Can an AI model say something false about my organization?

Yes. AI engines state false things about companies and people in the same confident tone they use for facts, 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, in the study’s words, alarming confidence. Error rates ran from 37% (Perplexity) to 94% (Grok 3). The 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, stated as confidently as anything true.
• Stale training data, information that no longer matches current facts, because a model answers from a training corpus frozen 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 it forms an answer.

The fix is at the sources, not the model

Asking the model to correct itself does nothing lasting. It does not remember what you tell it, and it builds every answer fresh from the sources it trusts. Influence comes from changing those sources. The work is to find which source is feeding the false claim and then either correct it (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.