Siemens and IFS Partner to Connect Engineering and Operations With Industrial AI

Siemens and enterprise software company IFS have announced a strategic partnership to integrate their industrial AI platforms, aiming to connect engineering, manufacturing, and asset management data into a continuous feedback loop across the product lifecycle.

The companies said the collaboration combines Siemens' expertise in engineering software, industrial automation, manufacturing execution, and Digital Twin technology with IFS' capabilities in enterprise asset management (EAM), field service management, and operational lifecycle data. The goal is to help manufacturers reduce the disconnect between product design and day-to-day operations, where equipment failures, maintenance schedules, and production changes often remain isolated from engineering systems.

According to the announcement, the companies plan to develop what they describe as a closed-loop Digital Twin that links engineering intent with operational performance, allowing manufacturers to use real-world data to improve future designs and production decisions. 

Connecting Design Data With Operational Reality

Manufacturers increasingly rely on digital engineering tools, factory automation, and AI-assisted production planning, but many still manage engineering, production, maintenance, and service data in separate systems. Siemens and IFS said those disconnected workflows reduce factory throughput, limit operational agility, and make it more difficult to optimize equipment throughout its lifecycle.

Tony Hemmelgarn, President and CEO of Siemens Digital Industries Software, said the partnership aims to connect those previously separate domains through industrial AI.

"Industrial AI only delivers value when it is grounded in both engineering intent and real-world performance," Hemmelgarn said. "Together with IFS, we are bringing these domains together by connecting design, manufacturing and asset lifecycle data in a secure, contextualized data fabric."

The companies said Siemens will contribute engineering, simulation, automation, manufacturing execution, and Digital Twin capabilities, while IFS will contribute operational lifecycle information such as service history, maintenance records, and asset performance. Together, those datasets are intended to create an industrial AI environment that remains auditable and governed across design, manufacturing, and field operations.

The announcement aligns with a broader trend of industrial companies expanding AI beyond software development into factory operations. That includes investments in physical AI technologies, such as BMW's deployment of humanoid robots in manufacturing facilities, which reflects growing efforts to integrate AI directly into industrial workflows.

Industrial AI Moves Toward Closed-Loop Manufacturing

The companies also positioned the partnership around industrial AI systems that can support operational decisions involving physical assets. Unlike general-purpose AI models, industrial environments require high levels of accuracy because errors can affect safety, production, regulatory compliance, and equipment reliability.

Mark Moffat, CEO of IFS, said manufacturers increasingly need AI systems built on operational context rather than standalone models.

"Agentic AI is the critical frontier, and industrial leaders need solutions with closed loop models and data, and a rich set of context that will not hallucinate in active operations," Moffat said.

The partnership also reflects a broader shift across industrial technology companies toward embedding AI into core manufacturing and operational systems instead of treating it as a standalone productivity tool. That trend is evident across the sector as companies restructure their businesses around industrial AI and automation, including recent moves by Honeywell to reshape its industrial portfolio.

While the companies did not disclose financial terms or a deployment timeline, they said the partnership will focus on helping manufacturers improve productivity, extend asset lifecycles, and enable engineering teams to use operational data to continuously refine products and production processes.