Enterprise AI Governance becomes much more effective when governance controls are connected directly to system behavior. AI Explainability provides the evidence layer needed to understand how models and agents reach important decisions. Organizations can capture model versions, input schemas, retrieval provenance, approved tools, policy results, confidence signals, and escalation events as machine-readable artifacts. Explainable AI techniques such as SHAP and LIME remain useful for bounded prediction nodes, while agentic systems require deeper trajectory-level diagnostics. This combination allows governance teams to investigate not only which feature influenced an output, but also which workflow step introduced a deviation. AI Transparency therefore becomes part of operational infrastructure. Enterprises can use these mechanisms to strengthen auditability, monitor drift, identify failure causes, and establish clearer accountability across complex AI deployments.