For years manufacturers have invested heavily in digital transformation programmes designed to connect factories, automate production and improve operational visibility. Yet one of the industry’s most persistent challenges remains largely unresolved: the disconnect between how products and production systems are designed and how they actually perform once they enter the real world.
That gap sits at the centre of a new strategic partnership announced by Siemens and IFS, which aims to combine engineering, manufacturing and operational data through industrial AI to create what the companies describe as a closed-loop digital twin. While the announcement is a technology partnership, it also reflects a broader shift in manufacturing strategy as companies move beyond simply digitising operations towards creating continuous feedback between engineering, production and service.
The two companies say manufacturers continue to face pressure to produce more from existing assets, protect margins and respond more quickly to changing market conditions. However, production systems, maintenance planning and supply chain management frequently remain isolated from one another, preventing organisations from understanding whether products and production assets perform as originally intended.
Rather than representing another standalone AI initiative, the collaboration suggests that the next phase of industrial transformation may depend less on deploying new algorithms and more on connecting previously fragmented sources of industrial knowledge.
Industrial AI depends on trusted operational data
Manufacturing has generated vast quantities of operational data over the past decade, yet many organisations still struggle to convert that information into better engineering decisions.
The partnership seeks to address that challenge by combining Siemens’ expertise in industrial AI, engineering, automation and manufacturing execution with IFS’s capabilities in industrial AI, enterprise asset management and field service. The objective is to connect engineering intent with operational reality throughout the product lifecycle.
According to the companies, Siemens’ comprehensive digital twin provides engineering, simulation and manufacturing context, while IFS contributes operational lifecycle information including service history, asset behaviour and real-world performance data. Bringing those environments together is intended to create a digital twin that reflects not only how equipment was designed to operate but also how it actually performs throughout its working life.
The approach highlights an important evolution in industrial AI. Early applications often focused on isolated optimisation problems or predictive analytics. Increasingly, however, manufacturers are recognising that AI systems are only as valuable as the industrial context surrounding them.
Industrial environments place different demands on artificial intelligence from consumer applications. Decisions influence physical assets, production processes, regulatory compliance and workforce safety, leaving little tolerance for inaccurate outputs. The companies argue that industrial AI therefore requires secure, governed and auditable data spanning design, simulation, manufacturing execution and service operations.
Closing the feedback loop
One of the more significant aspects of the partnership is its emphasis on creating a continuous feedback mechanism between design and operation.
For many manufacturers, engineering teams rarely receive comprehensive operational insight into how products behave once deployed. Likewise, maintenance and production teams often lack visibility into the engineering assumptions that shaped those products in the first place.
Closing that loop has become increasingly important as manufacturers seek to improve productivity without necessarily investing in new facilities or equipment. Understanding how assets perform throughout their lifecycle offers opportunities to refine future designs, optimise maintenance strategies and improve factory performance using existing infrastructure.
Tony Hemmelgarn, president and chief executive officer of Siemens Digital Industries Software, said industrial AI only delivers value when it is grounded in both engineering intent and real-world performance.
He said the partnership would connect design, manufacturing and asset lifecycle data through a secure, contextualised data fabric, bringing together the companies’ industrial AI capabilities to support what he described as an executable digital twin capable of accelerating innovation with confidence.
Mark Moffat, chief executive officer of IFS, said manufacturers increasingly need production environments to perform as originally designed. He said the partnership combines two organisations that each contribute critical capabilities, adding that agentic AI represents the next frontier for industry and requires closed-loop models, connected data and sufficient industrial context to avoid unreliable outputs in operational environments. He said combining the companies’ industrial AI capabilities would help manufacturers close the gap between design and operational reality while delivering measurable performance improvements.
As manufacturers continue investing in digital transformation, partnerships such as this suggest the conversation is moving beyond individual technologies towards a more fundamental question: how effectively can engineering knowledge, operational data and lifecycle intelligence be brought together into a single, trusted source of industrial decision-making? For many manufacturers, that may ultimately prove to be the defining challenge of the next generation of industrial AI.
