Europe as a breeding ground for industrial AI startups

The investment boom around industrial AI has a clear rationale. Companies across sectors — from pharmaceutical manufacturing to logistics to aviation — are looking for ways to increase efficiency under pressure from rising costs and a shortage of skilled labor. And generative models offer a path that didn't exist three years ago. "New generative approaches bring solutions with digital simulations and a level of efficiency that was previously unattainable," says Catherine Crump, director of WIRED Consulting and one of the judges of the Industrial AI Awards 2025. And it's not about simply speeding up existing processes. "Some solutions open up entirely new procedures and ways of working," adds Crump. That's where the real potential lies — generative AI doesn't just optimize what we already know, but enables testing variants that a human engineer would never think of.

How LLMs are changing industrial design and simulations

One area where language models are making the biggest impact is the design phase of manufacturing. Engineers today use AI to analyze enormous volumes of both structured and unstructured data — from technical specifications to research papers to historical failure data. The result is new perspectives on alternative materials, performance simulations, and identification of bottlenecks in supply chains. "I see a world where supply chains are dramatically reformatted and existing methods will be significantly disrupted," predicts Jon Nieman, vice president for investments at Abu Dhabi-based AI company G42. Concrete data confirms this vision. At Hannover Messe 2026, Siemens presented how manufacturers using digital twins and AI simulations are shortening production ramp-up by up to 40 percent. In the field of machining, AI enables 100% collision-free testing — a simulation that can confidently detect a potential error before damage to expensive equipment occurs.

Sustainability as a side effect

An interesting bonus of deploying generative AI in industry is the environmental impact. According to Meike Neitz, founder of startup consulting firm embassidy, up to 80 percent of the environmental footprint of an industrial product is determined already during its design phase. "AI-powered solutions can play a huge role in energy efficiency, electrification, minimizing material waste, and process optimization," emphasizes Neitz. A concrete example: redesigning industrial robotic grippers using AI combined with additive manufacturing (3D printing) leads to an 82 percent reduction in carbon emissions per robot. "The industrial domain is still one of the world's largest sources of greenhouse gases," reminds Neitz.

Domain expertise instead of generic solutions

At the same time, Siemens warns against naive optimism. In the flood of AI startups trying to break into the industry, experts say only those that demonstrate deep knowledge of a specific sector will succeed. "There's a huge AI hype and a flood of startups innovating with this technology. Those that stand out can show that their unique AI solution addresses a clear need and leads to better results," explains Crump from WIRED Consulting. Samuel Schuler, managing director of Reimann Investors Venture Management, goes even further: "Prioritize building deep understanding of real industrial workflows over customizing AI solutions. Domain-specific insights often outperform generic AI strategies." This emphasis on sector expertise is also confirmed by Alexander Oelling, CDO of ISAR Aerospace: "In discussions, I like to listen for whether founders spontaneously mention specific challenges of target industries that wouldn't appear in superficial market research."

User first: Why technology alone isn't enough

Another key insight from Siemens' analysis is the focus on the end user — the person who will actually work with the AI tool on the shop floor or in the production hall. "Be obsessed with the people on the ground for whom you're building your solution. Build for them, not for their bosses. Stay in touch with them, gather feedback, learn their pain points, their work reality, and their processes," advises Neitz to startup founders. Oelling from ISAR Aerospace emphasizes the value of early pilot implementations: "Industrial AI startups that secure early pilot deployments — even on a limited scale or partially subsidized — build credibility that theoretical projects can't even come close to."

Siemens Xcelerator: An ecosystem where models meet industry

Siemens has bet on this vision with a concrete platform. Siemens Xcelerator is an open digital marketplace that today brings together over 1,700 solutions from more than 400 certified partners. Companies can find everything from IoT hardware to software for digital twins to AI modules for predictive analytics. "Leading the industrial AI transformation is not something a single company can do alone. That's why we're building a thriving industrial AI ecosystem that requires strong collaboration between customers, industry leaders, startups, vendors, partners, and developers," says Linda Krumbholz, senior vice president of Siemens Xcelerator Ecosystem & Marketplace. Part of this strategy is the Siemens Industrial Copilot — a generative AI assistant built on Microsoft Azure OpenAI technology that helps operators and engineers with PLC programming, fault diagnostics, and creating technical documentation. It's a direct demonstration of how large language models are moving from chat windows directly into production halls.

What this means for the Czech Republic

The Czech Republic holds a strong hand in this story. Siemens employs over 10,000 people in the Czech Republic and operates development centers in Prague and Ostrava. The Siemens Xcelerator platform is available on the Czech market, and Czech industrial companies — from Škoda Auto to Třinecké železárny to smaller engineering firms — are increasingly turning to AI tools for production optimization. At the same time, the Czech AI Factory at IT4Innovations in Ostrava has been operating since 2025 — a national node of European AI infrastructure that gives Czech companies and researchers access to computing capacity for training and deploying their own models. Moreover, the EU AI Act, which comes into full effect in 2026, creates a regulatory framework specifically for high-risk AI applications — and industrial automation certainly falls among them. Czech companies will thus have to address not only the technology itself but also certification, transparency, and human oversight of AI systems.

How to choose the right AI solution for industry: 3 recommendations from Siemens experts

1. Start from the problem, not the technology. Don't ask "what can AI do," but "what specific problem do we need to solve." Generic chatbots don't belong on the factory floor — successful solutions address specific pain points such as machine downtime, output quality, or energy consumption. 2. Verify the supplier's domain expertise. As investors from the Siemens ecosystem advise: listen for whether the startup spontaneously mentions the challenges of your industry. If they only talk about model functionality and not about your business, keep looking. 3. Pilot small, evaluate, then scale. Even a limited pilot deployment with real data has more value than a year of theoretical presentations. Siemens Xcelerator offers free three-month trial versions for many solutions — an ideal way to get started without large investments.