the training data was collected: the old price, the departed partner, the positioning you dropped last year.
The second is retrieval. When the assistant searches instead of recalling, it builds your profile from whatever sources come back, which is rarely just your website. Directory listings, aggregator pages, a stale press release, an old job advert and a competitor's comparison table all sit in that candidate set, and several of them will disagree with each other about your hours, your headcount or what you sell.
What turns disagreement into a confident error is the way the model resolves it. A search engine can show you three conflicting results and let you judge. An assistant has to produce one sentence, so it picks, and nothing in the output marks the pick as contested. That is why the errors read so plausibly: the mechanism is built to sound settled whether or not the underlying sources are.
A third contributor sits underneath both. Where no clear source exists at all, models fill gaps rather than abstain, which is where invented staff names, invented offices and invented pricing come from. Thin coverage of your own business is not a neutral state. Our note on why a business is not showing up in AI search covers the absence side of the same problem.
How often does AI get business facts wrong?
AI gets basic business facts wrong often enough that checking is now a standing task rather than a one-off audit. Seer Interactive's study of AI-supplied phone numbers ran 178 branded queries across 7 models. The models almost always produced a number, and only 27% of those numbers matched the business's own Google Business Profile.
Profile accuracy varies sharply by engine. SOCi's 2026 Local Visibility Index, which measured roughly 350,000 locations across 2,751 brands, found Gemini reproducing basic profile information accurately in full, while ChatGPT and Perplexity both scored 68%. The explanation is structural rather than a difference in model quality: Gemini reads Google Business Profile data directly, and the others assemble a profile from third-party sources.
Accuracy problems are not confined to local listings. The EBU and BBC study of news integrity in AI assistants, published October 2025, had journalists review more than 3,000 answers in 14 languages and found 45% carried at least one significant issue, with 20% containing outright inaccuracies such as hallucinated details or outdated information and 31% showing sourcing problems.
| Study | What was tested | Finding |
|---|---|---|
| Seer Interactive, 2026 | 178 branded phone-number queries, 7 models | 27% of numbers matched the business's own profile |
| SOCi Local Visibility Index, 2026 | About 350,000 locations, 2,751 brands | ChatGPT and Perplexity 68% accurate on profile basics; Gemini accurate throughout |
| EBU and BBC, October 2025 | Over 3,000 news answers, 14 languages, 4 assistants | 45% had a significant issue; 20% contained inaccuracies |
| Oumi analysis on SimpleQA, 2026 | Google AI Overviews against a 4,000-question benchmark | Accuracy about 91%, which at Google's volume still means errors at scale |
The last row is the one to hold on to. According to the analysis run with the AI startup Oumi and reported in 2026, AI Overviews scored around 85% accuracy on OpenAI's SimpleQA benchmark under Gemini 2.5 and about 91% after Gemini 3, and at Google's query volume the residual error rate still produces wrong answers in enormous numbers. Google has disputed the methodology. Either way, a 9% error rate applied to questions about your company is not a rounding error.
How do you tell whether the error is in the model's memory or on the web?
Tell the difference by asking the same question twice. Ask it plainly first, with no instruction to search. Then ask it again, adding an instruction to search the web and name the sources it used. The pair of answers separates the two failure modes in under a minute, and nothing else you can do is as informative for the effort.
Wrong the first time and right the second means the error lives in the model's training memory. The correct information exists and is retrievable, and the assistant only reaches it when it searches. Your work is to make retrieval win: strengthen the page that states the fact plainly, make sure it is crawlable and current, and get the same fact repeated on the third-party sources the engine reaches for.
Wrong both times means the wrong fact is published somewhere the engine trusts. Read the sources it names, because they are the diagnosis. Expect an old directory entry, a data aggregator, a syndicated press release from a rebrand you have moved past, or a review site whose profile nobody has claimed. Correcting those is unglamorous work and it is the only work that changes the answer.
Run the test across each engine rather than once, since they read different indexes and will fail differently. Keep the wording identical between runs, log the date, and repeat monthly, because answers vary between runs for reasons that have nothing to do with your fixes. Our note on why AI engines give a different answer every time explains how much variation to treat as noise.
Can you make ChatGPT correct itself?
No, you cannot make ChatGPT correct itself. There is no edit button on an assistant's answer, no equivalent of a Google Business Profile claim that propagates into the model, and correcting the assistant inside a chat changes nothing beyond that conversation. Arguing with it produces a polite agreement and no lasting effect.
The feedback controls are worth the few seconds they cost and should not be mistaken for a remedy. A thumbs-down with a note registers the complaint with the provider, which is useful at scale and across many users, but no provider publishes a correction workflow with a timeline a business can plan around. Treating that as your fix means waiting indefinitely.
