How to Measure the Impact of AI Coding Tools

A Practical Guide for Engineering Leaders

Your team is using AI coding tools. Your CFO wants to know if they’re working. “Acceptance rate” and “lines of code” won’t cut it.

This guide gives you a practical framework for measuring what actually matters: Are you shipping quality software faster than before?

Built from 12 months of real data from GitKraken’s own engineering team, with insights from our VP of Engineering who led the effort.

What You’ll Learn

  • Which metrics actually indicate AI impact (and which ones mislead)
  • The AI Impact Matrix: a simple framework for interpreting speed vs. quality tradeoffs
  • What GitKraken’s engineering team saw over 12 months of AI adoption
  • How to set up your baseline and segment by adoption level
  • Common patterns and what they mean for your team
  • How to present findings to leadership without the spin

 

Download the guide to get a real-world look at measuring AI coding tool impact, built by practitioners, not consultants.

Read the guide

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