How tech companies measure the impact of AI on software development

Overview
This newsletter article examines how leading tech companies measure the impact of AI on software development. It presents data-driven approaches, metrics, and frameworks used by organizations such as GitHub, Google, Dropbox, Monzo, and Atlassian, based on interviews with CTO Laura Tacho.

Key Takeaways

• Companies track developer productivity, code quality, and cycle time changes after AI tool adoption.
• Controlled experiments and A/B testing are common to isolate AI effects.
• Business impact is measured through cost savings, feature velocity, and developer satisfaction scores.
• Transparent reporting and continuous feedback loops help iterate on AI tool usage.

Who Would Benefit

• Engineering managers looking to evaluate AI tooling.
• Technical leaders responsible for ROI on developer tools.
• CTOs and VP of Engineering planning AI strategy.
• Developers interested in understanding how AI impacts their workflow.

Frameworks and Methodologies

• Experimentation frameworks (A/B testing, canary releases).
• Metric dashboards for productivity and quality.
• Qualitative feedback loops (surveys, interviews).