Semantic intelligence is a crucial component of truly understanding what knowledge means and the way it may be used.
Microsoft is now deeply integrating semantics and ontologies into its Material knowledge platform with its new Material IQ expertise that it debuted on the Microsoft Ignite convention Tuesday.
Material IQ is a semantic intelligence layer designed to handle a elementary downside with enterprise AI brokers: Effectiveness relies upon not simply on dataset dimension however on how effectively knowledge displays precise enterprise operations. The brand new expertise creates a shared semantic construction that maps datasets to real-world entities, their relationships, hierarchies, and operational context. The semantic layer represents the most recent step in Microsoft's knowledge platform technique, which not too long ago built-in LinkedIn's graph database expertise to supply context.
Microsoft can also be increasing its knowledge portfolio with a sequence of latest companies: Azure HorizonDB, a PostgreSQL-compatible service in early preview, in addition to SQL Server 2025 and Azure DocumentDB, which are actually typically obtainable.
"After I take into consideration what cloth does for purchasers, it provides prospects a unified knowledge platform in order that they don't must sew collectively many, many, many various instruments to get to enterprise worth," mentioned Arun Ulag, company vice chairman of Azure Information at Microsoft.
Why semantic understanding issues for AI brokers
Conventional AI brokers battle with a elementary limitation: they will see patterns in knowledge however don't perceive what that knowledge represents in enterprise phrases. An agent may analyze gross sales transactions with out understanding buyer hierarchies, seasonal patterns or product relationships. It will possibly question stock ranges with out realizing how manufacturing traces hook up with distribution networks or how provider relationships have an effect on availability.
This hole between uncooked knowledge and enterprise which means is what causes unreliable predictions and poor automated selections. Ulag defined that Material IQ addresses this by offering a semantic layer that captures how organizations really function.
This architectural method differs considerably from retrieval-augmented era (RAG) and vector database methods that opponents have emphasised.
Whereas RAG pulls related paperwork to supply context, Material IQ creates a persistent semantic graph representing organizational construction, workflows and enterprise logic. Brokers don't simply retrieve info. They perceive relationships like which suppliers present which merchandise, how manufacturing traces hook up with stock methods or how buyer hierarchies map to gross sales territories.
From analytics semantic fashions to operational ontologies
Microsoft has invested in semantic fashions for over a decade by means of Energy BI. These fashions encapsulate enterprise logic and outline entities and relationships; they specify metrics and hierarchies; they usually hook up with various knowledge sources throughout Azure, AWS, Google Cloud, on-premises methods, and SaaS platforms like Dynamics 365.
"We now have 20 million semantic fashions that run in cloth right now. Why? As a result of we constructed the semantic modeling layer into Energy BI. So behind each Energy BI report is a semantic mannequin," Ulag mentioned. "These semantic fashions already encapsulate quite a lot of the enterprise logic that mirrors what a buyer cares about. What’s the knowledge that they care about? What are the metrics that they care about? How does the information relate to one another?"
The limitation of those semantic fashions has been their scope. They labored effectively for enterprise intelligence, analytics, and visualization, however they solely operated inside particular person experiences or departmental boundaries. Material IQ removes these constraints.
"Nevertheless, we've had a spot. These semantic fashions had been solely used for BI use instances," Ulag mentioned. "There's a a lot greater alternative on the market, which is the chance to have the ability to take these semantic fashions and improve them right into a full ontology."
Upgrading the semantic fashions to ontologies essentially modifications what organizations can do with enterprise context and which means. "What does it do if you happen to improve them into an ontology? What occurs is that now you may join knowledge throughout your enterprise," Ulag mentioned.
He defined that the ontology additionally integrates with real-time knowledge streams. Past connecting knowledge, ontologies enable organizations to outline operational guidelines. This mixture creates the inspiration for operational brokers that perceive enterprise context at a stage that conventional AI methods can not obtain. Cross-enterprise knowledge connections work along with real-time integration and rule definitions.
Operational brokers that perceive and act on enterprise operations
Material IQ allows a brand new class of brokers Microsoft calls "operational brokers." These brokers can autonomously monitor knowledge and take motion based mostly on the ontology's understanding of enterprise operations.
"We're additionally introducing one thing known as operations brokers in cloth that may watch your knowledge for you, that may watch the foundations that you just're asking it to watch. And it may autonomously take motion below human supervision," Ulag mentioned.
Ulag offered a provide chain instance that illustrates the distinction from conventional approaches. A company can mannequin its provide chain and supply operations within the ontology. When real-time knowledge reveals congestion in a part of a metropolis, the operational agent can robotically reroute vans round the issue.
The ontologies created in Material IQ combine instantly with Microsoft's agent improvement platforms. This gives enterprise context that makes brokers extra dependable and correct.
"It actually takes the work that we've accomplished in semantic fashions in cloth with unified knowledge to a totally totally different stage, permitting prospects to have the ability to mannequin their operations and take enterprise actions," Ulag mentioned.
What this implies for enterprise AI methods
There appears to be a want for context engineering to higher allow agentic AI.
Semantics and their related ontologies just do that and extra. Context is about understanding why a request is being made, and semantics perceive the deeper which means. For enterprises battling AI agent reliability regardless of massive datasets, Material IQ represents a essentially totally different method. It strikes past scaling compute or fine-tuning fashions. The crucial query is whether or not enterprise context captured in ontologies would enhance agent effectiveness greater than conventional optimization paths.
The strategic wager Microsoft is making is obvious: Semantic understanding of enterprise operations determines AI agent effectiveness. Entry to massive datasets alone is just not sufficient. Upgrading present semantic fashions into operational ontologies may present a sooner path to dependable brokers.
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