Capabilities versus reality

AI tools are marketed with words like "intelligent", "understands", and "thinks". Those words are metaphors. Taking them literally leads to the two classic calibration errors, and both cause real workplace damage.

Error one: overtrust

Treating the model as an expert who is occasionally wrong, when it is closer to an eloquent generalist who is unpredictably wrong. Overtrust looks like: sending an AI-drafted contract clause without review, quoting AI-provided statistics in a client report, or letting a chatbot answer customers unsupervised. The failure is rarely dramatic; it is a subtly wrong number or a confidently invented reference that nobody caught.

Error two: dismissal

Concluding after one bad answer that "AI is useless". This error is quieter but also costly: colleagues who refuse the tools outright lose real productivity on the tasks where AI genuinely excels, and organisations where dismissal dominates tend to develop shadow AI use instead of governed use, which is the worst of both worlds.

The practical test before delegating a task to AI: if the output were wrong in a way I fail to notice, what would it cost? Drafting an internal brainstorm: almost nothing. A regulatory filing, medical information, a customer-facing promise: a lot. Match your level of review to that cost, not to how confident the output sounds.