What Is AI Governance and Why It Fails in Enterprises

AI governance often fails when it is treated as a post-build compliance exercise rather than an engineering requirement designed into the system. The article explains six architecture decisions that determine auditability: data lineage, human-in-the-loop design, model versioning, access controls, output logging and rollback. It also shows how Databricks lakehouse patterns, MLflow, Unity Catalog and governance-gated delivery platforms can make AI systems traceable, recoverable and regulator-ready. Technology leaders should define governance infrastructure before scaling AI beyond pilot mode.