AI Strategy and Governance Masterclass for Boards and Senior Leaders Course

Introduction:

AI strategy and governance is the work of deciding where artificial intelligence creates value for an organisation or a state, and setting the oversight and accountability that keep its use safe and lawful. This 2-week AI Strategy and Governance Masterclass for Boards and Senior Leaders Course is for directors, executives, heads of public entities and the AI, risk and policy professionals advising them; each delegate leaves with an AI Strategy and Governance Charter. It is taught at executive, conceptual level through cross-sector cases and a simulated board session, drawing on ISO/IEC 42001:2023, the international AI management system standard.

Course Objectives:

• Distinguish an AI strategy from a digital or data strategy, and define the decisions on AI ambition, value and risk appetite that belong to the board
• Compare ISO/IEC 42001, the NIST AI Risk Management Framework and the OECD AI Principles, and select the mix that fits an organisation's AI exposure
• Interpret AI business cases, data and compute options and sovereign infrastructure choices well enough to challenge management proposals
• Apply board oversight of AI risk, assurance and audit across the three lines, including generative and agentic AI systems
• Justify decisions on AI talent, workforce transition, ethics and accountability to employees, citizens, investors and regulators
• Apply AI value and risk indicators to govern an AI investment portfolio and defend an AI Strategy and Governance Charter before a board

Target Audience:

• Board members, chairs and committee members of companies, public entities and funds, deciding how much AI risk the organisation accepts and how management is held to account
• Chief executives and executive committee members who own enterprise results, judging which AI investments to fund, pause or stop
• Heads and deputy heads of government entities, deciding where AI should change public services and under what safeguards
• Chief AI, data and digital officers who propose AI strategy, judging which governance structures, platforms and partners to put before the board
• Strategy directors and risk and audit committee members, deciding whether AI plans, controls and assurance reports give a true picture
• Senior AI governance and policy professionals who draft policies and charters, deciding which international frameworks and principles the organisation adopts

Course Outline:

Day 1: Foundations: AI Value, Strategic Choices and the Board's Role

• AI Strategy vs Digital Strategy: Where AI Choices Differ From Technology Plans
• AI Value Levers: Productivity, Revenue Growth, Risk Reduction and Public Benefit
• Board Duty of Oversight for AI: Direction, Monitoring and Accountability of Management
• National AI Strategy Architecture: Pillars, Enablers and Lead Entities Compared Internationally
• AI Ambition Statement: Defining Scope, Risk Appetite and Strategic Intent for AI

Day 2: Frameworks: International AI Governance Standards and Principles Compared

• ISO/IEC 42001:2023 AI Management System: Clauses, Controls and Certification Logic
• ISO/IEC 42001 vs NIST AI RMF 1.0: Certifiable System Compared With Voluntary Framework
• NIST AI RMF Core Functions: Govern, Map, Measure and Manage in Practice
• OECD AI Principles: Revised Values-Based Principles for Trustworthy and General-Purpose AI
• ISO/IEC 38507:2022 Governance Implications of AI: Guidance Written for Governing Bodies

Day 3: Setting AI Strategy: Use-Case Portfolios, Business Cases and Operating Models

• AI Use-Case Prioritisation Matrix: Value, Feasibility and Risk Scored Side by Side
• AI Business Case Structure: Costs, Benefits, Assumptions and Payback Tests for Boards
• Build vs Buy vs Partner: Sourcing Choices for AI Models and Platforms
• AI Operating Model Options: Centre of Excellence, Hub-and-Spoke and Embedded Teams
• AI Strategy Roadmap: Sequencing Pilots, Scale-Up Decisions and Capability Investments Over Time

Day 4: Enablers: Data, Compute and Sovereign AI Infrastructure Decisions

• Data Strategy for AI: Data Quality, Ownership, Access and Lineage Questions
• Sovereign Compute vs Hyperscale Cloud: Control, Cost and Resilience of AI Infrastructure
• Sovereign AI Models: National Language Models, Open Weights and Licensing Choices
• AI Supply Chain Map: Model Providers, Chips, Data Centres and Critical Dependencies
• Data Sharing Arrangements: Trusted Data Spaces, Synthetic Data and Privacy-Enhancing Techniques

Day 5: Case Study: Week-One Integration on Strategy, Frameworks and Enablers

• Case AI Strategy Review: Testing a Published Enterprise AI Strategy for Clear Choices
• Case Framework Gap Analysis: Mapping Current Controls Against ISO/IEC 42001 Requirements
• Case Business Case Challenge: Board Questions on an AI Investment Proposal
• Sovereign Hosting vs Public Cloud: Case Infrastructure Decision for a Sensitive Workload
• Week-One Findings Log: Draft Inputs for the AI Strategy and Governance Charter

