How to Measure AI Coding Assistant Productivity: A Framework for Engineering Teams
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.
Most teams can't prove AI coding ROI because they measure the wrong things. This framework focuses on concrete metrics — commit-assistance rate, PR throughput, cycle-time deltas — instead of vanity numbers. Works across Claude Code, Cursor, and Copilot, and pairs with PromptConduit for automated collection.