Inside Siemens and IFS's Performance-Boosting Industrial AI

Siemens and IFS are set to deploy industrial AI systems that connect production planning with asset management software.

The collaboration addresses a specific technical challenge: linking engineering design data with operational performance data across disconnected manufacturing systems.

According to McKinsey, 88% of companies now use AI in at least one function and 62% are either experimenting with or scaling agentic AI.

The consulting firm found that decreasing costs for cloud storage, processing and pre-packaged AI tools could make these technologies more accessible across industrial applications. However, implementation remains inconsistent.

According to McKinsey's survey of more than 100 manufacturing COOs, only 29% report having a global production system fully implemented across all sites, despite 74% claiming their company has one.

Closing the design-reality gap

The partnership centres on what both companies describe as a persistent technical problem in industrial AI deployment. Engineering intent, real-world performance data and service strategy often remain in separate systems with no functional connection between them.

Siemens cites unplanned downtime, disconnected maintenance schedules, siloed production data and supply chain disruption as outcomes of this disconnection. The company argues these factors continue to affect throughput, agility and operational margins.

Tony Hemmelgarn, President and CEO of Siemens Digital Industries Software, says: "Industrial AI only delivers value when it is grounded in both engineering intent and real-world performance.

"Together with IFS, we are bringing these domains together by connecting design, manufacturing and asset lifecycle data in a secure, contextualised data fabric.

"By converging our combined strengths in industrial AI, together we will empower our customers with our vision of an executable digital twin that will enable them to accelerate innovation with confidence."

Technical architecture and integration

Siemens will contribute its digital twin technology to the partnership. This system models how factory operations are designed to function.

IFS will provide service history data, asset behaviour patterns and operational lifecycle information.

The combination could create what the companies describe as a closed loop model connecting design specifications with actual operational outcomes.

Mark Moffat, CEO of IFS, says: "Manufacturers need their factory floor to behave the way it was designed. This partnership with Siemens brings together two companies that each own a critical piece of the puzzle."

Mark emphasises the importance of data quality for agentic AI systems: "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."

Addressing AI adoption barriers

The technical challenge extends beyond simply installing AI systems.

According to McKinsey, many companies have rolled out various digital tools and improvement initiatives, but reported results not in line with expectations.

A global production system functions as an interconnected, international network that designs and manufactures goods. Siemens argues that, without proper data integration between production, maintenance planning and supply chain management systems, these networks cannot operate as designed.

The partnership aims to provide manufacturers with AI systems that can access contextualised data from both design and operational domains.

According to Mark, this approach could help manufacturers close the loop between design and reality and unlock measurable performance gains.

The collaboration reflects how AI implementation in industrial settings requires more than algorithm deployment. It depends on connecting previously isolated data sources across the manufacturing lifecycle.