AI product analytics: How to know if your AI features are actually working

Over the past two years, the focus for SaaS product teams has shifted from rapidly deploying AI features to measuring their impact effectively. Mixpanel, among other top B2B SaaS companies, has realized the importance of AI product analytics as traditional measurement frameworks fall short due to the probabilistic nature of AI models. These models can degrade over time and may show lower engagement even when functioning optimally. A two-layered analytics framework is proposed to address these challenges by combining model behavior signals, like latency and error rates, with user behavior signals, such as retention and feature reuse. This approach helps interpret whether AI features are delivering genuine value and aligns with strategies outlined in PwC's 2026 AI report. By integrating model performance and user behavior data, companies can diagnose issues more effectively and adapt their measurement practices as products evolve. Mixpanel's tools and templates play a crucial role in enabling these analytics, helping teams like Observe.AI understand user engagement and backend performance simultaneously.