How does Microsoft’s IQ Layer (Fabric IQ, Work IQ, Copilot) actually unify data, AI,?

[erpforb2bblogs]

Q: How does Microsoft’s IQ Layer (Fabric IQ, Work IQ, Copilot) actually unify data, AI, and human insights across Microsoft 365, Dynamics 365, Azure, and Fabric—and what real business value are enterprises seeing in 2026?

I’m trying to understand how Microsoft’s so-called “IQ Layer” actually operates in real-world scenarios. How are signals from tools like Teams, Outlook, Power Platform, Dynamics 365, and Fabric combined without creating data silos, privacy risks, or performance bottlenecks? Is the intelligence truly contextual and role-based, or is it mostly surface-level automation? Also, how does Microsoft ensure data governance, security, and compliance when blending human work patterns with AI-driven insights? If you’ve implemented or evaluated Microsoft IQ Layer in production, what use cases delivered the highest ROI, and what limitations or challenges should organizations realistically expect?

[apturlov]

Hi @erpforb2bblogs, thanks for the great question. I, by all means, am not an Ontology expert, but let me give you my view on this topic.

Ontology is built on top of existing data storage technologies in Fabric - Lakehouses, Semantic Models, Eventhouses, and is the next generation of the semantic view of data that uses a business-friendly approach to describing data model via business entities instead of requiring business users to understand the complext underlaying data structures. Ontology becomes a new type of a semantically enabled data source that unifies both static (Lakehouse) and Real-time (Eventhouses) data which no other structure In Fabric can achieve. As a data source, Ontology exposes a Graph API that is built on top of the Ontology model and uses underlaying data binding for data querying. Graph Query Language is considered a more business-friendly way of querying data compared to traditional SQL or KQL.

But the strongest power of Ontology is in its integration with Fabric Data and Operation Agents that enable human language interaction with business data in a more semantically aware way compared to using data directly from the original data sources. The fact that Ontology data model combines both static and Real-time data enables Operations Agent to invoke automated data-driven actions in near-real-time mode.

There are a lot of various scenarios and use cases in which Ontology would be a more beneficial data source compared to traditional transactional and analytical DBs, but this is just my view in a nutshell.

Hope, this helps.