TL;DR
Microsoft Fabric IQ is a semantic intelligence layer, generally available as of Microsoft Build 2026, that organizes Fabric data by business meaning, entities, relationships, and rules, rather than just tables and columns, so AI agents can reason about business context instead of raw data. It runs on existing Fabric capacity with no separate license, connects to OneLake data across lakehouses and semantic models, and lets business users build ontologies with no-code tools. Current limits include DirectLake models only, a dependency on data already in OneLake, and mostly manual ontology building since automated discovery is not yet available. Kanerika, a Microsoft Data and AI Solutions Partner, helps enterprises audit their semantic model estate and build the ontology foundation Fabric IQ requires.
Enterprises have spent years unifying their data estates. Platforms like Microsoft Fabric now consolidate pipelines, warehouses, and analytics into a single environment. Yet despite this progress, one gap persisted: AI could access the data, but struggled to understand what it actually meant in business terms.
Microsoft first addressed this at Ignite 2025 with the public preview of Fabric IQ, a semantic intelligence layer designed to transform unified data into unified intelligence. At Microsoft Build 2026, Fabric IQ reached general availability, marking a significant step forward for enterprises looking to put AI agents to work on real business decisions. With Fabric now adopted by more than 28,000 organizations worldwide, the timing matters.
This guide covers what Fabric IQ is, its current capabilities and limitations, and how enterprises can prepare for adoption.
What Is Microsoft Fabric IQ?
Microsoft Fabric IQ is a semantic intelligence layer within Microsoft Fabric that organizes data according to the language of your business. Not just tables and columns, but entities, relationships, rules, and objectives.
According to Microsoft’s official documentation, Fabric IQ is “a workload for unifying data sitting across OneLake, including lakehouses, eventhouses, and semantic models, and organizing it according to the language of your business. The data is then exposed to analytics, AI agents, and applications with consistent semantic meaning and context.”
Think of Fabric IQ as a metadata layer on top of your Fabric data. It defines entities and their relationships so AI can make sense of and relate the underlying data. If Fabric unifies where your data lives, Fabric IQ unifies what your data means.
Key Facts:
- Status: Generally available (announced at Microsoft Build, June 2026). The Ontology item is in public view.
- Licensing: No separate SKU. Included with existing Fabric capacity.
- Billing: Capacity meters for the Ontology item expected in the coming months.
The Problem Fabric IQ Solves
Enterprises have unified where data lives, but the meaning of that data remains fragmented. Each team has its own definitions—”customer,” “revenue,” and “active user” mean different things across departments. Business logic lives in people’s heads, scattered reports, and inconsistent semantic models.
This creates a fundamental problem for AI. As Microsoft explains, AI agents “can read the data, but they do not understand your business. Without that grounding, AI cannot reason about cascading effects, constraints, or objectives.”
The business cost is significant: conflicting definitions cause reporting inconsistencies, AI initiatives stall after pilots, and every new project must rediscover business meaning from scratch.
How Does the Fabric IQ Ontology Work?
1. Entity Types, Properties, and Relationships
An ontology in Fabric IQ is a shared vocabulary for the business, built from three pieces. Entity types stand for the things in your environment, like a Customer, a Shipment, or a Store. Properties record facts about those entities, and relationships describe how entities connect to each other.
Business experts build and evolve ontologies directly through a no-code visual canvas. Organizations already running Power BI can bootstrap the process, since Fabric IQ generates an ontology from an existing semantic model automatically instead of starting from a blank canvas.
2. Ontology vs. Semantic Model
A semantic model is technical. It holds tables, columns, and DAX measures, built to answer a question someone already knows how to ask, like what revenue was last quarter.
An ontology sits a level above that. It defines what a Customer or a Shipment represents as a business concept, independent of which table currently stores that data.
When a source system changes, the ontology definition stays put and only the underlying binding updates. That’s what lets an agent reason about a shipment three systems away, instead of stopping at whichever table it happens to query first.
