"For most of the last decade, AI governance was treated as a matter of intent. Enterprises articulated ethical principles, created review committees, and relied on internal guidelines to manage risk. That approach stopped working in 2025. Over the past year, regulators around the world moved from guidance to enforcement. What had been voluntary became mandatory. And for CIOs, the implications were immediate: AI governance is no longer judged by policy statements, but by operational evidence."
"In Europe, the EU AI Act moved from theory to practice, imposing binding obligations on high-risk and general-purpose AI systems. In the US, states from California to Colorado and Texas accelerated AI legislation. Federal agencies followed with detailed guidance on clinical AI, safety-critical systems and software-driven decision-making. In tandem, international standards bodies finalized concrete frameworks for AI impact assessments, incident reporting and accountability."
In 2025 regulators globally shifted AI oversight from voluntary guidance to enforceable rules. The EU AI Act imposed binding obligations for high-risk and general-purpose systems while multiple US states and federal agencies accelerated legislation and issued detailed guidance for clinical, safety-critical, and software-driven decision-making. International standards bodies finalized frameworks for impact assessments, incident reporting, and accountability. Regulators began demanding operational evidence such as model documentation, risk assessments, incident handling records, and assigned lifecycle accountability. Enterprises with fragmented, siloed governance structures struggled to provide required proof. Operational, evidence-based AI governance became a mandatory enterprise requirement with legal and financial stakes.
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