2 posts tagged with "product-metrics"

Your AI feature launched three months ago. DAU is up. Session length is climbing. Your dashboard looks green. But here is the uncomfortable question: are your users actually adopting the feature, or are they just tolerating it?

Most teams track AI feature adoption with the same metrics they use for traditional product features — daily active users, session duration, feature activation rates. These metrics worked fine when features behaved deterministically. Click a button, get a result, measure engagement. But AI features are fundamentally different: their outputs vary, their value is probabilistic, and users develop trust (or distrust) through repeated exposure. The standard metrics don't just fail to capture this — they actively mislead.

You ship an AI-powered summary feature for your document editor. Adoption is great — 40% of users activate it within the first week. Your PM writes a celebratory Slack message. Two months later, average session duration has dropped 25%, collaborative editing is down, and your power users are quietly churning. Nobody connects these trends to the shiny new feature because the dashboard that tracks the summary feature shows nothing but green.

This is AI feature cannibalization: when an AI shortcut solves a user's immediate problem while destroying the engagement loops that make your product worth paying for. It is one of the most insidious failure modes in product development today because every metric that tracks the feature itself looks healthy, even as the product-level metrics decay.