Programme overview
Technical depth: Conceptual · Practical mode: Case study
Introduction
Spending on AI is rising faster than the evidence that it pays back. Many organisations can list pilots, licences and usage figures, yet cannot show which initiatives moved revenue, cost, risk or service quality, or whether the savings claimed ever reached the budget. This Core Concept course gives executives a disciplined way to define, measure and govern the value of AI investments, drawing on benefits realisation practice, financial appraisal and experimental evaluation. Participants leave with an AI Value Realisation Framework for their own AI portfolio.
Course Objectives
- Define the value an AI initiative is expected to create as testable hypotheses linked to strategic objectives
- Evaluate AI business cases using total cost of ownership, NPV, payback and risk-adjusted benefit estimates
- Set baselines, leading and lagging indicators and attribution methods that separate AI impact from other causes
- Govern an AI portfolio through stage gates, benefits reviews and stop-or-scale decisions
- Challenge overstated productivity claims and hidden run costs in AI proposals and vendor reports
- Approve an AI Value Realisation Framework that assigns owners, measures and review dates to each benefit
Target Audience
- Executives who sponsor AI and digital transformation programmes and are accountable for their returns
- Finance executives who approve AI investment cases and track benefits against budgets
- Strategy and transformation executives who prioritise the AI portfolio across business units
- Chief data, digital and technology executives who report AI performance to the board
- Business unit heads whose targets depend on AI-enabled processes and services
Course Outline
Day 1: The AI Value Problem and Current Portfolio
- AI Value Categories: Efficiency, Growth, Risk Reduction and Experience
- Why AI Pilots Fail to Scale: Evidence From Portfolio Reviews
- Productivity Gain Versus Realised Financial Benefit
- AI Portfolio Inventory and Value Heat Map
- Executive Questions for an AI Investment Committee
Day 2: Value and Benefits Frameworks
- Benefits Realisation Management Principles in MSP and ISO 21502
- Benefits Dependency Network: From AI Capability to Business Outcome
- Balanced Scorecard Perspectives for AI Initiatives
- ISO/IEC 38507 Governing Body Responsibilities for AI Value
- AI Value Hypothesis Statements and Measurable Outcome Definitions
Day 3: Measuring AI Value in Practice
- Baseline Setting and Benefit Profiles With Owners and Timelines
- AI Total Cost of Ownership: Build, Run, Data, Talent and Change Costs
- Token and Compute Unit Economics Under the FinOps for AI Approach
- NPV, IRR, Payback and Sensitivity Ranges for AI Business Cases
- Control Groups, A/B Tests and Difference-in-Differences for Attribution
Day 4: Value Risks, Distortions and Portfolio Decisions
- Benefit Double-Counting and Optimism Bias Checks
- Hidden Run Costs: Model Drift, Human Review and Vendor Price Changes
- Risk-Adjusted Value Using ISO/IEC 42001 Risk and Impact Assessments
- Stage-Gate Criteria for Stop, Pivot or Scale Decisions
- Board AI Value Dashboard Design and Reporting Cadence
Day 5: Case Work and the Value Realisation Framework
- Bank Customer Service Copilot Case Study: Validating Claimed Savings
- Hospital Scheduling Optimisation Case Study: Attributing Throughput Gains
- AI Portfolio Prioritisation Exercise Using Value and Feasibility Scoring
- AI Value Realisation Framework Drafting for an Own Portfolio
- Investment Committee Panel and Framework Defence
Skills You Will Gain
- AI Investment Appraisal
- Benefits Mapping
- Value Attribution
- AI Cost Modelling
- Portfolio Prioritisation
- Stage-Gate Governance
- Benefits Assurance
- Board-Level Value Reporting
Why Attend This Course
- Return to work with an AI Value Realisation Framework ready to apply to the AI initiatives already under way
- Ask the questions that expose weak AI business cases before funds are committed
- Report AI performance to the board in financial and operational terms rather than activity counts
- Test your investment judgement against executives from banking, healthcare, energy and government
Conclusion
AI earns continued investment only when its value is defined in advance, measured honestly and reviewed until it is realised. This course moves from the reasons AI portfolios struggle to show returns, through benefits realisation frameworks and financial appraisal, to attribution methods, cost distortions and stage-gate decisions. The final day produces an AI Value Realisation Framework that executives can take back to their investment committee, giving them a consistent basis for deciding which AI initiatives to scale, fix or stop.