Most companies fail due to poor data quality, not necessarily because of talent or tools, but rather due to legacy governance frameworks that were designed for compliance rather than velocity. These frameworks offer a mirage of control through audits and policies, but ultimately break under pressure. The financial toll of bad data can be substantial, with estimates suggesting that up to 20-30% of enterprise revenue is lost due to data inefficiencies, and data teams spend half their time on remediation. Legacy governance still operates like a post-mortem checklist, never preventing issues from occurring. To address this, governance must become embedded in dynamic systems, requiring continuous, explainable enforcement to meet evolving obligations. This is where Agentic Data Management comes in, offering autonomous agents that validate and fix data before it breaks business, self-healing pipelines that evolve in real-time, and built-in explainability that explains and resolves issues. By empowering stewards with agentic observability, dynamic policy enforcement, and cross-functional visibility, ADM brings clarity and cohesion to human ecosystems, making governance continuous, autonomous, and aligned to business value.