What is AI Governance?

AI governance has evolved from an academic concept to a practical organizational requirement as AI systems increasingly make consequential decisions about people, products, and processes. An effective AI governance program includes several key components: an organizational structure with clear accountability (governance committee, AI ethics officer, responsible AI teams), an AI inventory cataloging all AI systems with their purpose, risk level, and responsible owner, a risk classification framework that applies proportionate controls based on the potential impact of each AI system, AI impact assessments that evaluate effects on individuals, groups, society, and the environment before deployment, lifecycle management ensuring governance controls apply from design through decommissioning, data governance addressing training data quality, bias, provenance, and representativeness, transparency mechanisms informing stakeholders about AI decision-making, human oversight ensuring meaningful human control over high-risk AI decisions, and monitoring systems detecting model drift, bias emergence, and performance degradation. ISO 42001 provides the most comprehensive international standard for AI governance, while the EU AI Act establishes binding legal requirements. The NIST AI Risk Management Framework offers a voluntary risk-based approach. Organizations implementing AI governance typically start with an inventory and risk classification, then progressively add impact assessments, monitoring, and formal governance structures as the program matures.