pbi-model-health

Power BI semantic model readiness for Copilot is determined by evaluating metadata coverage, naming quality, relationship completeness, and DAX syntax. This generates a composite AI readiness score with per-dimension findings and actionable recommendations to improve Copilot and Q&A reliability.

AI readiness scoring for Power BI semantic models evaluates description coverage, naming quality, relationship completeness, and DAX measure syntax. It outputs a composite score with structured findings and actionable recommendations to ensure metadata quality drives reliable AI insights.

Fix DAX syntax and metadata issues by running an AI readiness assessment that identifies specific gaps in measure syntax, description coverage, and naming quality. The output provides actionable remediation recommendations across four dimensions to improve Q&A accuracy and Copilot reliability.

Copilot in Power BI requires complete metadata and relationship definitions to generate reliable insights. Incomplete description coverage, poor naming quality, or missing relationships lower the AI readiness score and degrade the accuracy of Copilot and Q&A responses.

AI readiness scoring integrates with existing Power BI schema analysis pipelines like pbi-tmdl-analysis and pbi-business-qa. This integration grounds readiness findings in detailed schema analysis and Q&A evaluation to provide a comprehensive assessment of your semantic model.

Automated Power BI model health assessments are limited to evaluating metadata coverage, naming quality, relationship completeness, and DAX measure syntax. They focus strictly on AI readiness scoring and do not validate underlying data accuracy, refresh logic, or report-level performance.