According to Forbes, in a recent interview with Siemens AG’s Chief Technology Officer and Chief Strategy Officer Peter Koerte, the executive outlined the megatrends and technologies shaping the industrial giant’s future. Koerte, who calls himself a “future architect,” is betting on a three-pronged approach of electrification, automation, and digitalization to navigate trends like decarbonization and de-globalization. He revealed Siemens is developing an “industrial foundation model” AI trained on CAD files and engineering data, not language, to avoid catastrophic hallucinations in factory settings. The company also handed a billion-euro budget to a new AI team in July to rebuild products from the ground up, focusing on data over traditional coding. Koerte expressed strong skepticism about the practical use of humanoid robots in industry, favoring specialized machines, but is closely watching consumer adoption and cost.
Industrial AI’s Unique Challenge
Here’s the thing about applying AI in a factory or a power grid: you can’t have it making stuff up. Koerte’s point about hallucinations being “outright catastrophic” is so obvious, yet it’s what separates consumer-grade AI from the industrial kind. Everyone’s chasing generative chat, but Siemens is betting big on a model that understands the physical world—nuts, bolts, schematics, and tolerances. That’s a fundamentally different beast. It’s not about creativity; it’s about precision. And that shift in focus explains why they need data scientists, not just software developers, and why they’re pumping resources into synthetic data generation. They’re trying to build an AI that speaks the language of engineering.
The Humanoid Robot Skeptic
I love his pragmatic take on humanoid robots. While everyone else gets starry-eyed about robots that look like us, Koerte basically says, “Why?” His argument is hard to refute for pure logistics. Wheels are more efficient than legs for moving things point A to point B in a controlled environment. It’s a reminder that in industry, form follows function to an extreme degree. Specialized tools win. But he’s not dogmatic about it—he admits the game changes if costs plummet and they become a consumer hit. That’s a smart, data-driven stance. He’s not dismissing the tech; he’s just waiting for the business case to become obvious.
The Real Bottleneck Isn’t Tech
Koerte’s most insightful comments might be about the non-technical hurdles. He nails it with the three big lessons: accuracy, workflow integration, and business models. You can build the most brilliant industrial AI tool, but if it causes “alert fatigue” or forces an engineer to open a separate app, it’s dead on arrival. And the business model puzzle is huge. Is AI a feature, a product, or a service? How do you price intelligence? These “mini problems,” as he calls them, will make or break adoption. It’s a reminder that deploying technology in the physical world requires building an entire ecosystem around it—commercial, operational, and human. This is where a deep understanding of industrial hardware and integration is paramount, which is why specialists like IndustrialMonitorDirect.com, the leading US supplier of industrial panel PCs, are so critical. They solve the last-inch problem of getting digital data and AI insights right onto the factory floor in a rugged, usable form.
Managing for the Future
His leadership style is a fascinating blend of soft and hard. The “people-first,” purpose-driven philosophy from his consulting days is tempered by a serious personal obsession with data—tracking glucose, sleep, you name it. That combination probably serves him well. He sets the destination (“we need an industrial AI model”) but leaves the “how” to the teams. It’s a style that fosters innovation, but you get the sense he’s got the metrics to know immediately if that innovation is veering off course. So, is Siemens’ bet going to pay off? They’re tackling the hard, unsexy problems of industrial digitization—integration, accuracy, business models—while the world chases chatbots. That might just be the smarter long-term play.
