Industrial AI & Machine Learning
Why Context Is More Important Than Algorithms
- Relevance: Only contextualized data leads to usable AI models.
- Semantics: The unified namespace as a “dictionary” for AI.
- Precision: Avoiding false positives by correlating IT and OT data.
- Efficiency: Faster training phases thanks to clean data structures.
Data without context is just noise
In the IT world, it may be enough to feed large amounts of data into a model. In OT, this leads to AI “hallucinations.” A rise in temperature could be a defect—or an intentional process step. True industrial AI understands the context. By bringing IT structures back into OT, every data point is enriched with metadata (machine, order, material). It is this context that transforms AI from a statistical toy into a reliable tool.
The UNS as a training camp
A Unified Namespace (UNS) is like a perfectly organized archive for AI. Instead of data scientists wasting 80% of their time cleaning data, AI accesses a semantic structure. This means that the AI “knows” that Sensor A belongs to Pump B. This massively accelerates the development of machine learning models and ensures that the results actually hold up in real-world production.
Correlation rather than isolated analysis
Real added value is created when AI correlates data from different sources. What does the humidity in the production hall (OT) have to do with the scrap rate in the ERP system (IT)? Only by bringing these two worlds together can AI identify such complex relationships. The result is more precise predictions that go far beyond simple threshold monitoring and enable true root cause analysis.
From a siloed solution to scalability
Without a standardized data structure, AI often gets stuck in the “pilot trap”: what works on Machine A fails on Machine B because the data there is named or structured differently. Contextualization enables transfer learning. When AI understands the functional context (e.g., the concept of a “drive” regardless of the manufacturer), models can be scaled to the entire machine fleet with minimal effort. Context is thus not only a quality feature but also the decisive lever for economically rolling out AI from a single project to the entire organization.
AI and Machine Learning with pronubes
pronubes is paving the way for high-end AI. By not only collecting data but also providing it with the necessary context within the unified namespace, the platform delivers the “premium fuel” your AI models need to perform accurately in the demanding OT environment.
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