AI Governance, Risk, and Responsible Deployment

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AI is taking on more consequential decisions, increasingly through systems that act on their own. Governance defines whether your enterprise scales up or stalls out. This IBM course builds the structures leaders need to deploy AI responsibly, with no legal or technical background required. Learn why senior-led governance produces measurably better outcomes. Climb the "trust ladder" from approving every action toward auditing systems that have earned autonomy. Identify and classify AI-specific risks—model bias, data, operational, and reputational—and manage them with a framework that registers risks so you know what to expect. Learn to read the shifting regulatory landscape, using the EU AI Act, the NIST AI RMF, and ISO 42001 as reference points and mapping requirements to concrete governance decisions. Tackle what few programs cover: governing agentic AI. Decide how much autonomy to grant and where a human stays in or on the loop, using our Sample Enterprise use cases. As a final project, apply responsible AI principles to a high-stakes case and decide whether it should ship, ship with conditions, or wait. Methods are taught tool-agnostic, with IBM and other tools demonstrated by industry experts.

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