How to Measure AI Coding Assistant Productivity: A Framework for Engineering Teams
· 9 min read
How to Measure AI Coding Assistant Productivity: A Framework for Engineering Teams
Here's a question I get asked constantly: "How do you know if AI coding tools are actually making your team more productive?"
It's a fair question. Engineering leaders are investing real budget in Claude Code, Cursor, and GitHub Copilot seats. Developers are restructuring their workflows around these tools. But when someone asks for data — actual numbers on impact — most teams have nothing to show.
I've been working on this problem for over a year, first as an engineering leader trying to justify AI tooling investments at Georgia-Pacific, and then by building PromptConduit to close the analytics gap. Here's the framework I've developed for measuring what actually matters.