AI for Data Governance, Ethics & ComplianceMonday, 15 June 2026 - AT (Arabian Time, GMT+03:00)

Theme: Explore how Artificial Intelligence creates value, shapes competitive advantage, and demands responsible leadership in its application.

Session 1: Artificial Intelligence: apabilities,Boundaries and Business Impact

• Distinguishing AI from traditional automation and software
• How intelligent systems learn from data, recognise patterns, and refine outcomes?
• Core categories of AI: narrow vs. general, supervised vs. unsupervised learning
• Real-world applications across sectors — from customer experience to risk management
• The rapid rise of generative AI, large language models, and intelligent agents
• How emerging technologies are reshaping business models, operations, and service delivery?
• Recognising the limits of AI: hallucination, bias, over-reliance, and human fallibility
• A visual journey through AI’s evolution — from expert systems to generative intelligence – this looks good

Session 2: The Strategic Landscape — Opportunity and Challenge

• Where AI creates enterprise value:productivity, personalisation, precision, and scale?
• The societal implications of adoption: misinformation, accountability, job design
• The environmental cost of computation and data storage
• The leader’s role in defining strategic boundaries for responsible innovation

Session 3: Principles of Responsible AI

• Embedding human oversight and governance into intelligent systems
• Achieving transparency and explainability to strengthen trust
• Balancing innovation ambition with organisational risk appetite
• How responsible AI safeguards brand integrity and long-term performance?

Session 4: Ethics as the Cornerstone of Trust

• The FAST principles – Fairness, Accountability, Sustainability, and Transparency
• Lessons from ethical success and failure in real-world deployments
• Building stakeholder and societal confidence through ethical leadership – again, looks really good

Activities:


1. The Strategic Landscape: Participants research one good and one negative story about AI
2. Principles of Responsible AI: From a scenario, groups discuss governance/accountability - who is responsible?
3. The Red Line: From given scenarios, participants discuss at which point AI crosses the line from useful to unethical

Theme: Understand how trusted data and effective governance frameworks underpin every successful and accountable AI initiative

Session 1: Data as the Strategic Fuel of Intelligence

• How AI transforms raw data into insight and prediction?
• Understanding structured, unstructured, and personal data ecosystems
• Why decision quality depends on data quality?
• Business cases where weak data governance undermined AI outcomes

Session 2: Establishing Robust Data Governance

• Core principles: ownership, quality, access, lifecycle management
• Defining roles and accountability across data stewardship functions
• Aligning data strategy with organisational objectives and compliance expectations

Session 3: Managing Bias, Privacy, and Fairness

• How imbalance and human assumptions enter datasets and models?
• Practical approaches to detect, reduce, and monitor bias
• Transparency and explainability as enduring pillars of trust

Session 4: Governance in Practice

• Translating principles into project governance frameworks
• Setting measurable objectives, controls, and documentation standards
• Ensuring cross-functional oversight between technical, legal, and operational teams

Activities:


1. Data Chain: Delegates visualise how data moves through an organisation and where governance responsibilities sit
2. Data Detective: Teams identify problems in fictional datasets (bias, duplication, missing data)
3. Trust Factors Mapping: Participants explore what builds or erodes confidence in AI decision making
4. AI Governance Checklist – What questions leaders should ask when working with AI

Theme: Examine the global standards, regulatory expectations, and risk management practices that turn compliance into strategic assurance

Session 1: From Governance to Assurance

• How governance structures underpin regulatory compliance?
• Connecting ethical intent, data integrity, and assurance frameworks
• Viewing compliance not as a constraint, but as a strategic protection and differentiation

Session 2: The Global Regulatory Environment

• Overview of emerging frameworks: ISO/IEC 42001, UK GDPR, OECD AI Principles, EU AI Act
• Clarifying organisational accountability and chain of responsibility
• Regional trends shaping future compliance in the GCC, UK, and EU contexts

Session 3: Risk and Assurance in Intelligent Systems

• Key categories of AI risk: ethical, operational, reputational, and cybersecurity
• Designing proportionate controls, documentation, and audit trails
• Building a culture of informed risk ownership across leadership functions

Session 4: Operationalising Compliance

• Integrating AI oversight into existing corporate governance models
• Creating alignment across Legal, IT, HR, and Compliance teams
• Turning regulatory excellence into a competitive and reputational advantage

Activities:

• AI Compliance Challenge: Teams review a fictional AI rollout and identify compliance and privacy gaps
• AI Risk Radar: Groups map top AI risks and brainstorm practical controls
• Policy Builder: Participants design a short “AI Code of Conduct” template suitable for their organisation
• Approval Line: From a scenario, delegates discuss which organisational roles should be involved in approving or halting an AI project

Theme: Develop the foresight, capability, and culture to lead AI adoption responsibly and sustain trust in an evolving technological landscape

Session 1: Building Organisational Confidence and Capability

• Executive accountability for responsible AI outcomes
• Developing multi-disciplinary teams and future-ready skill sets
• Embedding continuous professional learning and data-driven culture

Session 2: Leading Through Technological Change

• Managing transformation in governance and compliance structures
• Communicating AI values and ethical expectations internally
• Enabling digital confidence and resilience across the workforce

Session 3: Anticipating the Next Horizon

• Forthcoming ethical and regulatory challenges — agentic AI, autonomy, and adaptive systems
• The evolution of global assurance standards
• Positioning sustainability and ESG alignment within AI strategy

Session 4: From Insight to Action

• Reflection on key insights from the programme
• Crafting a Responsible AI Charter and communication plan
• Defining 90-day implementation priorities for governance, ethics, and compliance maturity

Activities:

• Future Vision Discussion: Groups imagine their organisation’s governance landscape in 2030
• Responsible AI Charter: Teams draft a set of commitments to guide future AI projects
• 90-Day Plan: Each participant writes three concrete steps to implement after the course, shared in peer pairs for accountability. - the content looks really good! Lots to get through. I am just not sure on the overview and title