One signal

A right prediction is of little business use if nobody acts on it. Cimba co-founder Subrata Biswas argues that companies should measure what happens after an AI recommendation lands, not stop at whether the model got the answer right.

His practical test: pick a report or alert the team already uses and track for two weeks how often its recommendations are acted on. For supplier evaluations, he suggests asking about acceptance and completion rates, as well as accuracy. It is a modest experiment, not a new procurement project. And, as Biswas notes, it makes little sense for rare decisions or choices with serious human consequences.

Discuss: Would you judge workplace AI by its accuracy, or by what people actually do with its advice?

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