Microsoft Ignite 2025 introduced Fabric IQ as a major shift in how organizations move from collecting data to truly understanding it. Alongside Fabric IQ, Microsoft also announced Agent 365, a new control plane for managing and governing AI agents across the enterprise. In this blog, we look at what this shift means in practice. To make the ideas concrete, we use manufacturing as an example, not because this is only about factories, but because manufacturing highlights the challenge clearly: enormous amounts of data, tightly connected systems, and high cost when understanding is missing. The same principles apply across every industry; manufacturing simply helps us illustrate them.
Walk into any modern production plant and you immediately see how data-heavy industrial operations have become. Machines generate streams of sensor signals. Operators log events. Systems document every order, material and action. Quality tools track defects. BI dashboards glow on office monitors.
Yet when something goes wrong - a yield drop, a puzzling scrap spike, a sudden machine stoppage - teams still scramble to combine information from different systems and different interpretations. Despite massive investments in technology, most organizations remain stuck in a world where they have plenty of data but very limited shared understanding.
This isn’t a technology problem. It’s a meaning problem.
Even basic items and attributes don’t align across systems. One system calls an item a “SKU”, another labels it a “material”, a third manages it as a “variant”, and BI groups it under a different category entirely. Attributes vary too: weight becomes net mass, size becomes volume class, and availability becomes active, allocated or blocked depending on the system. All of these descriptions are technically correct, but together they create a fragmented picture of reality that slows down investigations, complicates analytics and makes AI much harder to scale.
This is the gap Fabric IQ is designed to close.
Fabric IQ introduces a semantic intelligence layer that unifies meaning, not just pipelines. Instead of forcing every system to connect through technical integration, it creates a shared foundation that all analytics, operations and AI systems can rely on.
It starts with a common language. Fabric IQ lets organizations model machines, products, customers, suppliers, processes, events and KPIs consistently across their environment. When everyone - and every system - uses the same definitions, confusion disappears. Reports stop contradicting each other. AI agents inherit shared logic. And teams focus on solving problems instead of debating definitions.
This unified understanding becomes even more powerful when trusted business logic is no longer locked inside dashboards. Fabric IQ elevates KPIs such as OEE, yield, cycle time or order performance into a layer that both humans and AI can use. For the first time, the definition of a KPI is the same in a report, in an application, in a simulation model and in an agent’s reasoning.
The result: decisions become consistent, repeatable and trustworthy.
Real-world operational problems rarely happen in isolation. They propagate across processes, systems, suppliers, people and time. One shift in supplier material properties might change vibration patterns, which might affect scrap, which might influence planning, which might increase operator workload and ultimately impact throughput.
Most tools only see their part of this chain.
Fabric IQ introduces a reasoning graph that reveals how everything connects. Relationships that previously remained hidden suddenly become visible. Patterns that once appeared random begin to make sense. Teams stop chasing symptoms and start addressing underlying causes.
With this system-level visibility, problem-solving accelerates. Chronic issues become solvable. And improvement efforts become more strategic rather than reactive.
Another major shift comes through agents that understand business meaning. Fabric IQ enables this by giving data shared definitions and context - but organizations still need a way to manage, govern and monitor the agents acting on that intelligence. This is exactly what Agent 365, announced at Ignite 2025, provides. Agent 365 acts as the enterprise control plane: defining agent access, ensuring security, visualizing behavior, establishing audit trails and making sure agents operate with the same discipline as any business-critical system.
Data Agents act like virtual analysts. Instead of searching across dashboards, a supervisor can ask: “Why was scrap higher last week?” or “What’s at risk in our upcoming schedule?” The agent responds using the organization’s shared definitions, relationships and context - not guesswork.
Operations Agents go further by acting in real time. They detect early signs of deviation, interpret what those signals mean and recommend or perform corrective actions. They continuously learn. And with Agent 365 orchestrating and governing these agents end-to-end, organizations can adopt this capability safely and at scale.
This is how organizations move from describing what happened yesterday to influencing what happens right now and preventing issues before they occur.
When organizations adopt a semantic intelligence layer, performance improvements follow naturally. Downtime shrinks because problems are found sooner. Quality improves because causes are clearer. Throughput increases because bottlenecks become visible and predictable. Teams gain time because insight comes instantly rather than through manual work.
In short, the organization becomes more confident, more consistent and more capable - not because it has more data, but because it finally understands the data it already had.
For years, the industry has talked about predictive operations, smart factories and AI-driven decisions. But these visions always stalled because the underlying data lacked shared meaning.
Fabric IQ delivers that missing layer.
It doesn’t just connect systems; it connects understanding. It doesn’t just answer questions; it makes them easier to ask. It doesn’t automate guesswork; it automates reasoning grounded in business reality.
And with Agent 365 ensuring that the agents acting on this intelligence are governed, monitored and scalable, the entire AI ecosystem becomes enterprise-ready.
This marks a shift in how organisations create value from data — from collecting it to truly understanding it.