{"id":50098,"date":"2025-10-23T16:12:20","date_gmt":"2025-10-23T14:12:20","guid":{"rendered":"https:\/\/www.cybus.io\/?p=50098"},"modified":"2025-11-03T17:27:28","modified_gmt":"2025-11-03T16:27:28","slug":"manufacturing-intelligence-with-ai","status":"publish","type":"post","link":"https:\/\/www.cybus.io\/en\/manufacturing-intelligence-with-ai\/","title":{"rendered":"Manufacturing Intelligence with AI"},"content":{"rendered":"\n
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Why You Need a Solid Data Foundation First<\/strong><\/h2>\n\n\n\n

AI is rapidly moving from innovation labs into operational use cases across manufacturing. But many initiatives hit the same wall: While AI pilots show promise, they often fail to scale. Why? Because the foundational layer, aka the access to clean, contextual, real-time data from across the factory floor and enterprise systems, is missing or fragmented.<\/p>\n\n\n\n

Here\u2019s what I\u2019ve learned from conversations with analysts and customers: before you invest in AI, you need to invest in data infrastructure.<\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n

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Expert article by <\/p>\n\n\n\n

Jonas Schramm
Head of Industry Solutions, Cybus<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Out-of-the-box AI doesn\u2019t work for complex manufacturing environments<\/strong><\/h3>\n\n\n\n

Most AI solutions available today work for generic tasks like machine state detection (on\/off), basic anomaly detection or predictive maintenance under standard conditions. But real value comes from contextual, company-specific AI models that reflect how your machines, your processes and your supply chain actually work.<\/p>\n\n\n\n

That means AI must be trained with your own data, and not just any data. It needs to be real-time, reliable and connected across systems.<\/p>\n\n\n\n

The manufacturing system landscape is a maze of silos<\/strong><\/h3>\n\n\n\n

Enterprise manufacturers operate with dozens of interdependent systems in their IT and OT landscape: ERP, MES, PLM, SCADA, EMS, MOM and more. They have their raison d\u2019\u00eatre through their specialization \u2014 totally reasonable. However, the result is scattered, isolated or locked-in data, sometimes even in legacy interfaces.<\/p>\n\n\n\n

Standardizing or replacing these systems is rarely an option. For security, governance and compliance reasons, many data sources must remain in place. And major vendors like SAP are betting on extending their platforms with AI agents and assistants, rather than replacing the core stack.<\/p>\n\n\n\n

AI success depends on cross-system data orchestration<\/strong><\/h3>\n\n\n\n

This is where most AI initiatives break down: without a way to connect data across IT and OT in a secure and scalable way, even the most powerful algorithms are blind. You don\u2019t need a new architecture. You need an industrial data backbone that can:<\/p>\n\n\n\n