30+ AI product metrics to track today

Measuring the effectiveness of AI features requires metrics beyond standard activity indicators like pageviews or DAU, as these do not capture the value or impact of AI on user behavior or business outcomes. Many product teams still track AI ROI through indirect measures such as time saved, but a more comprehensive approach involves metrics in three categories: user adoption and engagement, model monitoring, and business impact. User engagement metrics, such as the number of prompts submitted or interactions per user, reveal meaningful use, while model monitoring focuses on accuracy, latency, and feedback to ensure quality. Business impact metrics, including retention impact and revenue conversion, assess the AI feature's contribution to the company's growth. Effective tracking of these metrics helps align AI product performance with business goals, and platforms like Mixpanel offer tools to integrate model quality signals with user behavior data for a holistic view of AI feature success.

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