# Powerbi Forecast Trust Market

> 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.

- Skill: `rweisssieker-xp/powerbi-forecast-trust-market` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add rweisssieker-xp/powerbi-forecast-trust-market`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rweisssieker-xp/powerbi-forecast-trust-market/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: rweisssieker-xp (https://skillmd.com/u/rweisssieker-xp)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rweisssieker-xp/powerbi-forecast-trust-market

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# 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.