We've shipped AI features across a dozen client products in the last two years. The ones still being used a year later almost never had the strongest model. They had the clearest failure mode.
Users forgive wrong answers. They don't forgive silent ones.
A fraud model that flags something incorrectly, with a visible reason, gets trusted more over time than one that's slightly more accurate but opaque. The explainability isn't a nice-to-have feature. It's the thing that determines whether a human keeps the system turned on.
The demo optimizes for the model looking smart. Production optimizes for the human trusting it enough to leave it on.
If you're evaluating an AI feature before shipping it, ask less about the benchmark and more about what happens on the 5% of cases where it's wrong. That's usually where adoption actually gets decided.
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