3. Rules and Automated Actions
Microsoft added Rules to the Ontology item at FabCon Atlanta, live now in public preview. A rule attached to an entity can watch a live data stream and fire an action the moment a condition holds.
A Freezer entity bound to a temperature feed can trigger an alert automatically once the temperature stays above a set threshold for a set number of minutes, no separate pipeline or custom code required. Full detail sits in Microsoft’s Fabric IQ Ontology update.
4. Agent Access Through MCP
Microsoft published an MCP server endpoint for the Ontology item inside Copilot Studio. Any agent built there, or any client that speaks MCP, can query entities and relationships in the ontology directly.
The capability is still in public preview and requires a paid Fabric or Power BI Premium capacity, plus specific tenant settings turned on before it works.
5. Building an Ontology from an Existing Power BI Model
Start from an existing Power BI semantic model where one exists, since Fabric IQ generates a starting ontology from it automatically. For an ontology built from scratch, business experts define entities, properties, and relationships through the no-code canvas directly.
Every ontology carries version history and validation as it evolves, so changes go through review rather than landing silently in production.
How to Create Direct Lake Semantic Models in Power BI
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Operational Agents—Autonomous Decision-Making
Operational Agents represent a new class of enterprise AI. According to Microsoft, these agents “monitor the business in real time, reason over live conditions, evaluate trade-offs, and take actions automatically to advance business outcomes.”
Unlike traditional automation, Operational Agents are context-aware and grounded in shared business semantics.
Use Case Example: A logistics company models delivery operations in the ontology. When real-time data shows traffic congestion, the Operational Agent automatically reroutes trucks while considering SLAs, delivery priorities, and cost constraints.
Data Agents—Conversational Analytics
Data Agents (previously called AI Skills) allow users to ask questions and get answers in real time using natural language. They use data from OneLake while maintaining roles and permissions.
Graph in Microsoft Fabric
Native graph storage and compute enables multi-hop reasoning. As Microsoft notes, “Ontology declares which things connect and why. Graph in Microsoft Fabric stores and computes traversals, like ‘Find shipments exposed to risky routes and related breaches.’
Fabric IQ Public Preview: Current Capabilities and Limitations
What’s Working Well
- Power BI integration: Can span semantic models to bridge business logic
- Microsoft ecosystem integration: Works with Real-Time Intelligence, Copilot Studio, Graph, Foundry IQ, Work IQ
- Democratized access: Visual, no-code tools lower barriers for business users
- No additional licensing: Runs on existing Fabric capacity
Current Limitations
Based on Microsoft documentation and practitioner reviews
| Limitation | Business Impact |
| DirectLake models only | Import and DirectQuery semantic models not yet supported |
| OneLake dependency | Data must reside in OneLake; external data sources require migration |
| Manual ontology building | No automated discovery; requires cross-functional workshops |
| Decimal type not supported | Fabric Graph returns null values for Decimal columns |
Analyst Sentiment
Optimistic View: Constellation Research analyst Michael Ni sees benefits including “consistent semantics, fewer one-off models, less duplicated logic, and a shared decision layer that lowers downstream maintenance.”
Cautious View: HFS Research warns that “for organizations not already invested in the Fabric ecosystem, adoption of IQ will likely take longer. Key hurdles include agreeing on shared business definitions, ensuring that data permissions are carried through to AI agents, and maintaining clean and reliable real-time data feeds.”
Vendor Lock-in Concern: Moor Insights analyst Robert Kramer notes, “The more an enterprise builds on this semantic layer, the harder it becomes to move that logic elsewhere.”
Microsoft’s Unified IQ Ecosystem
Fabric IQ doesn’t operate in isolation. It’s part of Microsoft’s broader “IQ” brand:
- Fabric IQ: Semantic layer for business data in Fabric/OneLake
- Work IQ (Microsoft 365): Semantic layer for documents, emails, Teams conversations
- Foundry IQ (Microsoft AI Foundry): Managed knowledge system for grounding custom AI agents
Together, these form a shared intelligence layer spanning business data, documents, communications, and enterprise knowledge. Developers can build agents that inherit live business context without extensive prompt engineering.
