Day 1: Decision Framing and Input Preparation

Framing the Decision Problem

• Specific decision question to resolve
• Problem boundaries before analysis
• Stakeholders affected by decision outcome
• Assumptions requiring independent testing

Decision Criteria and Weighted Scoring

• Criteria linked to decision objective
• Weights reflecting criteria priority
• Common scale for option scoring
• Ranking sensitivity to weight changes

Preparing Decision Inputs

• Traceable sources for decision data
• Facts separated from assumptions
• Data gaps affecting result
• Quality limitations requiring documentation

Day 2: Scenario Modeling and Sensitivity Analysis

Prompting AI for Decision Analysis

• Context explaining decision problem
• Clear criteria for requested comparison
• Constraints preventing generic answers
• Prompt exposing assumptions and rationale

Scenario Modeling and Sensitivity Analysis

• Baseline scenario for comparison
• Variables changing possible outcomes
• Different assumptions for each scenario
• Sensitivity analysis for critical variables

Comparing Executive Alternatives

• Benefits and limits of alternatives
• Trade-offs between cost and risk
• Weighted result by criteria
• Qualitative factors requiring judgment

Day 3: Validation Governance and Recommendation

Independent Validation of AI Outputs

• Facts requiring independent verification
• Contradictions requiring independent re-analysis
• Model limitations before reliance
• Conclusions linked to clear evidence

Bias Governance and Human Accountability

• Potential bias in input data
• Human accountability for decision
• Sensitive data requiring protection
• Audit trail explaining selection logic

Formulating the Executive Recommendation

• Clearly supported executive recommendation
• Rationale linked to decision criteria
• Residual risks requiring follow-up
• Next steps and defined ownership

• Decision-problem and weighted-criteria framing lab
• Exercise preparing traceable inputs and separating facts from assumptions
• AI-assisted scenario generation and alternative comparison
• Sensitivity testing for changes in weights and assumptions