| name | powerbi-forecast-trust-market |
| description | Use when designing or evaluating a Power BI forecast trust market, human override tracking, sales and finance confidence scoring, forecast accountability, and self-learning model-vs-human accuracy loops. |
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
- For each forecast version, capture:
- AI forecast
- roll forecast
- budget
- human override
- confidence percentage
- reason code
- owner or role
- timestamp
- After actuals arrive, calculate:
- absolute error
- bias
- directional accuracy
- calibration error
- trust score by role, segment, customer, product, and horizon
- 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_VersionAI_Forecast_OverrideAI_Forecast_ActualsAI_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.
