Module 1: Understanding the AI Landscape: From Automation to Agentic Systems

• Importance of understanding the AI landscape for CXOs.
• Essential AI terms for CXOs.
• Key technology differences in business impact and leadership decisions.
• Business model shifts driven by AI.

Module 2: Evaluating AI Adoption: Pilots, Scale, and Enterprise Value

• Importance of AI adoption and scale.
• Why AI pilots stall before scaling.
• Core terms and definitions.
• From experimentation to enterprise value.

Module 3: Defining AI Ambition: Efficiency, Growth, and Reinvention

• Setting the right AI ambition for enterprise leaders.
• Core terms and definitions.
• What each ambition means for the enterprise.
• Where AI delivers impact.

Module 4: Structuring the AI Portfolio: Quick Wins, Foundations, and Moonshots

• Importance of a balanced AI portfolio.
• Definitions and purposes.
• Characteristics, benefits, and risks.
• Value, feasibility, risk, and readiness.

Module 5: CEO Leadership in the AI Era: Vision, Sponsorship, and Enterprise Alignment

• Importance of CEO leadership in AI.
• Core terms and definitions.
• CEO roles and responsibilities in AI transformation.
• CEO sponsorship patterns in enterprise AI transformation.

Module 6: CEO Decision Framework: Asking the Right AI Questions

• Importance of disciplined CEO questioning.
• Core CEO questions for AI leadership.
• How CEO questions shape executive action.
• Real-world impact on enterprise AI.

Module 7: AI Value and Investment Discipline: The CFO's Perspective

• Importance of CFO leadership in AI investment.
• Core terms and definitions.
• Unique nature of AI investment.
• Three-part AI investment thesis.

Module 8: Finance Function Transformation: Use Cases and Cost Governanc

• How AI is reshaping finance.
• Key AI use cases in finance.
• Value and risks of AI use cases.
• AI cost governance and unit economics.

Module 9: Technology Foundations: Data Readiness and Platform Strategy

• Data and platform strategy for enterprise AI.
• Core terms and definitions.
• Five pillars of data readiness.
• Assessing the organization’s foundation.

Module 10: Operationalising AI: From Prototype to Production at Scale

• Prototype to production as the test of enterprise AI.
• Core terms and definitions.
• Five pillars of production-ready AI.
• Choosing the right AI operating model.

Module 11: AI for Growth: Customer, Brand, and Revenue Transformation

• AI-driven growth for CXOs and growth leaders.
• Core terms and definitions.
• AI opportunities and risks in marketing and sales.
• Hyper-personalisation and revenue intelligence.

Module 12: Responsible AI in Marketing: Brand Safety and Governance

• Brand safety and governance in AI-driven marketing.
• Core terms and definitions.
• Brand and trust risks introduced by AI.
• AI brand governance scenarios.

Module 13: AI in Operations: Productivity, Visibility, and Resilience

• AI-driven operations as a strategic imperative.
• Core terms and definitions.
• AI applications in operations and supply chain.
• Predictive and prescriptive operations.

Module 14: Resilience and Human-in-the-Loop Operations

• Resilience and human-in-the-loop operations.
• Core terms and definitions.
• Operational resilience and human-in-the-loop design.
• Source-derived real-world scenarios.

Module 15: Workforce Transformation: Automate, Augment, Elevate

• Workforce transformation importance.
• Core terms and definitions.
• Automate, Augment, Elevate framework.
• Workforce redesign in action.

Module 16: Ethics and Change Management in People Processes

• Ethics and trust in AI-enabled people processes.
• Core terms and definitions.
• Ethical risks in AI-driven people processes.
• Responsible AI in HR.

Module 17: Enterprise AI Governance: Risk, Ethics, and Responsible Leadership

• Enterprise AI governance for CXO leadership.
• Key AI risk categories.
• How AI risks appear in the enterprise.
• Moving from policy to practice.

Module 18: Third-Party, Vendor, and Lifecycle Risk Management

• Vendor and lifecycle risk as boardroom issues.
• Core terms and definitions.
• Main vendor and lifecycle risk categories.
• Vendor and lifecycle governance scenarios.

Module 19: AI Regulation and Board Accountability

• Regulatory readiness.
• Core terms and definitions.
• Main elements of risk-based AI regulation.
• Regulatory readiness and board engagement.

Module 20: Audit, Controls, and Board Reporting for AI

• Auditability and board reporting.
• Core terms and definitions.
• Audit and controls in enterprise AI.
• Board-ready AI reporting practices.

Module 21: Bridging the Pilot-to-Scale Gap: Operating Models for Enterprise AI

• Pilot-to-scale gap in enterprise AI.
• Core concepts for scaling AI.
• Barriers that stop pilots from scaling.
• Crossing the pilot-to-scale gap.

Module 22: Enterprise AI Operating Models and KPI Dashboards

• Scalable operating models.
• Core terms and definitions.
• Main enterprise AI operating models.
• Operating models in action.

Module 23: Role-Based Action Plans: The First 90 Days for CXOs

• Importance of focused 90-day AI action plans.
• Key elements of a CXO AI action plan.
• How each CXO role shapes the first 90 days.
• Real-world examples.

Module 24: Final Assessment

• Review key course concepts.
• Apply learning through assessment questions.
• Validate readiness for practical us