Effective AI coding governance requires automated, pipeline-integrated architectural controls and risk-proportional human review, not just unenforceable, paper-based acceptable use policies.
AI coding deployments deliver real value only when context engineering embeds your organisation’s standards, preventing generic code and constant rework.
AI coding debt accumulates silently and rapidly, creating unmaintainable codebases. Organisations must implement deliberate governance and tracking frameworks to manage this risk.