For years, we’ve debated the merits and pitfalls of using velocity as a proxy for productivity. We all know it’s flawed, but it’s a hard habit to break. Now, with AI code assistants like GitHub Copilot becoming standard, the game has fundamentally changed. If a developer can generate boilerplate code in seconds, what do ‘velocity’ or ‘story points’ even mean anymore?
This isn’t a problem; it’s a massive opportunity for Product Managers. The rise of AI is forcing a much-needed conversation with our engineering counterparts, shifting focus from output (lines of code, points) to outcomes (customer value, business impact). It pushes us to champion what truly matters: Are we shipping valuable software efficiently and reliably? This means focusing on DORA metrics like cycle time and deployment frequency, but more importantly, connecting that work directly to the user problems we’re solving.
To do this well, we need a clear, unified view of what our users are telling us. Centralizing user feedback and insights from various channels becomes paramount. At https://leera.io, we’re focused on helping teams build that single source of truth for user needs, making it easier to prioritize and measure the impact of what engineering ships. AI isn’t just changing how we build; it’s changing how we measure success.
How is your team adapting its measurement of engineering effectiveness in the age of AI?