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This article argues that AI governance is a strategic operational requirement, not just a compliance checkbox, for enterprises scaling AI systems.
•Effective governance requires communication, collaboration, and iteration—not just policy documentation and approval workflows.
•Organizations that layer AI onto existing slow review processes (privacy, architecture, security reviews) risk falling behind as AI capabilities evolve monthly.
•As AI shifts from generating insights to taking agentic actions, governance responsibility must shift toward business subject matter experts, not just technical teams.
•Leadership must define accountability structures upfront—measuring agent performance, escalation paths, and criteria for pulling AI out of production.
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Governance scales best when a "paved path" architecture with built-in traceability and auditability is established, and when feedback loops drive continuous improvement.
This summary was automatically generated by AI based on the original article and may not be fully accurate.