Course Objective & Scope
When discovery workshops produce dozens of AI ideas, lacking a clear prioritisation method means decisions get driven by the loudest stakeholder or the newest tool. This course equips teams with a transparent methodology to compare use cases across business units using shared criteria.
Participants build a weighted scoring model covering business value, technical feasibility, data readiness, risk and time-to-adoption, and position candidates on a 2x2 matrix as quick wins, strategic bets or foundational enablers. Through hands-on workshops tailored to the organisation's own backlog, teams produce a prioritised use case portfolio and a sequenced Wave 1-2-3 roadmap ready for executive sign-off.
Who Should Attend?
- Senior and mid-level managers overseeing the AI portfolio and budget
- Digital transformation, strategy, PMO and innovation office teams
- AI Centre of Excellence (CoE) leads and product managers
- Process owners proposing initiatives for the corporate AI roadmap
Key Takeaways
- Design a multi-criteria use case scoring model aligned with corporate strategy
- Run calibration sessions to eliminate scoring bias across business units
- Segment use cases into quick wins, strategic bets and deferrals on a value-feasibility matrix
- Sequence initiatives by accounting for shared data and platform dependencies
- Deliver a balanced AI roadmap ready for executive committee approval
Curriculum
Prioritisation Criteria and Weighting
- Business value, data readiness, technical complexity, risk and change effort dimensions
- Adapting RICE and WSJF frameworks to AI initiatives
- Setting criterion weights based on strategic priorities
Scoring Rubrics and Cross-Unit Calibration
- Defining 1-5 scoring scales with objective anchor points
- Calibration techniques to counter optimism bias across departments
- Flagging high-uncertainty ideas that need discovery spikes
2x2 Value-Feasibility Matrix and Portfolio Balance
- Distinguishing quick wins, strategic bets, foundational enablers and low-value ideas
- Balancing generative AI, predictive machine learning and automation
- Applying budget, talent and capacity constraints to the matrix
Dependency Mapping and Wave Sequencing
- Mapping shared data assets, RAG platforms and integration prerequisites
- Rules for sequencing Wave 1, Wave 2 and Wave 3 initiatives
- Quarterly portfolio review and re-prioritisation cadence
Executive Readout and Decision Governance
- Building the prioritised portfolio dashboard and executive summary
- Stakeholder communication and backlog management for deferred ideas
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