Metrics

This is Part 4 of the Velocity vs Value series. Previously: The Velocity Trap and AI Coding Tools: Greenfield vs Brownfield. You understand that velocity alone doesn’t tell the full story. You’ve seen how AI tools accelerate delivery while introducing new quality considerations. Now comes the practical part: what do you actually measure to show your team’s real impact? “We need better metrics” is easy to say. Your stakeholders need numbers. Your board needs a narrative. Your team needs to know what success looks like — and how to promote what they deliver. You need a framework that doesn’t require a PhD in data science to implement. ...

This is Part 2 of the Velocity vs Value series. In the pillar post I introduced the tension between velocity mandates and value delivery. This post goes deeper into why velocity as a target fails — and what actually works. The Speedometer Problem Velocity was designed as a planning tool. It helps teams estimate capacity. It’s a speedometer, not a gas pedal. You don’t make a car faster by taping over the speedometer and writing a bigger number. ...

Velocity vs Value: How to Measure Success in the Age of AI There’s a moment every engineering manager dreads. You’re in a steering meeting, your platform just survived a massive overhaul, your small-but-senior team is finally shipping on a solid foundation — and someone says: “We need a feature per day. Just use the AI.” I want to talk about what happens next — not the political maneuvering, but the measurement problem underneath it. Because the question isn’t whether AI makes us faster. The question is: faster at what? And how do we prove it? ...