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As product managers working on AI/ML projects, we often find ourselves caught between model metrics like accuracy or F1 scores and actual business impact. While data scientists focus on improving model performance, our job is to ensure these improvements translate into tangible business results.

The key difference is simple: model metrics tell you how well your model performs technically, while business metrics show the actual value it creates. For example, a model with 99% accuracy might still fail to deliver business value if it’s solving the wrong problem.