We live in an era where project data flows abundantly. Companies track time spent on code reviews, measure process delays when delivery gets blocked and forecast everything from bug counts to team burnout levels.
The infrastructure for metrics is impressive, but here’s the interesting part: Most of these tools still focus on analysis rather than action. They tell us what happened, sometimes predict what might happen but rarely step in to actively manage the work itself.
This gap between measurement and management is where AI is beginning to…