What does work is changing the inputs. Assistants answer from the web they can read plus the memory they were trained on, and only one of those is within reach on a normal timescale. Make the correct fact easy to retrieve, state it in one unambiguous sentence on a page the crawlers can reach, and make sure the third-party sources the engine consults agree with it.
The exception worth knowing is the engine that reads a structured profile directly. Gemini's accuracy advantage in the SOCi data comes from Google Business Profile being an authoritative, owner-controlled record, which means keeping that record correct is one of the few places where a business can edit something an assistant will repeat. Bing Places plays the same role on the Microsoft side.
How do you fix wrong information about your business in AI answers?
Fixing wrong information about your business in AI answers is a source-correction job, run in a fixed order. Start by cataloguing the errors: ask each engine ten to fifteen questions a buyer would ask about you, covering what you sell, who you serve, pricing structure, locations, contact details and how you compare to alternatives, and write down every wrong or missing fact with the date and the engine.
Then fix your own pages first, because they are the only source you fully control and the one the engines weight for facts about you. Put each contested fact in plain language on the page where it belongs, in a sentence that could be lifted whole. Vague copy is part of the cause: a model that cannot find a clear statement of what you do will keep reconstructing one from elsewhere.
Next, make the owner-controlled records match. Google Business Profile, Bing Places, LinkedIn, Crunchbase, the review platforms in your category and any industry register should all state the same name, the same address, the same phone number and the same description. Inconsistency between them is what the model is resolving when it invents a compromise, and identical records remove the ambiguity it was resolving.
Finally, work the third-party pages you do not own. Ask for corrections where a directory or aggregator carries an old fact, get an accurate comparison or review profile in place where the category's buying questions are answered, and keep a record of what you requested and when. Our methodology sets out how much weight the published research puts on cross-domain presence, which is the same lever that decides whether a correction sticks.
How long does a correction take to show up in AI answers?
A correction shows up on the retrieval side as soon as the corrected page is recrawled, which can be days rather than months. Semrush's study of how fast AI platforms cite new content, published in December 2025, tracked 81 new pages and found Google AI Mode citing 36% of them within a day of publication. Retrieval moves quickly when the page is clean and the engine already crawls the site.
The memory side is slower and outside your control. A fact baked into a model's weights changes when the provider trains and ships a model on newer data, which is a release schedule rather than a process you can enter. The practical strategy is not to wait for it: make the retrievable version so clear and so consistently repeated that the engine prefers searching over reciting.
Third-party corrections run on the slowest clock of the three, because each one depends on somebody else's editorial queue. Directory updates can take weeks, aggregator data propagates on its own cycle, and a syndicated article may never be corrected at all. Where a source will not change, the counter is volume: enough accurate, retrievable sources that the wrong one stops being the best available answer.
Measure the correction rather than assuming it. Re-run the same question set monthly, keep the wording fixed, and log which engine gets it right on which date, so you can tell a real fix from one good sample. Our guides to tracking AI visibility and measuring citation decay cover the sampling discipline that makes the difference readable.
What else do people ask about wrong AI information?
Why does AI get your business information wrong?
Because the answer comes from one of two places and both can be wrong. When the assistant answers from what the model absorbed in training, it is reciting a compressed memory of a web that has since changed, so old prices, old staff and old positioning survive. When it answers by searching, it assembles your details from whatever sources it retrieves, including directories and aggregators you do not control. Conflicting sources produce confident errors, because the model resolves the conflict rather than reporting it.
How often does AI get business facts wrong?
Often enough to be a commercial problem. Seer Interactive tested 178 branded phone-number queries across 7 models and found that only 27% of the numbers returned matched the business's own Google Business Profile. SOCi's 2026 Local Visibility Index, covering roughly 350,000 locations across 2,751 brands, found Gemini reproduced profile basics accurately in full while ChatGPT and Perplexity both scored 68%, which is about one answer in three carrying an error.
How do you tell whether the error is in the model's memory or on the web?
Tell the difference by asking the same question twice: once plainly, then again with an instruction to search the web and name its sources. Wrong the first time and right the second means the error sits in the model's training memory, and your job is to make sure live retrieval finds the correct version. Wrong both times means the wrong fact is published somewhere the engine trusts, so you have to find and correct that source.
Can you make ChatGPT correct itself?
No. You cannot edit what an assistant says, and correcting it inside a chat changes that conversation only. Feedback buttons register a complaint with the provider and are worth the few seconds they take, but they are not a correction mechanism with a timeline you can rely on. What changes the answer is changing what the engine reads: your own pages first, then the third-party profiles and directories it retrieves alongside them.
How long does a correction take to show up in AI answers?
A correction shows up in retrieved answers as soon as the corrected page is recrawled, which can be within days. Semrush tracked 81 newly published pages in December 2025 and found that Google AI Mode cited 36% of them within a day of publication. A wrong fact held in the model's own memory is slower, because that only changes when the provider ships a model trained on newer data, so the practical route is to make retrieval override the memory.