To fix wrong AI answers about a company, audit what each AI platform says, trace the pages those answers rely on, correct them with consistent facts, add independent coverage and re-check every month. AI models guess when a fact is rare, and OpenAI’s 2025 research shows that facts appearing only once in training data set a floor on how often a model gets them wrong.
Last updated November 1, 2026.
On this page:
- Why AI gets facts wrong
- Fix 1: Audit the answers
- Fix 2: Find the sources
- Fix 3: Correct the pages
- Fix 4: Add independent coverage
- Fix 5: Track and escalate
- Frequently asked questions
Why do AI platforms get facts about a company wrong?
AI platforms get facts about a company wrong because language models produce plausible answers even when the underlying fact is thin, outdated or contradicted by other pages. A September 2025 paper from OpenAI and Georgia Tech, Why Language Models Hallucinate, argues that models guess when uncertain because training and evaluation reward guessing over admitting uncertainty. The authors also show that the rate of errors on arbitrary facts is at least the share of facts that appear exactly once in training data, and they note that models seldom err on frequently referenced facts.
Applied to a company, the same logic points in a clear direction: a fact stated on one page is more fragile than a fact stated consistently across many independent pages. The McKinsey analysis of AI search adds a second cause. McKinsey’s October 2025 report found that a brand’s own sites make up only 5 to 10 percent of the sources AI search references, so wrong answers often trace back to pages the company does not control.
| Error type | What it looks like | Typical cause | Where the fix starts |
|---|---|---|---|
| Outdated fact | An old address, leader, price or product line | Older pages still outrank current ones | Update the owned page and the listings that repeat it |
| Wrong category | The company is described as a different kind of business | Conflicting labels across pages | Use one category phrase everywhere |
| Mixed-up identity | Facts from a similarly named company appear | Thin, ambiguous references | Add distinguishing details and consistent naming |
| Invented detail | A founder, award or statistic that does not exist | A rare fact with little support | Publish the true fact on several credible pages |
| Missing brand | The company is absent from category answers | Little independent coverage | Build coverage on the sources AI search cites |
The analysis of AI misclassification covers the wrong-category case in depth, including a Harvard Business Review example in which an AI model classified Ballantine’s Scotch as a prestige product.
Fix 1: How do you audit what AI says about your company?
An audit starts with a fixed list of questions that buyers would ask, run on each AI platform and recorded word for word. Include questions about what the company does, who leads it, what it sells, how it compares with named competitors and which companies lead the category. Keep the list the same every month so the answers can be compared.
Record four things for each answer: whether the company appears, whether each stated fact is correct, which sources the platform shows and how the company is categorized. Mark every wrong statement with the page or listing that likely supports it. McKinsey reported that only 16 percent of the brands it surveyed systematically track AI search performance, so a written baseline already puts a company ahead of most of its peers.
Fix 2: How do you find the sources behind a wrong answer?
Finding the source means identifying which pages repeat the wrong fact, because correcting the answer requires correcting the page behind it. Start with the sources the AI platform displays, then search the exact wrong phrase and the old fact in a standard search engine. Check these places in order:
- The company’s own website, including old press releases, archived blog posts and leadership pages.
- Business directories, review sites and app or marketplace listings.
- News articles and trade coverage that carry an outdated description.
- Wikipedia or similar reference pages, where editors with a conflict of interest should propose changes on the talk page instead of editing directly.
- Community discussions and affiliate pages, which McKinsey found make up a large share of AI search sources in some categories.
McKinsey’s analysis found that in consumer packaged goods and financial services, more than 65 percent of AI search sources are publishers, user-generated content and affiliate sites. A company that checks only its own domain will miss most of the pages that shape the answer.
Fix 3: How do you correct the pages AI relies on?
Correcting the pages means making every page the company controls state the same facts in the same words, then asking third parties to match. Begin with a single authoritative facts page that states the legal name, category phrase, leadership, location, products and founding date in plain sentences. Then update the pages that repeat those facts: the about page, leadership biographies, product pages, press boilerplate and social profiles.
For pages the company does not control, send a short, specific correction request. Name the page, quote the wrong statement, give the correct fact and link to the authoritative page that proves it. Publishers act faster on a documented request than on a general complaint. The AI reputation glossary entry defines the terms used in this process.
Use one category phrase everywhere. If the website calls the company a software platform, a directory calls it a consultancy and a press kit calls it an agency, an AI model has three labels to choose from and may pick the wrong one.
Fix 4: How do you add independent coverage that confirms the facts?
Independent coverage confirms the facts by repeating them on credible pages the company does not own, which gives AI platforms more than one source to draw on. Because a brand’s own sites are a small share of what AI search references, coverage on publishers, industry directories and community pages carries real weight. A company with a thin footprint is most exposed to the singleton problem described in the OpenAI paper.
Useful sources include trade publications that cover the category, expert commentary, customer reviews, industry association listings and original research that other pages cite. Each should state the same core facts the facts page states. The generative engine optimization practice covers how earned coverage and owned pages work together, and the State of AI Search 2026 research covers the wider shift in how AI search finds sources.
Fix 5: How do you track corrections and escalate serious errors?
Tracking means re-running the same question list every month and comparing the answers with the baseline. Corrections reach AI answers on each platform’s own schedule, so measure progress by what changes in the answers, not by when a page was edited. Log each wrong statement, the page fixed, the date of the fix and whether the answer changed.
Escalate when an error is serious: a false claim that could harm customers, a misstatement of regulated information such as financial or health claims, or a statement that could be defamatory. Involve legal counsel for those cases, use each platform’s in-product feedback option and document every report. For errors that spread fast or attract press attention, treat the issue as a reputation event and bring in the crisis communications practice alongside the correction work.
- Review the audit results monthly and compare them with the original baseline.
- Update the facts page whenever leadership, products or locations change.
- Keep a log of correction requests, including the date sent and the response.
- Re-check the sources behind any answer that stays wrong after two review cycles.
Frequently asked questions
Why does AI say something wrong about my company?
AI models answer from patterns in training data and the sources they retrieve. When a fact is rare, outdated or contradicted by other pages, the model may produce a plausible but wrong answer instead of admitting uncertainty.
Can I ask an AI platform to change an answer?
Most platforms offer an in-product way to report an incorrect answer, but a report does not guarantee a change. The more reliable approach is to correct the pages the answer draws on and add independent pages that confirm the right facts.
How long does it take for a correction to show up?
The timing depends on each platform and on how quickly the underlying pages are updated and recrawled. Re-run the same questions every month and judge progress by whether the answers change.
Does my own website matter if AI uses other sources?
Yes. Your website is the source of record that third parties and platforms can check, but it is only one of many sources. McKinsey found brand-owned sites make up 5 to 10 percent of the sources AI search references, so coverage elsewhere matters as well.
When should I involve legal counsel?
Involve legal counsel when an answer could be defamatory, misstates regulated information or could cause real harm to customers. Document the answer, the platform and the date before any outreach.
