As data volumes grow exponentially, Power BI customers demand AI-driven analytical solutions that scale to petabytes, are secure, governed, and easy to manage. Power BI semantic models, backed by Microsoft Fabric, are the foundation for user-friendly BI solutions, bridging the gap between business users and IT by enabling trusted ad-hoc analysis. Consequently, Power BI semantic models serve as key enablers of AI-powered insights, ensuring consistent answers to data questions across large organizations and maximizing return on existing Power BI investments.
Ubiquitous in enterprise BI
Power BI semantic models are the authoritative source of truth for ad-hoc analysis and reporting across countless organizations, empowering over 35 million monthly active users and 95% of Fortune 500 companies. Microsoft is the only vendor to be recognized as a Leader in the Gartner® Magic Quadrant™ for Analytics and BI Platforms every year since 2008, positioned as a leader in The Forrester Wave™: Business Intelligence Platforms, Q2 2025, and GigaOm Radar for Semantic Layers and Metrics Stores. Based on the most widely adopted BI semantic modeling technology in the market, Power BI semantic models offer a mature API surface area, exceptional performance and scalability for interactive analysis over massive datasets, and a thriving ecosystem of developers, partners, and third-party tools.
Bridge the gap between the business and IT
Power BI semantic models are uniquely positioned between business users of Microsoft M365 and enterprise data assets in Microsoft Fabric created by professional developers and BI professionals. You can have the most sophisticated data-foundation on the planet, but if your business users aren’t using it because it’s not integrated with the tools they use on a daily basis, it’s not end-user oriented, or they don’t trust it, then it’s pointless. This is the core value proposition of Power BI semantic models – to bridge the gap between business users and enterprise data assets.
Enable both centralized and self-service BI
Organizations that successfully drive a data culture tend to embrace “discipline at the core and flexibility at the edge”. Power BI semantic models support both centralized and self-service BI use cases. Customers get to choose business-critical data entities requiring organizational discipline and centralized management. Business analysts can easily combine that data with other data from a wide range of sources – without a dependency on IT, duplicating corporate data, or violating governance controls. This represents value for customers because it reduces total cost of ownership through standardization and facilitates collaboration between the business and IT like ownership takeover.
Commitment to open standards/platform
With Direct Lake storage mode, Power BI semantic models are a key part of Microsoft Fabric’s commitment to open standard formats using Delta Lake and OneLake, enabling customers to avoid lock-in to proprietary vendor storage formats. Power BI semantic models enable open-platform connectivity for a wide range of non-Microsoft data visualization tools through compatibility with the XMLA Endpoint, allowing end users to perform ad-hoc analysis from their tool of choice without needing to create local copies of data. Power BI semantic models are highly programmable using Fabric REST endpoints for CRUD operations, query execution, refresh management, Python notebook support with semantic link, .NET programmability using the Tabular Object Model (TOM), and open metadata definitions using the Tabular Model Definition Language (TMDL) for Git-friendly file formats and scripting automation.
Ad-hoc analysis is the sweet spot for Power BI semantic models
Enterprise semantic models have always been about enabling non-technical users to ask ad-hoc data questions using business terminology spanning broad subject areas. They deliver trusted insights based on dynamically calculated metrics and dimensionality. Highly curated semantic models surface corporate data using business-friendly naming conventions, abstracting complex business logic for sophisticated calculations, inferred relationships, user hierarchies, metadata translations and much more. With blazing-fast query performance, users are empowered to slice and dice massive datasets at the speed of thought. Together, these capabilities foster trust and drive consistent decisions across the organization.
The DAX query language used by semantic models is purpose built for ad-hoc analysis. DAX queries are autogenerated based on end-user interactions, so are context aware and designed to reuse pre-defined definitions in the semantic model. DAX calculations contained within the semantic model enable advanced, aggregated, BI-style calculations that can prove very difficult to define in SQL for scenarios like time intelligence, period-to period comparisons, ratio and contribution analysis, financial and inventory calculations.
Contrast with solutions that lack a semantic layer
Reporting solutions without a robust semantic layer based on mature BI engine technology often require business logic to be defined on the fly for analytical queries, leading to inconsistent results. AI-based consumption is less deterministic. Query performance is slower because data structures, query plans and query-processing engines are not well optimized for BI-style queries. Lack of presence in tools business users use every day like Microsoft M365 – coupled with the inability for business users to combine data from other data sources without a dependency on IT – leads to ungoverned “shadow IT”, data silos and data fragmentation, which are poisonous to organizations seeking to embrace a data culture.
Power BI semantic models are accelerators for AI-enabled consumption
The strong foundation in ad-hoc, curated analysis using business-friendly terminology is precisely why Power BI semantic models are key accelerators for AI-based consumption. Now it’s just that the AI is asking the ad-hoc data question on behalf of the user. Natural language data questions are answered more consistently and easily when the AI can rely on business logic encapsulated by semantic models. LLMs are already familiar with well-defined API surface areas for metadata and queries enabling agentic modeling and consumption. Power BI semantic models are already deployed at scale across countless large organizations and primed to accelerate AI-driven consumption of enterprise data.
Try Power BI Copilot today!
Try the standalone Power BI Copilot experience on existing Power BI semantic models and reports today! Educate Copilot by providing AI instructions and Verified Answers for known business terms and common questions. Empower business users to access the most relevant data assets in Fabric for AI-driven insights, using Copilot discovery based on sophisticated relevance signals. Enable Copilot to ask ad-hoc analysis questions on behalf of business users based on dynamically calculated, pre-defined definitions in your semantic models, and leverage the richness and interactivity of Power BI visuals without leaving the chat experience!
Refer to the Overview of Copilot for Power BI documentation to get started.
Here is a short demo of Power BI Copilot integrated with Power BI Apps for a scoped discovery experience by subject area. Rich visual interactivity and slice/dice capabilities are demonstrated without leaving the chat experience!
