Day 1: Finding Problems Worth Solving with AI

Business Pain Points and Decisions

• Performance signals around business pain points
• Agent actions logged for traceability
• Explicit inputs for automated actions
• Trigger conditions starting automated steps

Task and Process Opportunity Mapping

• Ownership expectations for process opportunity
• Decision points requiring human judgment
• Access limits for automated agents
• Stop conditions for human intervention

User and Stakeholder Needs

• Ownership expectations for stakeholder needs
• Verified outputs prior to downstream handoff
• Handoffs between systems and teams
• Exceptions preventing full workflow automation

Day 2: Evaluating Use Case Value and Feasibility

Data and Technology Readiness

• Ownership expectations for technology readiness
• Recovery paths after workflow failure
• Performance measures prior to and after
• Repetitive steps suitable for automation

Value, Risk, and Complexity

• Control boundaries around value and risk
• Service dependencies mapped prior to change
• Interface contracts between system components
• Integration failures needing fallback paths

Responsible AI Screening

• Validation checks for responsible AI screening
• Security controls embedded by design
• Trust boundaries around sensitive data
• Data flows across connected services

Day 3: Building a Prioritized AI Portfolio

Scoring and Portfolio Balance

• Practical tests for portfolio balance
• Dependencies shaping service reliability
• Capacity limits affecting system performance
• System components and integration boundaries

Pilot Selection and Sequencing

• Ownership expectations for pilot selection
• Monitoring signals after production release
• Failure recovery for high-impact components
• Workplace telemetry supporting diagnosis

AI Use Case Roadmap

• Ownership expectations for AI use
• Escalation routes for control failures
• Audit evidence supporting full traceability
• Oversight cadence for governance controls

• Decision simulation testing process opportunity in context
• Hands-on workshop applying value and risk to realistic tasks
• Peer review challenging assumptions around portfolio balance
• Practical lab developing AI use for operational use