AI coding assistants like GitHub Copilot, Cursor, and Windsurf are transforming how developers write software, but most organizations are measuring the wrong things. Acceptance rates and lines of code don’t tell you if AI is actually delivering value. Are features shipping faster? Are teams improving quality? Is the business seeing a return?
This white paper flips the script. Get a comprehensive framework to measure what really matters, from code generation to production, from pull requests to ROI.
What You’ll Learn:
- Why acceptance rates, lines of code, and velocity are misleading indicators of AI success
- The risks of vanity metrics and how they mask real performance
- A new framework that traces AI code from keystroke to customer impact
- How to measure AI impact across four critical pillars:
- Development Efficiency
- Delivery Excellence
- Code Quality & Risk
- Business Outcomes
- How to use advanced analytics (like Opsera’s Leadership Dashboard) to track ROI, optimize workflows, and uncover bottlenecks
- The real-world strategies for instrumenting AI measurement at scale, beyond adoption to measurable value
Who Should Read This:
Engineering leaders, DevOps and platform teams, CTOs, and product executives who want to move past buzzwords and understand the true enterprise impact of AI-powered development.
