Day 1: Framing Decisions and Assessing AI Readiness

AI Value for Organizational Decisions

• Identify decisions benefiting from AI
• Separate speed from decision quality
• Define accountability for assisted decisions
• Set measures for decision improvement

AI Decision Data Readiness

• Assess people processes and technology
• Compare readiness with operating needs
• Identify capabilities requiring management attention
• Assign owners for readiness improvements

Selecting Decision Problems for AI

• Identify pain points for improvement
• Define value for each use-case
• Verify data availability and readiness
• Build shortlist for pilot selection

Day 2: Designing AI Use Cases, Models, and Inputs

Designing AI Decision Use Cases

• Frame problem before selecting technology
• Define users decisions and outcomes
• Assess feasibility using data constraints
• Prioritize use-cases by measurable value

AI Decision Model and Tool Selection

• Define criteria before evaluating options
• Check alternatives against operating constraints
• Balance flexibility control and support
• Compare options and justify selection

AI Decision Data and Input Preparation

• Define minimum data requirements clearly
• Assess reliability across data sources
• Standardize fields required for analysis
• Prepare concise data-quality summary

Day 3: Validating Outputs and Embedding AI in Decisions

AI Decision Output Validation

• Check outputs against original requirements
• Verify critical facts using sources
• Identify errors and unsupported claims
• Approve results after human review

AI Decision Operational Integration

• Identify process steps affected
• Link outputs to downstream decisions
• Design exception and failure handling
• Test integrated workflow end-to-end

AI Decision Human Oversight

• Identify decisions requiring human judgment
• Define handoffs between people systems
• Prevent overreliance on automated recommendations
• Document effective human system interactions

Day 4: Governing Ethics, Security, and Decision Value

AI Decision Ethics Bias and Transparency

• Identify ethical risks in use-cases
• Test outputs for unfair patterns
• Explain system limitations to users
• Set reviews for high-impact decisions

AI Decision Data Privacy and Security

• Identify personal and confidential data
• Minimize data collection to necessity
• Select secure authorized sharing channels
• Apply privacy checklist to scenario

Measuring Decision Impact and Value

• Link metrics to management questions
• Verify definition and source consistency
• Compare performance against relevant baseline
• Convert results into follow-up actions

Day 5: Building Trust, Piloting, and Responsible Scale

AI Decision Adoption and Trust

• Identify groups affected by adoption
• Explain adoption purpose by role
• Address adoption barriers with support
• Use feedback to reinforce adoption

AI Decision Pilot and Responsible Scale

• Select low risk pilot area
• Record baseline performance before intervention
• Monitor intended and unintended effects
• Approve scaling after evidence review

AI Decision-Making Roadmap

• Build AI decision implementation roadmap
• Prioritize roadmap actions by value
• Identify dependencies requiring early resolution
• Set measures tracking roadmap progress

• Case files examining AI value in business and government decisions through realistic workplace decisions
• Hands-on workshops preparing data and developing inputs from realistic case material
• Simulations requiring judgment about data security and usage privacy under constraints
• An end-of-course project producing an AI decision roadmap with governance and review points