The Business Case for AI Code Review: Costs, ROI, and How to Measure Impact
This chapter covers why the productivity numbers from AI coding tools don’t tell the full story, where the hidden costs of AI-generated code actually land, and how to build a business case for AI code review with metrics that hold up in budget conversations. It closes with what good looks like 90 days after rollout — and how to measure it.
Key Takeaway
AI coding tools have increased development output by 25–35%. The cost of that velocity shows up later — in production incidents, rework cycles, and senior engineers debugging code they didn’t write. AI code review is how engineering leaders close that gap without slowing teams down.
The Productivity Gain Is Real. So Is the Bill That Comes With It.
The pitch for AI coding tools is clean: developers ship 25–35% more code, cycle times compress, and teams get more output without adding headcount. The numbers are real and...
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