NEWS
How the Industrial AI Conversation Has Changed Since the Last IMTS
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The International Manufacturing Technology Show (IMTS), the largest manufacturing show in North America, returns to Chicago September 14 to 19. The event is a bellwether for discrete manufacturing technology innovation with significant changes from one edition to the next given it is held every other year. Expect a much greater emphasis on Agentic and Physical Artificial Intelligence (AI) from systems that can interpret information and determine what should happen next to those that help initiate an appropriate response with little to no human intervention. There will also be a paradigm shift for automation, robotics, quality, digital engineering, and tooling solutions.
IMPACT
Where Can AI Deliver Real Value?
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ABI Research tracking data shows a 218% increase in industrial AI activity since the last IMTS in 2024. These include a range of partnerships, acquisitions, investments, and internal initiatives, suggesting that industrial AI innovation is as much an ecosystem play as it is a prerogative that can be advanced in isolation.
NVIDIA, for example, has driven 56 such activities in the last couple years across photorealistic engineering, simulation, autonomous robotics, and edge applications; Siemens has engaged in 42; and Microsoft 31. Schneider Electric’s US$3.1 billion acquisition of Cognite is another specific example.
The nuanced dichotomy to look out for at IMTS is how suppliers handle the industrial AI conundrum in production environments: “we want innovative capabilities, but not at the detriment of reliability and determinism.”
RECOMMENDATIONS
Balancing AI Innovation with Operational Discipline
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DMG MORI, Flow International, GROB Systems, Haas Automation, Mazak, Okuma, and TRUMPF must prove they are with the times by demonstrating traction with digital twins, simulation, and AI.
Consider a manufacturing dashboard. Traditionally, someone must know which screen to open, which data to review, and how to interpret it. AI can make that experience more dynamic. An operator could ask why a production line slowed during the previous shift and receive a focused explanation based on quality records, machine data, and operating conditions.
Quality analysis and root cause identification are other promising AI applications. When something goes wrong, the first challenge is understanding what happened. The second is determining how to resolve it quickly. Industrial AI excels at contextualizing events, enabling customers to move through both stages faster. It also delivers relevant information to workers when they need it instead of requiring them to search through disparate systems.
That information can become valuable beyond the individual workflow. Rather than documenting what went wrong, AI can help customers understand what went right. If one factory achieves an unusually strong result, AI can document the conditions and practices that contributed to it. Production owners can then determine whether that success can be repeated at another site, which is attractive to both operational business leaders and Chief Technology Officers (CTOs)/Chief Information Officers (CIOs) tasked with scaling digital transformation projects.
