One Global AI Policy or Regional Autonomy?

Should AI governance be controlled centrally across a global organization, or should regional teams have greater autonomy? In this episode, we explore the governance dilemma between centralized, decentralized, and hybrid AI governance models. Using a hypothetical global organization, the discussion examines how enterprise-wide standards can coexist with legitimate regional flexibility — including common AI risk classifications, decision rights, escalation thresholds, local regulatory considerations, and accountability. The episode also applies the NIST AI Risk Management Framework through its four functions — GOVERN, MAP, MEASURE, and MANAGE — to show how governance architecture can connect enterprise direction with local context and operational execution. Key themes include: • Centralized vs. decentralized AI governance • Hybrid governance models • Enterprise minimums and local overlays • AI risk classification • Decision rights and escalation • Regional regulatory and operational context • NIST AI RMF: GOVERN, MAP, MEASURE, MANAGE • Balancing consistency with innovation Flow AI Co., Ltd. — Thailand Governance · Compliance · Technology Law Building Trust in Emerging Technologies This video is provided for educational and general informational purposes and does not constitute legal or regulatory advice.