Siemens and IFS Partner to Close the Loop Across the Product Lifecycle with Industrial AI

Siemens and IFS announced a strategic partnership to help manufacturers connect engineering intelligence with operational reality, increasing the value of their products and optimizing their production assets across the entire product lifecycle with Industrial AI.

The collaboration brings together Siemens’ leadership in Industrial AI, engineering, automation, and manufacturing execution and IFS’ strengths in Industrial AI, enterprise asset management, and field service domains. Together, the two companies aim to help manufacturers close a persistent gap: the disconnect between how factory operations are designed and how they run in reality, where unplanned downtime, disconnected maintenance schedules, siloed production data, and supply chain disruption continue to erode throughput, agility, and margin.

Manufacturers are under growing pressure to do more with their existing assets: produce more on the plant floor, protect margins, extend the value of equipment across its full lifecycle, and react to change with greater agility and adaptability. Yet many still operate with production, maintenance planning, and supply chain management systems that do not talk to each other, meaning engineering intent, real-world performance, and service strategy remain disconnected.

Industrial AI is central to the partnership’s ambition. Siemens and IFS share the belief that the next era of industrial performance will be defined by bringing the physical and digital worlds together to help manufacturers translate design intent into operational reality and connect that operational reality back into better design to accelerate innovation.

Siemens’ comprehensive digital twin brings the engineering, simulation, and manufacturing context, while IFS brings the service history, asset behavior, and operational lifecycle data that show how those products and assets perform in the real world. Together, they plan to create a closed-loop digital twin grounded in both design intent and field performance that is secure, governed, and auditable across design, simulation, service records, and factory execution, and can be trusted to deploy at industrial scale.

Unlike generic AI models, industrial environments demand accuracy, reliability, regulatory compliance, and adaptability to drive optimization and agility, as even small error rates are unacceptable when decisions affect safety, compliance, and costly physical assets. The partners’ shared approach to industrial AI is built for this reality.