AI · Productivity · Metrics1 min read
Productivity in the age of AI: what to measure now
AI changes how work is produced. Here’s a modern measurement framework that stays aligned with outcomes, quality, and sustainable pace.
AI is accelerating the “construction” part of software. That shifts the bottlenecks to planning, integration, review, and operating in production. If your measurement system is still centered on output volume, it will quickly drift away from what matters.
The new bottleneck is coordination
As code becomes cheaper to produce, coordination costs become more visible: review queues, unclear ownership, integration conflicts, and rework.
- Cycle time and its components (build/review/merge)
- Queue time and wait states
- Rework signals (churn) vs new work
Reliability stays non-negotiable
Teams can ship faster with AI and still regress reliability. Pair speed with reliability metrics (like DORA) to ensure improvements stick.
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