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In artificial intelligence projects, there is a natural tension between technical rigor and business velocity. Machine learning teams often strive for statistically perfect models built on pristine datasets, a process that can take months or even years. But what if a “good enough” model, deployed quickly, could deliver more business value than a perfect one that arrives too late?