Day 6: Advanced Topics: Generative AI, Agentic AI and AI in Public Services

• Generative AI vs Agentic AI: Content Generation Compared With Autonomous Task Execution
• NIST AI 600-1 Generative AI Profile: Risks Specific to Generative AI Systems
• Agentic AI Control Points: Delegated Authority, Human Oversight and Kill-Switch Design
• AI in Public Services: Eligibility Decisions, Citizen Services and Algorithmic Transparency Registers
• Frontier and General-Purpose AI Models: Capability Evaluations and Model Provider Obligations

Day 7: Risk, Assurance and Audit: Board Oversight of AI Risk

• ISO/IEC 23894:2023 AI Risk Management: Guidance on Identifying and Treating AI Risks
• First vs Second vs Third Line: AI Risk Ownership, Oversight and Independent Assurance
• ISO/IEC 42005:2025 AI System Impact Assessment: Effects on Individuals and Society
• EU AI Act Risk Tiers as International Reference: Prohibited, High-Risk and Transparency Duties
• AI Audit and Assurance Reports: What Audit and Risk Committees Should Ask

Day 8: People and Accountability: Talent, Workforce Transition, Ethics and Liability

• Augmentation vs Automation: Workforce Impact Choices for Roles, Tasks and Skills
• AI Talent Strategy: Hiring, Reskilling and Board-Level AI Literacy Plans
• AI Ethics Committee Charter: Mandate, Membership, Escalation Rights and Decision Records
• Accountability and Liability for AI Harm: Director, Executive and Supplier Responsibilities
• Stakeholder Communication on AI: Explaining Use, Limits and Safeguards to the Public

Day 9: Measurement: AI Value Realisation, Portfolio Governance and Maturity Benchmarks

• Leading vs Lagging AI Indicators: Adoption, Model Performance and Realised Business Value
• AI Investment Portfolio Review: Stage Gates, Kill Criteria and Capital Reallocation Decisions
• AI Maturity Assessment Models: Scoring Strategy, Data, Talent, Governance and Technology
• Board AI Dashboard Design: Value, Risk, Incident and Compliance Measures Together
• AI Governance Effectiveness Review: Internal Audit Findings, Incident Lessons and Charter Updates

Day 10: Simulation: Board Session and the AI Strategy and Governance Charter

• Simulated Board Briefing Pack: Applying Business Case and Portfolio Analysis to Decisions
• Simulated Risk Committee Hearing: Applying Assurance Questions to a High-Risk AI System
• Simulated Incident Escalation: Applying Agentic AI Control Points Under Time Pressure
• Board Approval vs Conditional Approval: Recording AI Decisions, Conditions and Follow-Up
• AI Strategy and Governance Charter: Final Assembly, Defence and Peer Challenge

Skills You Will Gain:

• AI Use-Case Prioritisation
• AI Business Case Appraisal
• AI Management System Oversight
• AI Risk Appetite Setting
• AI Impact Assessment Review
• Agentic AI Control Design
• AI Portfolio Governance
• AI Workforce Transition Planning

Why Attend This Course:

• From approving AI projects one at a time to governing them as a portfolio with stage gates and stop criteria
• From relying on management's word on AI safety to asking assurance questions grounded in international standards
• From treating generative and agentic AI as an IT matter to setting clear limits on delegated authority and human oversight
• From scattered AI policies to an AI Strategy and Governance Charter tested before a simulated board

Conclusion:

Governing artificial intelligence well means a board that chooses where AI should and should not be used, sets its risk appetite, and checks that management delivers value within those limits. The course makes three distinctions clear: a certifiable AI management system compared with a voluntary risk framework, generative AI compared with agentic AI, and management ownership of AI risk compared with independent assurance. It suits directors, executives, heads of public entities and senior AI, risk and policy professionals who must set and oversee AI strategy rather than build models themselves.

Frequently Asked Questions (FAQ):

What do delegates need before an AI strategy and governance course for boards and senior leaders?

No technical or coding background is needed. Delegates benefit from board, executive or senior advisory experience and from bringing their organisation's current AI plans, policies or risk reports, which are used in the case work and the final charter.

How does AI strategy and governance for boards differ from a general AI leadership course?

It focuses on the board's duties: setting AI ambition and risk appetite, overseeing assurance and audit, governing an AI investment portfolio and adopting a charter. A general AI leadership course centres on leading teams and using AI tools in daily management decisions.

Is ISO/IEC 42001 or the NIST AI RMF better for AI strategy and governance at board level?

Neither replaces the other. ISO/IEC 42001 sets requirements for a certifiable AI management system, while the NIST AI RMF is a voluntary framework for identifying and managing AI risk. Many organisations use the standard for structure and the framework for risk practice.

What does a delegate take back to work from AI strategy and governance training for boards?

Each delegate takes back an AI Strategy and Governance Charter for their own organisation, covering AI ambition, risk appetite, decision rights, oversight structures, assurance arrangements and portfolio measures, already challenged in a simulated board session.