AI Governance, Responsible AI and Data Governance: Connecting the Dots

Artificial intelligence promises transformative business value, but without strong governance foundations, AI initiatives risk being biased, opaque, or non-compliant. Organizations are increasingly expected—by regulators, customers, and society at large—to ensure AI systems are ethical, explainable, and trustworthy. Yet, most governance efforts remain fragmented: AI governance is treated separately from Responsible AI principles, while Data Governance operates in a silo.
This seminar connects the dots. Participants will gain a comprehensive understanding of how Data Governance underpins Responsible AI, and how AI Governance frameworks operationalize ethics and compliance in practice. Combining strategy, case studies, and hands-on frameworks, the course provides attendees with the tools to design and implement governance approaches that make AI not only innovative, but also reliable and responsible.

Learning Objectives

By the end of this seminar, participants will be able to:

• Understand the principles of AI Governance and the importance of Responsible AI
• Explore the role of Data Governance in supporting ethical and compliant AI practices
• Learn how to develop and implement AI Governance frameworks that align with organizational goals
• Discover best practices for ensuring transparency, accountability, and fairness in AI systems
• Examine real-world case studies that highlight successful AI and data governance integration
• Gain practical tools and techniques for fostering a culture of responsible AI and data management
• Identify common challenges and strategies to overcome them in the governance of AI and data.

Who is it for?

• Data & AI Leaders: Chief Data Officers, AI program leads, and executives responsible for data-driven strategy
• Governance & Compliance Professionals: Data Governance managers, risk officers, and compliance teams seeking to embed AI accountability
• Technology Leaders: Architects, product owners, and IT leaders building or overseeing AI/ML solutions
• Business Leaders & Policy Makers: Executives and decision-makers needing to ensure AI aligns with organizational goals and ethical standards
• Researchers & Educators: Professionals in higher education and research institutions deploying AI in sensitive, high-stakes contexts.

Detailed Course Outline

Part 1 — Foundations & Risks

• Session 1: AI Primer – The growth of AI, opportunities, and emerging risks .
• Session 2: AI Pitfalls – Understanding algorithmic bias, data quality challenges, and unintended consequences.
• Session 3: The Need for Governance – Why AI governance is essential, and how it intersects with Responsible AI and Data Governance.

Part 2 — Frameworks & Practices

• Session 4: AI Governance in Practice – Policies, standards, and risk management frameworks.
• Session 5: Responsible AI – Embedding ethical principles (fairness, transparency, accountability, inclusivity) into systems and processes.
• Session 6: Data Governance for AI – Addressing data ethics, data quality, lineage, and security as enablers of trustworthy AI .

Part 3 — Connecting the Dots & Implementation

• Session 7: Practical Integration Framework – A blueprint for combining AI Governance, Responsible AI, and Data Governance.
• Session 8: From Frameworks to Business Strategy – Scaling governance into enterprise programs, communicating value, and embedding sustainable practices.