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.

Upon successful completion of your course, you will earn a certificate of completion from the MIT Sloan School of Management. This course may also count toward MIT Sloan Executive Certificate requirements.

At the end of this course, you will be able to:

• Reframe AI risk management as a driver of speed, trust, and competitive advantage
• Identify and assess risks across models, systems, legal frameworks, and organizational structures
• Distinguish between model-level vulnerabilities and system-level threats
• Design governance frameworks that align stakeholders, clarify accountability, and enable responsible deployment
• Navigate evolving regulatory environments, including global standards and liability considerations
• Build and implement layered risk mitigation strategies, including guardrails, monitoring, and escalation pathways
• Translate governance investments into measurable business value and strategic advantage
• Develop a board-ready AI Readiness and Governance Playbook tailored to your organization

This program combines rigorous frameworks with direct application to your organization’s AI initiatives. You won’t just learn about AI governance—you will actively design it.

A central element of the experience is the AI Risk Quadrant, a practical framework used to map and prioritize risks across model, system, legal, and ethical dimensions. Alongside this, you will construct a Stakeholder Liability Map, clarifying accountability across developers, operators, regulators, and end users—surfacing gaps that often go unaddressed.

Throughout the two days, you will build a Personalized AI Readiness Assessment and Governance Playbook. This structured, board-ready deliverable integrates risk identification, data readiness, governance architecture, regulatory considerations, and a clear roadmap for implementation.

You will also complete an AI Readiness Canvas, a one-page diagnostic tool designed to assess your organization’s preparedness across data, systems, governance, legal exposure, and workforce readiness.

Together, these tools form an integrated learning journey. You will leave not only informed, but equipped with a practical governance framework and a clear path to scale AI responsibly and strategically.

Learn more about the in-person course experience.

This course is ideal for:

• Senior executives responsible for AI strategy, digital transformation, or enterprise risk
• Leaders moving from AI experimentation to structured, scalable deployment
• Innovation, operations, and strategy leaders evaluating AI opportunities and risks
• Legal, compliance, and governance professionals overseeing responsible AI use
• Board members and functional leaders in high-stakes industries such as healthcare, finance, and technology