Artificial intelligence promises to transform industrial operations, but in practice the gains are often slowed by one simple reality: manufacturers have petabytes of data, but most of it doesn’t fit together in a way that facilitates real analysis. Operators of refineries, chemical plants and offshore rigs face two stubborn challenges. The first is that the data is extremely diverse. The second is that none of them are connected.
On the diversity side, plant managers must manage three very different streams of information. There are unstructured documents, like PDFs and schematics. There are structured business records from ERP and maintenance systems. And there are massive volumes of time series data flowing in from equipment sensors: lots of updates with little context. Each requires its own storage and management approach. This creates silos that make it difficult for operators to combine workforce planning with operational needs.
Even when these silos are merged in a data lake, the mismatch problem remains. The name of a sensor on a schematic may not match the name in the ERP system, and neither matches the physical tag on the equipment. That makes it nearly impossible to connect information across systems. If a field operator wants to see a month of sensor readings, what maintenance was performed, and where the equipment is located in the plant, the task can take hours or days. You cannot make real-time decisions when it takes hours just to find a valve.
One refinery leader described how his team of 800 engineers used to spend two to three hours at the start of each shift just searching for the data they needed before going into the plant. Those hours represent lost productivity, higher costs and safety risks.
This is where AI techniques can begin to make a difference. Entity resolution and semantic matching can align sensor names and tags across different systems, making it possible to connect information that otherwise would not line up. Multimodal search allows operators to retrieve context-rich answers that pull from time series, business systems and documents all at once, rather than looking in each source separately. Knowledge graphs preserve the relationships between different data streams, creating a single view that connects work orders, equipment specifications and sensor values.
Generative AI thrives on context, the rich information that explains what the data is and the relationships, and this is where industrial systems often fall short. Time-series data by itself is just billions of rows of timestamps and values with no understanding of assets, maintenance history, engineering schematics, or operating conditions. And without that context, GenAI results on raw data alone cannot be trusted. By linking all these sources in a knowledge graph, organizations give AI the relational understanding it needs to deliver accurate, verifiable, and operationally reliable insights.This opens the door to new possibilities. Root cause analysis, which often takes months, can be reduced to weeks. Predictive maintenance becomes more accurate. Operators spend less time searching for information and more time acting on it. Safety and reliability improve because decisions are based on a complete and accurate picture of plant operations.
The lesson is clear: to succeed with AI in industry, organizations must first succeed with their data. If the data remains raw and fragmented, AI will fall short. But when companies address diversity, mismatch and context, they can move from data chaos to operational clarity and finally realize AI’s potential in the field.
