A comprehensive framework for measuring AI's impact on engineering productivity introduces three core dimensions: utilization (adoption rates), impact (effects on developer experience and code quality), and cost (ROI analysis). The approach builds on established productivity metrics while addressing AI-specific challenges like tracking AI-generated code, maintaining quality standards, and measuring autonomous agents. Organizations can gather data through tool APIs, periodic surveys, and experience sampling to make data-driven decisions about AI adoption and investment.
Table of contents
Q: Is there a framework to measure AI’s impact?Q: How do you ensure AI isn’t hurting long-term code quality?Q: How do you actually track AI-generated code?Q: How do you gather these metrics in practice?Q: How should we think about measuring AI agents?Q: How do you use these metrics to drive action?Final thoughtsSort: