Measuring AI Value & Return on Investment
Overview
After the first AI projects go live, leaders want to know whether they are paying off. This course shows how to define, measure and report the value of AI: setting baselines, choosing the right indicators, attributing benefits fairly and using results to decide what to scale, fix or stop.
Who should attend
Finance and performance managers, PMO staff, AI programme leads, and managers accountable for AI budgets.
Training methodology
Facilitated discussion, practical frameworks, case studies from different sectors, group exercises, short demonstrations of AI tools, and an action plan for each participant's organisation.
Objectives
By the end of this course, participants will be able to:
- Define value dimensions for AI initiatives
- Set baselines and measurement plans
- Calculate ROI and total cost of ownership
- Track adoption, quality and risk indicators
- Report results and guide portfolio decisions
Course outline
Day 1What Value Means
- Financial and non-financial value
- Value chains for AI
- Common measurement mistakes
- Value mapping exercise
- Stakeholders and their value expectations
- Value hypotheses
Day 2Baselines and Indicators
- Establishing baselines
- Leading and lagging indicators
- Adoption and quality metrics
- Designing a measurement plan
- Data collection methods
- Control groups and comparisons
Day 3Costs and ROI
- Total cost of ownership
- ROI, payback and NPV
- Attributing benefits
- Worked example
- Hidden and ongoing costs
- Benefit realisation tracking
Day 4Risk and Quality Monitoring
- Model performance and drift
- Error and incident tracking
- Cost of failure
- Balanced AI scorecard
- User satisfaction measures
- Exercise: building the scorecard
Day 5Reporting and Decisions
- Value dashboards
- Scale, fix or stop decisions
- Communicating results
- Personal measurement plan
- Portfolio review meetings
- Lessons for future investments
