LAS VEGAS — AI everywhere was the big theme at CES this week, with the technology showing up in nearly every application and device, no matter whether users actually want it or not. Beyond AI-enabled refrigerators and smart glasses, the transformative story of this year’s show actually had an enterprise bent: the arrival of physical AI . That’s the kind of AI that makes robots smart and that makes autonomous cars safe for the road. (Though robots and cars are the most visible part of the physical AI picture, they’re also the smallest.) The bigger impact of physical AI is in large-scale industrial applications. What is an assembly line, after all, but a giant robot composed of hundreds or thousands of smaller ones? And why stop at assembly lines? Why not treat an entire factory as one giant robot? In fact, why stop at a single factory when you can look at the entire supply chain — all the factories, all the partners and suppliers, perhaps even the customers. Say, for example, a bumper falls off one of the specialized industrial vehicles made by the Oshkosh Corporation. “You can have traceability,” Jay Iyengar , the company’s executive vice president, CTO, and strategic sourcing officer, told Computerworld . For example, was there enough torque applied to the bolt at the factory? “So, you go back and, say, ‘Our manufacturing plant was okay and there was nothing wrong with the torque.’ Then, when was this bumper manufactured and by which supplier? We can trace it back to the supply chain, all the way through.” And once the visibility is in place, the entire AI-powered manufacturing system can autonomously — or semi-autonomously — take action to correct the problem. “That’s the euphoria you get from that,” Iyengar said. Oshkosh isn’t ready to update everything to AI right away, however. Even once Oshkosh’s factories are brought into the industrial AI era — it is starting the upgrades this year — all the other companies in the supply chain have to come on board as well. The bigger suppliers are well on their way to full digitization and visibility, but some of the smaller players face a bigger challenge. “There’s a lot of work involved,” she said. “It’s going to take us several years to get to that level.” Autonomous cars At CES, there were a number of announcements related to physical AI. Nvidia, for instance, talked about autonomous car AI. In a keynote speech Monday, Nvidia President and CEO Jensen Huang announced the release of Alpamayo, an open-source AI model designed specifically for autonomous cars. Having AI that understands not just car systems but also how the world works is key to making autonomous vehicles safer, he said. “If a ball rolls out into the street, a child might be following quickly behind,” Huang said. The first car built atop this platform — the all-new Mercedes‑Benz CLA — will hit the market in the first quarter of this year, he said. The vehicle has already received a five-star safety score from EuroNCAP, the European New Car Assessment Program. “It was just rated the world’s safest car,” Huang said, noting that Uber and BYD are also using the new autonomous car AI platform. Autonomous factories Nvidia is looking beyond cars — or individual robots — as applications for its focus on physical AI. Caterpillar, for example, uses Nvidia technology and has built not just the world’s largest robot, but is looking to scale up the technology to its factories, Huang said. “These manufacturing plans are going to be, essentially, giant robots,” he said. But while Nvidia makes open-source AI models for things like robots, cars, and real-world physics, it doesn’t have the data and expertise to make entire factories autonomous. The company that emerged as the leader in that area this week was Siemens, which was founded back in 1847 as a small machine shop and has since grown into a global tech company. Nvidia plans to use Siemens’ technology to help improve its own chip factories, and the chip design process itself, even as Siemens relies on Nvidia’s models as part of its own AI development. Siemens already has a big presence in the global manufacturing sector. One out of every three manufacturing machines worldwide runs a Siemens controller, Siemens President and CEO Roland Busch told reporters Monday. That’s a lot of data for the company to work with and allows it  to build what Busch called the industrial AI operating system. That includes the Digital Twin Composer, which will allow companies to create digital twins of entire factories . And these twins won’t be used simply to offer a real-time view of operations or to help companies simulate individual future scenarios. This tool, powered by AI, will also allow companies to predict failures before they occur and to autonomously — or semi-autonomously — take action to remediate problems or rearrange production and schedules around the problem until it can be fixed. This digital twin can also understand the physics behind how components interact, predict events that might not be in its training data set, and pull in information such as weather data from external sources. Siemens said its first autonomous factory will come online in Germany this year and the Digital Twin Composer tool will be on the market by mid-year. That said, some customers have already moved ahead with the technology. Pepsico, for example, rolled it out last year, said PepsiCo’s Athina Kanioura , CEO for Latin America and the company’s global chief strategy and transformation officer. “We have had significant impact, even in the first three months,” she said. Kanioura was one of several corporate leaders who participated in the CES opening keynote. “In the Gatorade plant in the US, we were able to increase efficiency 20% in just the first three months,” she said. “And we had a capex reduction of 10 to 15%.” The biological frontier Siemens also envisions its digital twins as helping automate the pharma drug discovery process. Its acquisition of Dotmatics, a life sciences R&D software company, was completed in mid-2025. “Drugs are getting more expensive every time we go through a new development cycle,” Siemens’ Busch told reporters. “And when you target specific long-tailed diseases, the costs become prohibitive.” The biggest problem is that drug discovery is still very much a labor-intensive process, he said. But there are opportunities to apply industrial manufacturing principles to the research and development process. “What if we can do the same things for cells and how the cells behave?” he said. “And look at antibodies, and drug compounds — and simulate how they would interact with antibodies.” He estimated that the technology Siemens is building on top of the Dotmatics platform will accelerate the lab-to-patient cycle by 50%. Editor’s note: Lenovo paid for Maria Korolov’s transportation and hotel costs for this year’s CES, but had no editorial role in the creation of this story.