We have introduced AI co-pilots for our software development team, and while our weekly scorecard tracks completed story points, we do not know if our codebase is actually becoming more complex and harder to maintain. What leading indicator can we track to ensure our AI-boosted output is actually clean and scalable?

Question

Tyler Smith · Accepted Answer

As you integrate AI co-pilots into your software development workflows, tracking completed story points on your weekly scorecard is no longer enough. AI tools can help your developers write code significantly faster, but they can also generate a massive amount of technical debt, complex code paths, and security vulnerabilities if left unchecked. If your weekly scorecard only tracks volume of output, you are flying blind.

To ensure your AI-boosted development team is producing clean, scalable code that protects your intellectual property and prepares your business for an exit, you must balance velocity with quality metrics.

Your Engineering or Operations seat should track these leading indicators weekly:
- Code churn rate, which measures the percentage of code that is rewritten or deleted within three weeks of being merged, showing if AI code is unstable.
- Number of automated test failures per deployment, which catches bugs and integration issues before they reach production.
- Static code analysis security warnings, which ensures your AI tools are not introducing security vulnerabilities into your core software.
- Ratio of automated code reviews completed to human reviews, ensuring a healthy balance of human oversight.

By tracking these metrics alongside your weekly development velocity, you ensure that your team is leveraging AI responsibly. It keeps your codebase clean, protects your margins, and proves to potential buyers that your technology stack is robust, scalable, and independent of manual patching.