We Measured AI Impact for 12 Months. Here’s What Actually Happened.

AI coding tool adoption should be evaluated through delivery and quality outcomes rather than vendor-provided activity metrics such as suggestion acceptance rates or code volume. By grouping teams according to objective AI engagement over rolling four-week periods and comparing lead time, throughput, bug work, review time, churn, abandoned pull requests, defect escape rates, and recovery times across a year, the organization found that high-adoption teams improved delivery speed two to three times while maintaining stable quality and incident recovery. Increased churn and abandoned pull requests often reflected faster experimentation and iterative development rather than waste, although their meaning depended on context. A two-dimensional framework based on speed and quality helped identify whether teams were succeeding, outpacing review capacity, failing to reach effective adoption, or experiencing genuine problems. When issues emerged, the organization used team discussions, knowledge sharing, training, and additional review capacity rather than punishment, emphasizing that data should guide investigation rather than dictate conclusions. Sharing dashboards openly with teams and measuring team-level improvement instead of individual performance helped turn metrics from surveillance into a collaborative tool for improving engineering practices.

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