The Turning Point Has Arrived – And German Engineering Excellence Is Leading the Way
Las Vegas, January 6, 2026 – While the tech world debates the next big foundation model, Siemens demonstrated at CES 2026 how industrial AI actually scales. Not as a vision. Not as a pilot. But in production, with measurable results.
PepsiCo increased throughput by 20 percent. KION Group is evaluating the stack. Foxconn and HD Hyundai are on board. And Siemens Electronics Factory in Erlangen will become the world’s first fully AI-driven, adaptive manufacturing site in 2026.
This is the moment I’ve been waiting for.
Why This Announcement Is Different
I work at KION on the AI strategy for a €13 billion industrial corporation. I see daily how difficult it is to get AI from the presentation deck to the shop floor. The complexity. The integration hurdles. The reliability requirements for mission-critical systems.
And I’ve been asking myself for years: When will the much-vaunted deep knowledge and capabilities in German mechanical engineering finally arrive in the AI era?
Not as a toy. Not as “let’s do something with AI too.” But with the full force and seriousness that has distinguished this industry for decades.
Roland Busch’s CES 2026 keynote, CEO of Siemens, delivers exactly this answer. And it shows: German engineering excellence combined with cutting-edge AI infrastructure – built from the ground up for industry – can lead.
What Siemens Unveiled
The Industrial AI Operating System
Together with NVIDIA, Siemens presented their Industrial AI Operating System – an end-to-end platform covering the entire industrial value chain. From design and engineering through manufacturing and operations to the supply chain.
This isn’t another cloud tool. This is a fundamental reimagining of how physical systems are designed, built, and operated.
The technological foundation:
- NVIDIA Omniverse as simulation framework
- NVIDIA PhysicsNeMo for AI-driven physics modeling
- Complete GPU acceleration across Siemens’ entire simulation portfolio
- NVIDIA CUDA-X Libraries integration
- Hundreds of specialized Industrial AI engineers from Siemens
The result: 2-10x speed improvements in critical workflows like electronic design automation, verification, and process optimization.
The Industrial Foundation Model
But what’s truly remarkable isn’t the partnership itself – it’s what Siemens is building with it: An Industrial Foundation Model.
Not a general foundation model adapted for industrial applications. But a model developed from the ground up for industry, trained on:
- Decades of proprietary industrial data from Siemens and partners
- Physics-based simulations as an integrated layer
- Domain-specific engineering knowledge from 175 years of industrial experience
Roland Busch captured the strategic vision perfectly:
“From the most comprehensive digital twin and AI-powered hardware to copilots on the shop floor, we’re scaling intelligence across the physical world, so businesses realize speed, quality and efficiency all at once. This is how we scale a once-in-a-generation technology shift into measurable outcomes.”
Why the Large Players Are Leading
What often gets lost in AI discussions: Industrial AI at this level is fundamentally different from fine-tuning a foundation model for specific applications.
The requirements are radically different:
Consumer AI: Helpful, creative, productivity-enhancing – but when it hallucinates, it’s annoying, not catastrophic.
Industrial AI: Controls physical systems, production facilities, mission-critical infrastructure. There’s no room for “interesting mistakes.”
This explains why established industrial companies like Siemens can lead here – and why startups struggle:
What Siemens brings:
- Proprietary industrial data spanning decades
- Deep process and domain expertise
- Trust advantage with mission-critical systems
- Hardware-software integration as end-to-end stack
- Regulatory know-how and safety culture
These are assets that can’t be replicated overnight.
While OpenAI saw its enterprise market share shrink from 50 percent (2023) to 27 percent (2025), it’s clear: Foundation models are commoditizing. Differentiation comes through domain-specific infrastructure, proprietary datasets, and integration capabilities.
Exactly where Siemens has led for decades.
The First Production Deployments
PepsiCo: The Reference Case
PepsiCo is already using Siemens’ Digital Twin Composer in production – collaborating with Siemens and NVIDIA to transform manufacturing and warehouse facilities in the U.S.
