<ul><li>Metrics like accuracy or precision offer only a partial view of an AI feature’s success.</li><li>Real-world relevance and user experience are crucial for the success of AI models.</li><li>Business outcomes are not guaranteed even with technically flawless AI features.</li><li>User behavior and business results must be championed to capture AI’s true impact.</li><li>Metrics such as AI Feature Adoption, Frequency and Depth of Use, and User Retention matter.</li><li>Efficiency gains and productivity improvements should be tracked for AI applications.</li><li>AI-Related User Satisfaction and Experience metrics like NPS and CSAT are vital.</li><li>Measuring direct and indirect business impacts of AI features is essential.</li><li>A structured, hypothesis-driven approach is necessary to link technical AI model improvements to tangible outcomes.</li><li>A/B testing is crucial for objectively measuring the impact of AI and making data-informed decisions.</li></ul>

AI Success Isn’t Just Accuracy: The Product Metrics That Truly Matter

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