There is a persistent and costly misconception in how many organizations approach AI: that risk management is the brake, and adoption is the accelerator. This course begins by dismantling that framing entirely.
AI readiness and risk management are not constraints imposed on AI adoption. They are the scaffolding that makes it possible. Organizations that govern well, adopt faster. Organizations that adopt without governing, stall, fail, or face regulatory and reputational consequences that set them back further than if they had moved more carefully from the start.
This two-day course demonstrates that the organizations winning with AI are not those taking the fewest precaution but the companies that have built the data governance, risk architecture, legal clarity, and stakeholder accountability that enables them to move with both speed and confidence.
Drawing on frameworks including from IBM Research's AI Security, Safety, and Governance practice and Harvard Law School's work on the legal, ethical, and regulatory dimensions of AI, participants will develop a comprehensive, actionable understanding of what it means to be truly AI-ready. The program covers the full stack of AI risk, from model-level vulnerabilities and system-level threat vectors to liability law, bias, informed consent, data privacy, and the global regulatory landscape, and translates each into practical governance decisions.
The organizing question across both days is not 'what could go wrong?' but rather: 'what does getting this right actually look like, and how does it unlock everything else?'
Join your peers on campus to reframe risk as a strategic enabler and to build the infrastructure required for responsible, high-velocity AI adoption.
