AI Governance for Executives & Board Members

About this course

Disclaimer: This course contains the use of artificial intelligence(AI).

Artificial Intelligence is rapidly transforming how organizations operate, compete, and grow. From predictive analytics and automation to generative AI and large language models, AI is influencing strategic decisions at the highest levels of leadership.

Yet while AI capabilities are advancing at unprecedented speed, governance frameworks often remain underdeveloped.

Boards are being asked tougher questions. Regulators are introducing new compliance standards. Investors are demanding accountability. Customers expect ethical and transparent AI use.

In this environment, AI governance is no longer optional. It is a core leadership responsibility.

This course is designed specifically for executives and board members who must oversee AI initiatives without getting lost in technical complexity. You do not need to be an engineer or data scientist. You need clarity, structure, and confidence in how AI is governed across your organization.

Throughout this program, you will gain a strategic understanding of AI risk and oversight. You will explore how AI differs from traditional technology, why it introduces unique governance challenges, and how boards must adapt their fiduciary responsibilities in response.

We will examine real-world risk categories, including bias and discrimination, data privacy exposure, hallucination risk in generative AI systems, vendor dependency, cybersecurity vulnerabilities, and regulatory non-compliance. More importantly, you will learn how to structure controls that reduce these risks while still enabling innovation.

The course provides a step-by-step framework for building an AI governance operating model. You will learn how to define executive accountability, establish oversight committees, design reporting dashboards, and implement a structured AI risk register. You will understand how to classify AI systems by risk level and how to prioritize high-impact use cases.

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