Industry Use Cases
Supply Chain & Logistics
Real-time rerouting based on traffic and weather; automatic schedule adjustments considering SLAs and costs; cross-domain queries connecting shipments, routes, sensors, and breach events.
Energy & Utilities
ENMAX Power is exploring Fabric IQ “to unify transmission and distribution grid data, overcoming the limitations of traditional relational databases that silo information and complicate real-time analysis.”
Financial Services
Unified customer views across products; risk assessment with context-aware AI; regulatory compliance with consistent definitions.
Manufacturing
Connected production planning, inventory, and supplier relationships; operational agents monitoring for supply disruptions.
Preparing for Fabric IQ: A Readiness Framework
Step 1—Assess Your Semantic Model Estate
Before building ontologies, understand your existing BI landscape:
- How many Power BI semantic models exist across workspaces?
- Which models are actively used versus dormant?
- Where do definition conflicts exist?
A BI Audit helps identify high-value models that should seed the ontology and surfaces conflicts that could undermine quality.
Step 2—Evaluate OneLake Readiness
Fabric IQ requires data in OneLake. Inventory data sources outside Fabric, assess migration complexity, and plan data movement using Fabric pipelines, Dataflows, or mirroring capabilities.
Step 3—Define Core Business Entities
Start small with 5–10 critical business concepts. Workshop core entities like Customer, Product, Order, Revenue, and Risk. Document relationships, rules, and constraints. This is harder than it sounds—definition debates have derailed many data initiatives.
Step 4—Pilot with Bounded Use Case
Choose a single business domain (one supply chain route, one product line). Build the ontology, deploy a Data Agent or Operational Agent, measure outcomes, document lessons learned, then iterate and expand.
How Kanerika Accelerates Your Fabric IQ Journey
As a Microsoft Solutions Partner for Data & AI and Fabric Featured Partner exhibiting at FabCon 2026 (March 16–20 in Atlanta), Kanerika brings deep expertise in Microsoft Fabric implementations.
BI Audit & Usage Reporting
Before building ontologies, enterprises need visibility into their semantic model landscape. Kanerika’s BI Audit service provides comprehensive inventory, usage analytics, definition conflict analysis, and ontology candidate identification.
Karl—Production-Ready Data Agent for Microsoft Fabric
While Fabric IQ’s native Data Agents continue maturing in public preview, Kanerika’s Karl delivers conversational analytics capabilities today. Currently in public preview as a Microsoft Fabric workload, Karl goes GA in mid-March 2026.
Karl Capabilities:
- Natural language queries on structured data—no coding required
- Real-time data retrieval with instant visualizations
- Context-aware: Understands follow-up questions and remembers past queries
- Enterprise-ready: Role-based access, audit trails, citation-backed answers
- Multi-source integration: Works with databases, Excel, CSV, Postgres—and Fabric
Ontology Design & Implementation
Kanerika facilitates workshops to define core business entities, translates business rules into ontology constraints, and designs governance frameworks including approval workflows, version control, and Purview integration.
OneLake Migration & Data Consolidation
Using FLIP Migration Accelerators, Kanerika helps enterprises consolidate data into Fabric from Azure, on-premises, and third-party platforms.
Why Kanerika for Your Fabric IQ Journey?
Kanerika was among the first organizations worldwide to deploy Microsoft Fabric in real business environments. As a Microsoft Solutions Partner for Data & AI and Fabric Featured Partner, we’ve helped enterprises across industries build the data foundations that Fabric IQ requires.
Proven Fabric Implementation Results
Southern States Material Handling (SSMH) — Implemented a Microsoft Fabric-based Data Lakehouse unifying data across fleet management, service operations, and inventory control.
