Four Signals Scorecard

We track and act on engineering metrics (cycle time, deployment frequency, etc.)

Product decisions are backed by quantitative analysis

Technical debt is measured and prioritised with data

We run experiments and measure outcomes before scaling solutions

Engineers use AI coding assistants effectively in their workflow

We have clear guidelines for AI tool usage and code review

Leadership understands AI capabilities and limitations

We are exploring AI for automation beyond code generation

Engineers feel psychologically safe to raise concerns and take risks

We have clear career paths and growth opportunities

Knowledge sharing happens naturally across teams

We can attract and retain senior engineering talent

We have a clear strategy for addressing technical debt

Legacy systems are documented and understood

Modernisation efforts deliver measurable business value

We balance maintenance work with new feature development