87 INDUSTRIALS & MANUFACTURING these questions, scaling connected products – and the AI capabilities built on top of them – becomes difficult. AIoT platforms like Cumulocity are designed to bridge the gap between industrial environments and modern cloud architectures. They provide capabilities for securely onboarding devices, managing software and firmware updates across large fleets and structuring operational data through flexible asset models. This operational layer ensures industrial assets can connect safely to enterprise cloud platforms for analytics, AI and security monitoring without forcing manufacturers to rebuild their operational systems. Just as importantly, they provide a consistent operational layer that allows manufacturers to connect legacy and modern systems without redesigning their entire infrastructure. None of this eliminates the governance tax entirely. But it changes the equation. When lifecycle management, device identity, and operational context are managed through a coherent platform architecture, governance becomes far more manageable. Security updates can be rolled out more safely, operational changes can be tracked and audited, and data can be delivered to analytics and AI systems with the context they require. Industrial AI will ultimately succeed not just because algorithms continue to improve, but also because the operational foundations supporting them mature. Manufacturers that invest in strong fleet lifecycle management and a coherent operational platform will be far better positioned to scale industrial AI responsibly. By separating operational control from cloudscale analytics, organisations can connect industrial systems to modern AI, security, and enterprise data platforms while maintaining the safety, reliability and accountability that industrial environments demand. Juergen Kraemer is chief product officer at Cumulocity Manufacturers are now managing thousands, or even millions, of connected assets Photo: iStock/gorodenkoff
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