I've been exploring how to effectively measure the impact of AI on my engineering team. It seems tricky to find the right metrics. Currently, I'm testing a few: the time taken from identifying a blocker to assigning a clear owner, the duration unresolved decisions remain open, and the rate at which merges are reopened. Does anyone have insights on more effective metrics that could predict reduced rework in future sprints? I'm especially interested in practical examples from teams with varying levels of experience.
4 Answers
Measuring programming productivity with AI is a challenge. If someone figures it out, they’d be revolutionizing the way we measure coding efficiency overall. So, don’t get too hopeful just yet—it’s a tough nut to crack.
Your question reminds me of our experience with design system adoption metrics. Often, the real gain isn't just in numbers but how much more bandwidth the dev team has to tackle real user issues instead of dealing with repetitive tasks. This shift can significantly improve user experience!
We came up with a quirky metric we call 'slops per second'. Thanks to AI, it helps us gauge the amount of unpolished work our prompt engineers produce. It’s a bit tongue-in-cheek, but it gets the point across!
Looking at my previous company, we tracked two main metrics: the total cost of labor—which we wanted to minimize—and revenue per share, which we aimed to maximize. AI played a role in trimming labor costs and boosting revenue.
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