Can an AI model say something false about my organization?

Yes. AI engines confidently state false things about companies and people, and the failure modes are predictable. A 2025 study by the Columbia Journalism Review Tow Center tested eight AI search engines and found most presented inaccurate answers with alarming confidence, with error rates ranging from 37% (Perplexity) to 94% (Grok 3). The durable fix is not to argue with the model but to correct the sources it draws on, or to strengthen accurate ones until they carry more weight.

The four predictable failure modes

• Hallucination, a fabricated executive, a lawsuit that does not exist, or a product feature that was never shipped, delivered in the same confident tone as a true statement.
• Stale training data, information that no longer reflects current facts, because a model answers from a training corpus fixed at its knowledge cutoff.
• Entity confusion, your CEO conflated with someone of the same name when the entity infrastructure (Wikidata, schema, clean disambiguation) is weak.
• Single-source over-weighting, the engine leaning too heavily on one contested source when forming an answer.

The fix is at the sources, not the model

Asking the model to correct itself has no lasting effect: it does not remember what you tell it, and it builds every answer fresh from the sources it trusts. So influence comes from changing those sources. The work is to identify which source is feeding the false claim and then either correct that source (a Wikipedia edit request, a structured-data fix, a press correction) or strengthen competing accurate sources until the engines re-weight. AIQ makes finding the source fast; the hands-on work on that source is where the time goes.