Powerbi Forecast Trust Market

Power BI Forecast Trust Market

Use this skill when the user wants the forecast to learn from human judgment, overrides, and organizational accuracy over time.

Concept

Every forecast can receive confidence signals from AI, Sales, Finance, Supply Chain, and Management. Later actuals determine which source was calibrated and which source was biased.

Workflow

1. For each forecast version, capture:
   • AI forecast
   • roll forecast
   • budget
   • human override
   • confidence percentage
   • reason code
   • owner or role
   • timestamp
2. After actuals arrive, calculate:
   • absolute error
   • bias
   • directional accuracy
   • calibration error
   • trust score by role, segment, customer, product, and horizon
3. Feed the trust score back into the next forecast cycle:
   • high-trust human input can challenge model output
   • low-trust input remains visible but gets lower weight
   • biased sources trigger review rather than automatic blending

Suggested tables

• AI_Forecast_Version
• AI_Forecast_Override
• AI_Forecast_Actuals
• AI_Forecast_TrustScore

Required metrics

• WAPE
• bias
• hit rate within tolerance
• override lift versus AI baseline
• override lift versus roll forecast
• confidence calibration

Guardrails

• Do not turn trust scores into personal blame. Frame them as calibration by source and context.
• Preserve the original AI forecast and human override separately.
• Require reason codes for overrides that materially change the forecast.