Fabric IQ
Fabric IQ is the context layer that models your business as an ontology — entities, relationships and measures that both people and AI agents reason over as one shared meaning. We build that layer on a unified OneLake foundation, because an agent is only as good as the context it reasons from.
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What we hear from operators
The problems we solve
Agents reason from raw tables, not business meaning
Point an AI agent at raw Fabric tables and it has to guess how your business fits together — which table is the order, which field is the customer, how a plant rolls up to a region. Without a modelled ontology it guesses, and it guesses inconsistently. Fabric IQ is the layer that gives the agent your actual business meaning to reason from.
Every team defines the same metric differently
Finance, operations and supply chain each have their own definition of OEE, OTIF or margin, so an agent asked the same question by two people can return two answers. A shared ontology forces one definition of each entity and measure, which is the prerequisite for agents — and people — to agree on the number.
The data is not unified, so there is no context to model
An ontology models relationships across your business, which needs the data in one place to relate. If your sources have not met in OneLake, there is nothing coherent for Fabric IQ to sit on top of. Unify first; model the context second. The order is not optional.
Agentic ambition with no governed foundation
Leadership wants autonomous agents acting on operations. Built on a fragmented, ungoverned estate, those agents act confidently on wrong context — which is a bigger risk than the manual process they replace. Fabric IQ plus a governed foundation is what makes agentic operations safe enough to run.
Who this is for
Who fabric iq is built for
The roles that feel the problem first — and what we build for each of them.
Chief Operating Officer
The problem
Wants autonomous agents acting on operations, but the numbers still disagree across functions so no agent can be trusted to act.
What we build
A Fabric IQ ontology defining operations entities and measures once, on a governed OneLake foundation, so agents act on shared meaning.
Agents act on one agreed version of the business
Chief Data Officer
The problem
Every AI initiative rebuilds its own understanding of the business, so effort never compounds and definitions keep diverging.
What we build
A reusable ontology in Fabric IQ that analytics, operational agents and Microsoft 365 Copilot all reason from.
One modelled context reused across every AI use case
Operations Director
The problem
Operational decisions still wait on people stitching context together from separate systems by hand.
What we build
An operational loop on Fabric IQ where agents observe live signals and reason over modelled context, with approval gates on action.
Context assembled by the platform, not by hand
CIO
The problem
Pressure to deploy agentic AI, and concern about agents acting on ungoverned, fragmented data.
What we build
Fabric IQ on a governed foundation, with the ontology and action loop bounded by human approval and Purview governance.
Agentic operations on a governed foundation
Measurable outcomes
What changes after implementation
Specific shifts from delivered fabric iq work — the before, and the after.
One definition of each entity and measure across the business
OEE, OTIF and margin mean the same thing to finance, operations and every agent, so answers agree instead of contradicting.
Agent accuracy: reasoning from business meaning, not guessed table joins
Agents ground on the ontology rather than inferring structure from raw tables, so their reasoning follows how the business actually works.
Governed action in the moment, with human approval where it matters
The operational loop lets agents act on live signals within guardrails, keeping a person in control of anything that spends money or touches a customer.
Reuse across analytics, operations and Copilot
The same modelled context serves dashboards, operational agents and Microsoft 365 Copilot, so the investment compounds rather than being rebuilt per tool.
By market
Fabric IQ — market-specific pages
Each page below covers what fabric iq looks like specifically in that market — the local ERP landscape, compliance context, and the operational patterns we actually see there.
Singapore & Malaysia
United Kingdom
North America
Technology stack
Common questions
Fabric IQ — frequently asked
What is Microsoft Fabric IQ?
Fabric IQ is Microsoft's enterprise context layer — an ontology that models how your business operates so that both people and AI agents reason over the same shared meaning. It sits on top of your Fabric data and turns a data platform into an intelligence platform that agents can reason against. It is part of the wider Microsoft IQ family alongside Work IQ, Foundry IQ and Web IQ.
Is Fabric IQ generally available?
Yes. Microsoft first announced Fabric IQ at Ignite in November 2025 and made it generally available at Build in 2026, alongside the general availability of Operations Agents and Graph. The Ontology layer that grounds agents in business meaning continues to expand with further capabilities and integrations across Fabric, Foundry and Microsoft 365 Copilot.
How does Fabric IQ relate to a Fabric Data Agent?
Fabric IQ is the shared context — the ontology — and a Fabric Data Agent is one of the things that reasons over it. An agent grounded on a modelled ontology answers with your business meaning rather than guessing structure from raw tables, which makes it far more reliable. You can build data agents without Fabric IQ, but the ontology is what lets multiple agents and people agree on the same definitions.
Do we actually need Fabric IQ, or are dashboards enough?
If your goal is reports people read, a governed semantic model and Power BI may be enough. Fabric IQ earns its place when you want AI agents to reason and act over your operations, because agents need modelled business context to be trustworthy at that job. It is the foundation for agentic operations, not a reporting upgrade — so the honest answer depends on whether agents acting on your data is where you are heading.
What is an ontology in this context?
An ontology is a model of your business in terms of entities (order, customer, plant, shipment), the relationships between them, and the measures that describe them (OEE, OTIF, margin) — defined once, consistently. It is the difference between an agent seeing a pile of tables and an agent understanding that this order belongs to this customer and was made at this plant. Building it well is a business exercise as much as a technical one, because the definitions have to be owned by the business.
What are the prerequisites before we start with Fabric IQ?
A unified data foundation in OneLake and a governed semantic model. Fabric IQ reasons over a single coherent estate, so if your sources have not met and your definitions still fork by team, that is the work to do first. We often sequence it as: unify the data, govern the model, then build the ontology — trying to model context over fragmented data produces a confident but wrong intelligence layer.
Related FAQs
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