AI Usage Metrics Every Engineering Manager Should Track

The article explores the challenges engineering teams face in measuring the return on investment (ROI) from AI tools, highlighting a disconnect between the increased adoption of AI and satisfaction with its effectiveness. Traditional metrics like lines of code and number of pull requests are inadequate proxies for productivity and fail to capture AI's true impact. Research presents mixed results, with some studies indicating productivity gains from AI tools like GitHub Copilot, while others show increased completion times. The article introduces Weave, a platform using machine learning to provide deep insights into AI usage and productivity by analyzing pull requests and classifying work into categories such as new features and bug fixes. Weave aims to enhance traditional metrics like DORA by offering comprehensive context and insights to better connect AI usage with business outcomes. The proposed approach emphasizes understanding AI adoption patterns, quality metrics, and financial impacts, suggesting that Weave's AI-powered analysis can offer competitive advantages by providing real-time productivity insights.

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