Day 1: Designing Decision-Oriented AI Dashboards

Decision Questions and Users

• Evidence requirements for decision questions
• Validation checks prior to using results
• Outliers reviewed before model use
• Data quality prior to analytical interpretation

Forecast Metrics and Drivers

• Decision points for forecast metrics
• Uncertainty boundaries around analytical conclusions
• Query logic checked against requirements
• Missing values needing deliberate treatment

Visual Hierarchy and Context

• Validation checks for visual hierarchy
• Assumptions tested before final conclusions
• Business question tied to decisions
• Data lineage informing result traceability

Day 2: Integrating Predictive Insight into Dashboards

Forecast Ranges and Scenarios

• Validation checks for forecast ranges
• Explanation boundaries communicated to decision-makers
• Common definitions for core measures
• Visual outputs supporting decision clarity

Anomaly and Early Warning Views

• Review criteria for early warning
• Failure patterns requiring deeper investigation
• Leading indicators of process drift
• Quality measures linked to outcomes

Narrative Insight Generation

• Decision points for narrative insight generation
• Corrective actions tied to evidence
• Process variation before customer impact
• Verification checks prior to final release

Day 3: Validating and Operating Analytical Dashboards

Data and Forecast Quality

• Operational risks around forecast quality
• Sampling evidence informing quality decisions
• Defect patterns requiring root-cause analysis
• Prevention actions after recurring defects

Alert Review and Action

• Performance signals around alert review
• Measurable acceptance criteria for quality
• Acceptance evidence retained for oversight
• Control limits highlighting unusual variation

AI Dashboard Governance

• Review criteria for AI dashboard governance
• Escalation paths for failed controls
• Audit evidence informing full traceability
• Review cadence for governance controls

• Group review evaluating forecast metrics against evidence
• Case analysis examining early warning in workplace decisions
• Simulation testing forecast quality under operational pressure
• Guided exercise applying AI dashboard governance to workplace cases