Measuring AI effectiveness beyond developer productivity metrics

This post introduces the concept of 'AI Impact' as a new dashboard within GitLab's Value Stream Analytics. It details the challenges of measuring AI effectiveness, particularly for developer productivity tools, by critiquing metrics like lines of code and acceptance rates. It advocates for focusing on business outcomes (lead time, cycle time, production defects, user satisfaction) and proposes combining quantitative SDLC data with qualitative developer feedback. The post outlines the planned approach for the AI Impact dashboard, which will blend Value Stream Analytics, DORA metrics, and GitLab Duo usage data to provide a comprehensive view of AI's influence on the SDLC.