The Challenge of Ensuring High-Quality Power BI Semantic Models

What if every Power BI semantic model could be reviewed by an experienced architect in just a few minutes?

As Power BI adoption grows, so does the number of semantic models, and with it, the challenge of maintaining quality and consistency. Teams working on multiple projects often adopt different approaches to naming, design, DAX, and performance, leading to models that are harder to maintain and operate efficiently. Ensuring adherence to standards has traditionally relied on manual reviews by experienced practitioners, but this approach is time-consuming, hard to scale, and dependent on expert availability.

To address this, we explored how AI could support and automate part of the review process, not to replace expert validation, but to provide a fast, consistent, and repeatable assessment that helps teams accelerate quality assurance.

The Semantic Model Review Agent is designed to analyse Power BI semantic models, evaluate them against a predefined set of best practices, and generate an easy-to-consume assessment report.

How the Agent Works

At its core, the agent acts as a virtual reviewer.

The review process begins when the agent receives a Power BI project file (.pbip). Based on the semantic model metadata, the agent performs a structured analysis and identifies potential issues, improvement opportunities and areas that already comply with recommended practices.

The agent does not rely on a single checklist or scoring mechanism. Instead, it performs a multi-layered assessment that combines automated technical analysis, metadata inspection, design pattern validation and governance controls to build an objective view of a model’s maturity and production readiness, specifically:

• Best Practice Analyzer (BPA) execution: Runs Microsoft’s BPA to assess technical issues, anti-patterns, and optimisation opportunities across modelling, performance, DAX, naming standards and formatting. Findings are classified by severity and feed directly into the overall maturity assessment.
• Direct TMDL metadata inspection: In parallel, the agent analyses the semantic model’s TMDL definition itself, not just BPA outputs. This surfaces architectural characteristics, modelling patterns, relationship structures, measure organisation, parameter usage, and DAX decisions that standard BPA rules may not fully capture.
• BI4ALL Medallion rules validation: The model is evaluated against BI4ALL’s Medallion design and architecture standards (bronze, silver, gold). The agent automatically checks which best practices are implemented, which are missing and how this impacts the model’s overall maturity level.
• Governance review: The agent also assesses operational practices in the Power BI Service, specifically, workspace management, security administration, deployment processes and refresh ownership.

Finally, the agent consolidates all assessment findings into two main indicators:

• Model Maturity Score. The score provides an overall indication of the model’s quality and readiness for production use. The score is on a scale from 0 to 100.
• Medallion Tier. Based on the maturity score, the model is classified into one of three tiers: Bronze, Silver or Gold.

The combination of the maturity score and Medallion Tier classification provides a concise and meaningful summary of the model’s overall health.

Delivering Actionable Insights

The ultimate objective of the Semantic Model Review Agent is not simply to assess a model, but to translate technical findings into actionable insights that can drive continuous improvement.

To achieve this, the agent generates a comprehensive HTML report designed to present the assessment results in a clear, structured, and business-friendly format. The report is divided into six main sections:

• Executive Summary, providing an at-a-glance view of the model’s overall health. Here, users can immediately see the Model Maturity Score, the assigned Medallion Tier, and a summary of the most important risks and recommendations.

• Score Distribution, showing how the final maturity score was calculated across the different assessment dimensions. This allows users to understand not only the overall result, but also the areas where the model performs well and the areas where improvement opportunities exist.

• Critical Findings and High-Priority Findings. These sections provide a clear explanation of each issue, its potential impact, and the recommended corrective action. By elevating the most significant findings, the report helps teams quickly identify the issues that pose the greatest risk to maintainability, performance, scalability, or production readiness.

• Action Plan. This section transforms assessment results into a practical remediation roadmap. Recommendations are organized according to severity, impact, and implementation effort, allowing teams to distinguish between immediate improvements and longer-term initiatives.

• Medallion Journey. This section illustrates the model’s current maturity level and the requirements needed to progress toward the next tier. This visual representation helps users understand where the model stands today and also what specific improvements are required to achieve higher levels of maturity.

• Technical Appendix. This section contains the complete set of Best Practice Analyzer findings, the results of all Medallion Rules validations, and the governance assessment outcomes that contributed to the final score.

Together, these sections transform the report from a simple audit output into a decision-support tool. Rather than presenting isolated technical observations, the Semantic Model Review Agent provides a structured view of model quality, explains the significance of its findings, prioritizes improvement opportunities, and helps teams establish a clear roadmap towards more robust, scalable, and production-ready Power BI semantic models.

Business Benefits

By introducing a standardised and repeatable review process, the agent helps ensure that Power BI Semantic Models are assessed consistently, regardless of who developed them or who performs the review. This reduces variability between projects and helps establish a common quality baseline across the organisation, such as:

• Consistency across projects: Standardises the review process so models are assessed the same way regardless of who developed or reviewed them, reducing variability and establishing a common quality baseline across the organisation.
• Reduced manual effort: Automates much of the analysis work (model structure, DAX logic, best practices), freeing experienced Power BI architects to focus on higher-value architecture, design, and advisory activities.
• Improved governance and alignment: Using a single evaluation framework gives teams a shared understanding of what constitutes a well-designed, production-ready semantic model, driving adoption of best practices.
• Transparent quality communication: The maturity scoring framework and Medallion classification make it easy to communicate model quality to stakeholders, track improvements over time, and prioritise investments based on objective results.
• Scalable quality assurance: Enables the organisation to scale quality assurance activities while maintaining high standards of governance and technical excellence.

As new best practices emerge and organisational standards evolve, the agent can continue to grow through the addition of new validation rules, assessment criteria, and reporting capabilities. This ensures that the review process remains aligned with both Microsoft recommendations and BI4ALL’s evolving methodology.

By combining automation with established expertise, the agent helps organisations build more reliable, maintainable, and scalable Power BI solutions while fostering a culture of continuous improvement in analytics delivery.