ABX Innovative Packaging Solutions — Consolidated scattered data across multiple systems into a unified framework with standardized ETL processes and custom Power BI dashboards.
“Our collaboration with Kanerika has been transformative. Their expertise has significantly enhanced our data management capabilities, empowering us to make faster, more informed decisions.
— Greg Thompson, Chief Information Officer, ABX
The semantic models you define today become the business language your AI agents speak tomorrow. Kanerika helps you build that foundation. Get started today!
Frequently Asked Questions
Is Microsoft Fabric IQ generally available?
No. Microsoft Fabric IQ is currently available in public preview, announced during Microsoft Ignite 2025. Microsoft has not shared a confirmed general availability timeline yet. Organizations can explore Fabric IQ capabilities during this public preview phase and understand how its ontology, semantic modeling, and AI context features can support future data and AI initiatives.
Does Fabric IQ require additional licensing?
No. Fabric IQ runs on existing Microsoft Fabric capacity and does not require a separate licensing SKU. Organizations can use their current Fabric environment to access Fabric IQ capabilities during the public preview period. Billing meters for the Ontology item are expected to be introduced in H1 2026 as Microsoft continues expanding Fabric IQ features and availability.
Can I use existing Power BI models with Fabric IQ?
Yes, but with certain limitations. Fabric IQ currently provides full support for semantic models that use tables in DirectLake mode for data bindings. Import and DirectQuery-based semantic models may require additional adjustments or have limited support depending on the use case. Organizations should review their existing Power BI models and data architecture before adopting Fabric IQ.
What's the difference between Fabric IQ and Copilot in Fabric?
Copilot in Fabric helps users work with data through AI-powered assistance, including generating queries, summaries, and insights across Fabric workloads. Fabric IQ provides the underlying business context that helps Copilot and other AI agents understand enterprise data, business terms, relationships, and key metrics. Together, they enable more accurate and context-aware AI experiences.
How does Fabric IQ compare to Palantir Foundry?
Both Microsoft Fabric IQ and Palantir Foundry use ontology-based approaches to connect enterprise data with business concepts and relationships. Fabric IQ benefits from its integration with the Microsoft ecosystem, including Fabric, OneLake, and Power BI, allowing organizations to build on existing investments. Palantir Foundry has a longer history in ontology-based operational applications and supports complex operational workflows across industries.
What data sources does Fabric IQ support?
Fabric IQ builds on data available within the Microsoft Fabric ecosystem, including Lakehouse tables, Eventhouse streams, and existing semantic models stored within OneLake. Organizations can use these connected data assets to create business context and relationships for analytics and AI applications. External data sources need to be brought into the Fabric environment before they can be used with Fabric IQ.
What's required before implementing Fabric IQ?
Before implementing Fabric IQ, organizations need a strong data foundation and clear business alignment. This includes consolidating trusted data sources in OneLake, reviewing existing semantic models, and defining important business entities, metrics, and relationships. A governance framework is also important to ensure the ontology remains accurate, consistent, and aligned with changing business requirements.
What is Karl and how does it relate to Fabric IQ?
Karl is Kanerika’s Data Intelligence Agent available as a Microsoft Fabric workload. It helps users interact with enterprise data through conversational analytics by understanding business questions and generating insights from trusted data sources. While Fabric IQ provides the semantic foundation for AI-driven experiences, Karl offers a production-ready approach for organizations looking to implement AI-powered data intelligence capabilities.
Which industries benefit most from Fabric IQ?
Industries with complex operations and large volumes of enterprise data can benefit significantly from Fabric IQ. Sectors such as supply chain, logistics, manufacturing, energy, financial services, and retail often require accurate business context to support faster decisions. By connecting data with business meaning, Fabric IQ helps organizations build more reliable analytics and AI experiences across operational use cases.
When is FabCon 2026?
FabCon + SQLCon are together March 16–20, 2026, in Atlanta, GA.Kanerika will be at booth 836 at the event.
