Fabric Semantic Models are key for good math with AI Fabric Data Agents and Power BI Copilot
Introductory and overview
We are currently experiencing a generational surge in AI use cases and transformation. But will AI be able to connect directly to your raw data, perform all the necessary transformations, apply business-friendly names to fields, and add accurate logic to solutions? Does data engineering and architecture still matter?
A well-known limitation of AI—specifically large language models (LLMs)—is that they are not fundamentally designed to perform accurate math. While newer LLMs can handle some mathematical tasks, query speeds are often slow, and the compute costs can be high. Translating the specialized context of natural language questions into precise logic also presents challenges. For example, if a business user asks, “Show me total sales for the year,” what exactly does “year” mean? Is it a calendar year, a fiscal year, or year-to-date? Now imagine how much more complex the math becomes with a question like, “Show average sales for blue and red widgets for customers in the East, excluding store holidays.”
Traditional best practices known by data professionals for decades provide a solid foundation for accurate math in AI-driven applications. These practices will continue to evolve as we design data architectures optimized for AI. Microsoft Fabric semantic models are a powerful tool for building that logic in a way that provides both context for accurate calculations and fast, efficient query performance. If you’re a data professional with skills in dimensional modeling, query optimization, ETL/ELT, RLS/OLS—your expertise is now more crucial than ever for AI solutions that require “good math.”
This presentation will explore strategic reasons for using semantic models as the foundation for AI when querying structured data. We’ll walk through a use case that begins with 275 million rows of raw data, demonstrates how to model the data for AI, leverages tools in Fabric semantic models to prepare the data, and then serves it to AI tools and agents using Fabric Data Agents, Power BI Copilot, Azure AI Foundry, and Microsoft 365 Copilot.
A well-known limitation of AI—specifically large language models (LLMs)—is that they are not fundamentally designed to perform accurate math. While newer LLMs can handle some mathematical tasks, query speeds are often slow, and the compute costs can be high. Translating the specialized context of natural language questions into precise logic also presents challenges. For example, if a business user asks, “Show me total sales for the year,” what exactly does “year” mean? Is it a calendar year, a fiscal year, or year-to-date? Now imagine how much more complex the math becomes with a question like, “Show average sales for blue and red widgets for customers in the East, excluding store holidays.”
Traditional best practices known by data professionals for decades provide a solid foundation for accurate math in AI-driven applications. These practices will continue to evolve as we design data architectures optimized for AI. Microsoft Fabric semantic models are a powerful tool for building that logic in a way that provides both context for accurate calculations and fast, efficient query performance. If you’re a data professional with skills in dimensional modeling, query optimization, ETL/ELT, RLS/OLS—your expertise is now more crucial than ever for AI solutions that require “good math.”
This presentation will explore strategic reasons for using semantic models as the foundation for AI when querying structured data. We’ll walk through a use case that begins with 275 million rows of raw data, demonstrates how to model the data for AI, leverages tools in Fabric semantic models to prepare the data, and then serves it to AI tools and agents using Fabric Data Agents, Power BI Copilot, Azure AI Foundry, and Microsoft 365 Copilot.
Session prerequisites and resources may be available.
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Speaker
Greg Beaumont
Microsoft Data & AI Technical Specialist
Greg Beaumont is a data architect at Microsoft, where he enjoys identifying and solving complex problems backed by his experience in data architecture and a passion for innovation. Focusing on the healthcare industry, Greg works closely with customers to plan enterprise analytics strategies, evaluat...
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