A hallucination is when an AI model generates information that sounds correct but isn’t: a made-up fact, a fake citation, a wrong number, an event that never happened. The model isn’t lying. It’s predicting plausible-sounding text, and plausible isn’t the same as true.
Why leaders should care
The danger isn’t that AI makes mistakes. People make mistakes too. The danger is that AI’s mistakes arrive in the same confident, polished tone as its correct answers, which makes them easy to miss, especially in high-stakes documents like contracts, reports, and customer communications.
Example
Someone uses AI to polish an important email. It reads beautifully, but in the final editing pass the AI swapped two critical facts. The sender only notices after it’s gone out, and now looks careless on something that mattered.
My take
Confidence isn’t correctness. You won’t eliminate hallucinations entirely, so design for them: ground the model in your own sources, check important output with a second, different model, and keep a human on anything with real consequences. And treat “it sounded right” as a warning sign, not reassurance.
Leave a Reply