86 “AIoT platforms like Cumulocity are designed to bridge the gap between industrial environments and modern cloud architectures” VIEWPOINT Industrial AI is moving quickly from experimentation to operational reality. Manufacturers are deploying connected assets across factories, supply chains and customer environments with the expectation that data and AI will unlock new levels of efficiency, automation and service innovation. But as organisations begin scaling connected products and industrial AI initiatives, many encounter an unexpected constraint: governance. As fleets of connected devices grow, manufacturers must manage cybersecurity, software lifecycle updates, operational accountability and regulatory expectations across thousands – or even millions – of distributed assets. The operational effort required to manage this complexity is becoming what many organisations now recognise as a ‘governance tax’ on industrial AI. Regulation is accelerating this trend. Frameworks such as the European Union’s Cyber Resilience Act and NIS2 are raising expectations around secure product design, software lifecycle management and vulnerability disclosure. Similar conversations are emerging globally as governments increasingly treat connected devices as part of critical infrastructure. But regulation is only part of the challenge facing manufacturers. The deeper challenge is architectural. Most industrial environments were never designed to operate globally connected fleets of intelligent devices. Manufacturers must manage heterogeneous environments that mix modern cloud-connected systems with decades-old operational technology. Devices operate across multiple protocols, firmware generations and connectivity environments. This fragmentation makes governance difficult. Security teams struggle to maintain visibility into device identity and software versions. Operations teams worry about applying updates that could disrupt production. Engineering teams must manage increasingly complex software supply chains. At the same time, industrial AI initiatives depend on high-quality operational data. Raw telemetry alone is rarely enough. Data must be contextualised – linked to asset models, operational processes and enterprise systems – before it becomes useful. In practice, this means manufacturers must solve two challenges simultaneously: operational context and lifecycle governance. Operational context allows organisations to understand how assets are performing. By structuring telemetry within asset hierarchies and linking it to enterprise systems such as enterprise resource planning, manufacturing execution system and service platforms, manufacturers can build the foundation required for predictive maintenance, performance optimisation and other applications. Lifecycle governance answers a different set of questions. Which devices are deployed across the fleet? What software versions are running? How are updates delivered securely? What actions have occurred across the lifecycle of each asset? Without reliable answers to The governance tax With manufacturers now managing the complexity of many new AI-enabled assets, a mature operational platform is essential to their success JUERGEN KRAEMER: CUMULOCITY
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