AI Code Quality Metrics That Actually Matter: The 9 Dimensions of AI-Readiness

Traditional code metrics like cyclomatic complexity and lines of code don't capture the real blockers for AI-assisted development teams. Here are the 9 dimensions that actually matter for AI-readiness. The 9 Dimensions of AI-Readiness Semantic Consistency - How consistently your codebase uses naming conventions and patterns Context Window Efficiency - How much context AI needs to understand your code Import Chain Depth - How deep your dependency chains go Domain Cohesion - How well related logic is grouped together Pattern Recognition - How easily AI can identify and reuse patterns Documentation Coverage - How well-documented your code is Type Safety - How well your types guide AI understanding Test Coverage - How well your tests validate AI suggestions Architectural Clarity - How clear your system's structure is Why Traditional Metrics Fall Short Traditional metrics were designed for human developers, not AI assistants. They measure code complexity from a human perspective, but AI has