Upon successful completion of this program, participants will be able to:

• Define a clear, values-driven AI strategy aligned with overall corporate goals.
• Establish effective governance mechanisms for AI development, deployment, and monitoring.
• Identify and mitigate key ethical risks associated with bias, transparency, and accountability in AI systems.
• Lead and motivate cross-functional teams through complex, AI-driven digital transformation initiatives.
• Understand the regulatory landscape (e.g., EU AI Act, sectoral regulations) and ensure compliance.
• Foster a culture of digital literacy and continuous learning within their teams.
• Measure the strategic impact of AI deployments beyond immediate productivity gains.
• Develop a responsible data strategy that supports AI innovation while protecting privacy.

• C-Suite Executives (CEO, COO, CIO, CDO, CTO)
• Senior VPs and Directors leading large-scale transformation
• Heads of Governance, Risk, and Compliance (GRC)
• Ethics and Legal Officers
• HR and Organizational Development Leaders
• IT Strategy and Enterprise Architecture VPs
• Policy Makers and Regulatory Advisors

• **Scenarios:** Working through a simulated data breach and ethical misuse incident involving an AI system.
• **Case Studies:** Analyzing the governance models of large tech firms and their ethical oversight mechanisms.
• **Group Activities:** Developing a draft AI Ethics Charter and core principles for a fictional company.
• **Individual Exercises:** Creating a personal leadership communication plan for introducing AI into a sensitive department.
• **Mini-Case Studies:** Rapid analysis of a public-facing AI bias controversy and proposing remediation steps.
• **Syndicate Discussions:** Debating the balance between innovation speed and stringent regulatory compliance.
• **Board Simulation:** Presenting an AI risk and opportunity assessment to a simulated Board of Directors.

Unit 1: The Strategic AI Landscape for Leaders

• Understanding Generative AI, Machine Learning, and Robotic Process Automation (RPA).
• Identifying AI capabilities that drive strategic competitive advantage.
• Analyzing AI disruption across various industry sectors.
• Developing a future-proof AI adoption roadmap.
• Case studies of successful and failed AI transformations.
• The shift from traditional IT governance to AI governance.

Unit 2: Ethical AI Governance and Frameworks

• Establishing an AI Ethics Committee or Council.
• Defining clear principles for responsible AI design and deployment.
• Implementing continuous auditing for bias detection and fairness.
• Developing transparency and explainability standards for AI decision-making.
• The legal and fiduciary responsibilities of AI system ownership.
• The role of human-in-the-loop oversight mechanisms.

Unit 3: Navigating the Regulatory Environment

• Deep dive into major AI regulations (e.g., EU AI Act principles).
• Understanding sector-specific AI regulations (e.g., finance, healthcare).
• Developing a privacy-by-design approach for AI data pipelines.
• Strategies for international regulatory harmonization and conflict management.
• Managing intellectual property (IP) and data rights in the age of Generative AI.
• The importance of clear documentation and impact assessments.

Unit 4: Inspiring Digital Change and Culture

• Techniques for communicating the vision and benefits of AI transformation.
• Managing employee concerns regarding job displacement and reskilling.
• Fostering a data literacy and experimentation mindset.
• Creating psychological safety for teams to fail and learn fast.
• Leadership behaviours required to champion ethical technology adoption.
• Structuring cross-disciplinary AI teams (data scientists, ethicists, business owners).

Unit 5: Operationalizing Responsible AI

• Designing MLOps (Machine Learning Operations) with ethical checkpoints.
• Developing standards for model testing, validation, and monitoring drift.
• Implementing techniques for explainable AI (XAI) in production environments.
• Creating an AI risk register and mitigation plan.
• Metrics for tracking responsible AI maturity and effectiveness.
• Integrating ethical governance into the procurement process for third-party AI solutions.