Course Objective & Scope
AI initiatives often struggle in budget meetings because of vague benefit claims and incomplete cost estimates. This course teaches how to quantify a use case's value with defensible assumptions and test whether it can realistically be delivered.
It covers value levers and baselining, scenario and sensitivity analysis, model usage costs (tokens, inference, licences), integration and change management costs, and build/buy/partner options. Feasibility is assessed across technical, data, organisational and adoption dimensions. In the workshop, a one-page business case is prepared for a use case selected from the organisation.
Who Should Attend?
- Business unit managers contributing to AI investment decisions
- Finance, controlling and business development teams
- Digital transformation and portfolio management teams
- Product and project managers
Key Takeaways
- Define value levers and a baseline for a use case
- Estimate benefits with optimistic, expected and pessimistic scenarios
- Calculate TCO covering tokens, licences, integration and operations
- Compare build, buy and partner options against clear criteria
- Present a decision-ready, one-page business case
Curriculum
Value Levers and Baselining
- Revenue growth, cost reduction, risk reduction and experience improvement
- Measuring the current state and data sources
Benefit Estimation, Scenario and Sensitivity Analysis
- Impact of adoption rate and accuracy assumptions
- Three-scenario benefit model
- Making assumptions visible
Cost Items and TCO
- Token and inference costs, licences and infrastructure
- Integration, maintenance, monitoring and change management costs
Feasibility Assessment
- Technical, data, organisational and adoption dimensions
- User acceptance and process change needs
Build, Buy or Partner Decision
- Off-the-shelf SaaS, platform-based development and custom build
- Balancing vendor lock-in, data control and speed
One-Page Business Case
- Structure and language for decision makers
- Feedback round and revision
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