7 Common Mistakes in AI Visual Inspection Implementation — and Why Most of Them Aren't About the Model
An AI inspection system is a measurement system. Teams that validate the model but never validate the measurement end up automating their old blind spots. It is week six after go-live. The pilot report showed 98% accuracy on a held-out image set. Now the second-shift lead is on the phone: the station is kicking out good castings every few minutes, operators have started waving parts through on override, and nobody can say whether last night's escapes were the model's misses or the overrides. (This is a composite scenario, not a specific deployment, but most quality heads will recognise it.) The coast lands in three places at once. False rejects become scrap and re-inspection labour. Overrides become customer escapes and PPM complaints. And the plant manager, who signed off the capital request, now has to explain why a system bought to remove inspection variability has added a new kind. The quality head owns the outcome, but the root cause usually sits upstream of the model. Aft...