Structured across seven core modules and an advanced track — from foundational literacy through to strategic execution, governance and the economics of AI investment. Every module is built around the decisions executives actually face, not the concepts technologists find interesting.
A comprehensive AI leadership curriculum.
The Programme
Construct a business case that survives rigorous board scrutiny, budget with realistic assumptions, and manage performance with transparency that builds confidence.
On completion
The three-section performance dashboard, how to report when things go wrong, and stakeholder communication.
Managing Investment Performance & Reporting
8.4
Five business-case failure modes, the six-component business-case framework, and five financial-modelling principles.
Building the AI Business Case
8.1
For executives ready to go deeper on the financial and commercial dimensions of AI leadership. Builds directly on Modules 1–7.
AI Economics: Business Case, Budgets & Board Approval
08
The six-category cost landscape, the TCO multiplier, five funding models, and the Build vs Buy financial comparison.
AI Budgeting: What Things Actually Cost
8.2
Five board-member archetypes, six sequencing principles, and the five most common board objections with effective responses.
Securing Board & Executive Approval
8.3
Module 8 of 8
Advanced Track
Maintain strategic awareness without being overwhelmed, design workflows that preserve accountability, and lead with the integrity that earns lasting credibility.
On completion
Genuine thought leadership vs hype, the six-channel leadership platform, and five integrity commitments — the capstone.
Becoming a Trusted AI Evangelist & Change Agent
7.4
The three-layer AI intelligence system, a personal AI toolkit, and a sustainable 30-minute weekly practice.
Staying Informed: Tools, Trends & Thought Leadership
7.1
Build the habits, networks and strategic awareness to stay current and lead confidently as AI continues to evolve.
Staying Ahead as an AI Leader
Module 7 of 8
07
Near-term commercial reality separated from long-term speculation across three convergence domains.
AI & Emerging Tech Convergence: IoT, Quantum & Robotics
7.2
Five work categories, the six-step collaboration design framework, and the automation-bias warning.
The Future of Work & Human-AI Collaboration
7.3
Move investments from pilot to production with rigour, design a CoE with appropriate mandate, and build a vendor approach that protects your strategic position.
On completion
Seven scaling barriers with executive responses, the four-phase scaling roadmap, and the executive governance cadence.
Scaling AI Across Business Units
6.4
The seven-stage lifecycle, the reality of PoC purgatory, and the Production Commitment Test before any pilot begins.
AI Project Lifecycle: From Pilot to Production
6.1
Move from pilot to production — structuring AI programmes, selecting partners and scaling across the enterprise.
Execution & Scale
Module 6 of 8
06
Five CoE responsibilities, the Hub & Spoke model, and cross-functional squad design.
AI Centres of Excellence & Cross-Functional Teams
6.2
Six vendor categories, the seven-dimension evaluation framework, and the reference-call protocol.
Choosing AI Partners, Platforms & Vendors
6.3
Lead the workforce transition with honesty, design a capability programme fit for your scale, and communicate in a way that builds trust rather than anxiety.
On completion
All three audiences in depth, mapped to disclosure requirements under the EU AI Act and GDPR.
Communicating AI to Boards, Employees & Customers
5.4
Why AI change is genuinely different, the four-phase change framework, and how to have the job-displacement conversation honestly.
Change Management in an AI-Enabled Enterprise
5.1
Drive the human side of AI transformation — change management, reskilling, culture and executive communication.
Empowering People & Culture
Module 5 of 8
05
The five-layer AI skills landscape, six learning-programme principles, and the reskilling roadmap for the roles most affected.
Upskilling & Reskilling for AI Readiness
5.2
The four cultural conditions for AI innovation, five leadership behaviours, and a measurable culture scorecard.
Creating a Culture of Innovation & Experimentation
5.3
Lead with personal credibility, design a governance framework that satisfies board and regulatory scrutiny, and make confident build-vs-buy decisions.
On completion
The four-domain governance framework, the AI Risk Register across six categories, and the EU AI Act classification — with personal executive accountability made explicit.