Results from initial deployments:
- 20 percent throughput increase in initial deployments
- 90 percent of issues identified before physical build
- Nearly 100 percent design validation virtually
- 10-15 percent reduction in capital expenditure by uncovering hidden capacity
- Design cycles reduced from weeks to days
Athina Kanioura, CEO Latin America and Global Chief Strategy & Transformation Officer at PepsiCo, describes the vision:
“We are deploying the first digital blueprint that reimagines how the supply chain is designed, built, and scaled, a first for the industry. With a unified, AI-powered digital foundation, PepsiCo is building toward a world where every plant and warehouse operates as part of a single, intelligent ecosystem.”
The technology behind it: Using Siemens’ Digital Twin Composer and NVIDIA Omniverse, PepsiCo creates high-fidelity 3D digital twins of their facilities. Every machine, every conveyor belt, every pallet route, every operator path – modeled with physics-level accuracy.
AI agents then simulate thousands of configurations, test system changes virtually, and optimize before a single euro flows into physical modifications.
Commonwealth Fusion Systems: Accelerating Nuclear Fusion
Another remarkable use case: Siemens is working with Commonwealth Fusion Systems to accelerate the development of commercial fusion reactors.
CFS is building the SPARC prototype in Massachusetts (currently 70 percent complete). Using Siemens technology, they’re creating a digital twin of the entire facility.
The promise: Compress years of manual experimentation into weeks – through continuous simulation and data analysis virtually before conducting tests on the physical machine.
Erlangen 2026: The First AI-Driven Factory
The most concrete signal: Siemens Electronics Factory in Erlangen will become the “world’s first fully AI-driven, adaptive manufacturing site” in 2026.
The “AI Brain” concept:
- Continuous analysis of digital twins
- Virtual testing of improvements
- Automatic implementation of validated changes on the shop floor
- Autonomous optimization in real-time
The fundamental shift: From reactive problem-solving to proactive adaptation. The factory optimizes itself before problems emerge.
What This Means for Industry
At KION, I observe how rapidly this shift is occurring. The strategic question is fundamentally changing:
From: “Where do we deploy AI?”
To: “Do we have the infrastructure and data foundation for AI-driven operations?”
Because industrial AI in production requires:
✓ Domain-specific data aggregated over years – not just access to public datasets
✓ Deep process expertise – understanding how production really works
✓ End-to-end integration – not just API calls, but complete stack
✓ Hardware-software control – physical and digital layers synchronized
✓ Trust in mission-critical systems – track record over decades
This is exactly the combination that creates barriers to entry for new players.
The Nine Industrial Copilots
Beyond the Operating System, Siemens announced nine new Industrial Copilots bringing intelligence across the entire industrial value chain:
Teamcenter Copilot: Optimizes product data navigation, reduces errors, accelerates time-to-market
Polarion Copilot: Automates compliance, enables faster regulatory approvals
Opcenter Copilot: Transforms manufacturing processes, increases efficiency and reduces costs
These copilots – along with Siemens’ growing portfolio of AI solutions – are available on the Siemens Xcelerator Marketplace. For companies of every size.
The Outlook: 2026 and Beyond
Siemens isn’t showing 2030 vision. They’re showing 2026 reality.
The shift is happening now:
- From experimentation to execution
- From AI as feature to AI as operating system
- From pilot metrics to business impact
Industrial AI functions fundamentally differently than consumer AI. The deployment complexity is higher. The integration deeper. The requirements for trust and reliability mission-critical.
But the results are measurable. And they scale.
The Question for the Mittelstand
What concerns me most: How do we translate these enterprise deployments to the German Mittelstand?
Not everyone has Siemens’ budget. Not everyone has decades of proprietary industrial data. Not everyone can afford hundreds of AI engineers.
But the principles apply everywhere:
- AI must be integrated into processes, not stacked on top
- Domain expertise beats generic solutions
- Integration matters more than features
- Trust and reliability are non-negotiable
The exciting question is: Which partner ecosystems, which platforms, which approaches will enable mid-market manufacturing companies to benefit from this industrial AI revolution?
Because one thing is clear: The gap between those with AI-driven operations and those still stuck in pilots will grow exponentially in 2026.
About the Author: Tim is Director IT-Business Partnering EMEA & Strategic AI Lead at KION Group, where he builds the AI strategy for a €13 billion industrial corporation.