pbi-ai-readiness

You can check Power BI AI readiness by scanning semantic models for complete descriptions, consistent naming, valid relationships, and clean DAX measures to generate a quantifiable readiness score. This skill evaluates those four dimensions to guide targeted improvements for AI-assisted analysis.

Power BI model AI readiness requires meaningful table and column descriptions, consistent naming conventions, complete relationships, and clean measure syntax. These elements allow AI copilots and Q&A features to accurately interpret the semantic model and generate reliable automated insights.

Scoring Power BI model health for AI insights involves evaluating descriptions, naming, relationships, and measure syntax across defined maximums of 30, 25, 25, and 20 points. This multi-module framework outputs an actionable readiness score with specific recommendations for remediation.

Yes, the AI readiness assessment supports integration with pbi-tmdl-analysis and pbi-model-health tools. It complements these existing Power BI model health utilities by adding a dedicated readiness scoring layer focused on preparing semantic models for AI-assisted analysis.

The best way to prepare a Power BI semantic model for an AI readiness scan is ensuring your tables, columns, and measures have descriptions, consistent naming, and established relationships. The scan requires a frontmatter with name and description to evaluate these model properties.

AI copilots struggle with Power BI semantic models when they lack descriptions, consistent naming, complete relationships, and clean DAX measures. Running an AI readiness scan identifies specific deficiencies in these areas and provides actionable recommendations to uplift model quality.