AI Governance, Risk Register & Compliance
4.4
Six behavioural shifts that distinguish effective AI leaders, the Demo Trap warning, and four personal leadership habits.
AI Leadership Mindset: From Control to Collaboration
4.1
Lead the organisational, operational and governance dimensions of enterprise AI transformation.
Leading AI Transformation
Module 4 of 8
04
People, process and technology; five design principles; six key roles; and the 1:2:0.5 hiring-ratio rule.
Designing the AI Operating Model
4.2
The six-option Build vs Buy spectrum, vendor and partner strategy, and the five-layer internal talent strategy.
Building or Buying AI Capabilities
4.3
Prioritise use cases with confidence, construct a credible business case, and present AI's strategic value to your board in terms that connect to competitive position.
On completion
Why traditional ROI fails for AI, the three-horizon value measurement framework, and the KPI replacement table.
Rethinking KPIs & ROI in the Age of AI
3.4
Three strategic positions, the AI Opportunity Matrix (Exploit Now / Build Towards / Quick Wins / Deprioritise), and four strategic risks.
AI as a Strategic Lever: Opportunities & Risks
3.1
Apply AI as a strategic lever — identifying use cases, building business cases and rethinking value chains and KPIs.
Strategic Thinking in the AI Era
Module 3 of 8
03
A function-by-function scan, the Value-Readiness Matrix, and the three-gate Use Case Validation Test.
Identifying AI Use Cases for Business Value
3.2
Five structural business-model shifts and the Porter value chain walked activity by activity, with the executive implication at each.
AI-Driven Business Models & Value Chains
3.3
Hold a credible conversation with vendors or technical teams, identify governance gaps before they become incidents, and decide grounded in technical reality.
On completion
Six responsible-AI principles, six failure modes and their controls, the four-domain governance framework, and the EU AI Act four-tier risk classification.
Responsible AI: Ethics, Bias & Governance
2.4
Data types and quality dimensions, algorithm families, the full model lifecycle, and the 80/20 budget rule.
Data, Algorithms & Models
2.1
Develop the conceptual fluency to evaluate AI proposals, challenge vendor claims and govern deployments responsibly — without a technical background.
Building AI Literacy
Module 2 of 8
02
Eight technology types with maturity ratings, a generative-AI deep dive, and the Executive AI Technology Radar.
Types of AI: ML, Generative AI, NLP & More
2.2
Five architectural layers, the Build vs Buy vs Fine-Tune framework, and the enabling technologies (RAG, vector databases, orchestration, prompt engineering).
The AI Tech Stack
2.3
Evaluate any AI proposal with rigour, brief your board on AI's business impact, and articulate a clear case for where to invest first.
On completion
Eleven business impact areas, three P&L levers, a function-by-ROI table, and the AI Impact 4-Box Matrix.
Understanding AI & Its Business Impact
1.4
The urgency case anchored in economics. The AI Trinity (Data + Algorithms + Compute) and the Cost of Inaction across three time horizons.
The AI Revolution: Why It Matters Now
1.1
Establish a jargon-free foundation in core AI principles, common vocabulary and the executive's role in transformation.
Understanding the AI Landscape
Module 1 of 8
01
The AI Hierarchy (AI ⊃ ML ⊃ DL ⊃ GenAI), the three AI types by capability, ML vs DL, and a glossary of the twelve terms executives most commonly misuse.
What Is AI? Key Concepts & Technologies
1.2
Three roles — AI Sponsor, Governor, Champion. Three-horizon planning, Hub & Spoke vs Stage-Gate, and the AI BS Detector.
The Executive's Role in AI Transformation
1.3
Articulate the difference between managing technology and leading AI transformation — and assess honestly where you currently stand.
On completion
Aware → Literate → Strategic → Transformative: a self-assessment of where you sit and the path forward, closing with a Traditional vs AI Leader comparison across eight dimensions.
The four-stage AI journey
Framework
Certainty → probability · expertise → curiosity · planning → experimentation · ownership → orchestration · efficiency → adaptability.
Five mindset shifts
Focus
Transforming into an AI leader is not a technical journey — it is a leadership one. This pre-read sets the mindset foundation for everything that follows.
