Expert vs Novice Perceptions

How you perceive AI depends heavily on how much you know about it, but not always in the direction you might expect. Novice users tend to oscillate between two extremes - either treating AI as almost magical, capable of anything, or dismissing it as useless after a single disappointing interaction. Experts, by contrast, tend to have more calibrated expectations: they know what current systems can and cannot do, and they are better at designing tasks that play to AI's strengths. But expertise brings its own biases. Technical experts sometimes underestimate the difficulty of real-world deployment, assuming that a model that performs well in testing will perform equally well in messy production environments. Domain experts - doctors, lawyers, engineers - may either over-trust AI outputs in their own field (assuming the machine must know something they don't) or under-trust them (refusing to accept that a machine could contribute to their specialised work). For organisations, this gap in perception creates practical challenges. Training programmes and interface design need to account for different levels of understanding. The goal is not to make everyone a technical expert but to help people at every level develop realistic expectations - understanding both the genuine value AI can provide and the specific ways it can fail.