There is a moment every engineering leader eventually faces. The AI coding tool rollout is complete. Dashboards show commit frequency up 30%. Pull request volume has climbed. Deployment frequency looks healthier than it did six months ago. And yet, somehow, the engineering organization feels slower. Senior engineers are frustrated. Onboarding new hires takes longer than before. Code reviews have turned perfunctory — rubber stamps on AI-generated output that nobody fully owns.
Something is wrong, but the metrics say everything is fine.