Engineering Management

This is the final post in the Velocity vs Value series. Previously: The Velocity Trap, AI Greenfield vs Brownfield, and Measuring What Matters. AI coding tools accelerate delivery — we’ve covered the data in this series. They’re genuinely valuable. But acceleration without visibility creates a specific risk: quality degradation that nobody notices until it compounds. Here’s how it plays out: an AI tool generates a feature in 20 minutes. It passes all automated tests. CI is green. The PR is merged. Velocity: +1. Two weeks later, a security audit flags a pattern. A month later, a production issue traces back to a function with cyclomatic complexity of 47. Three months later, the module is hard to modify because the generated code lacks documentation and clear structure. ...

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? ...

Stop Throwing AI at Your Software Process. Start Strategically Placing It. A practical framework for integrating AI into brownfield software development The CEO’s Napkin vs. Reality I’ve sat in enough boardrooms to know the drill. Someone draws four boxes on a whiteboard: Idea → Design → Develop → Deploy Then the AI conversation starts, and those four boxes collapse into three: Idea → ☁️ AI Magic ☁️ → Money I get it. AI is exciting. The demos are impressive. But if you’ve ever managed a real software team — especially one maintaining systems that have been running for years — you know that napkin sketch is missing about 98% of what actually happens. ...