Europe&#8217;s Industrial AI Race: Leveraging Data and Expertise for Global Leadership
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<aside>(This article was generated with AI and it&#8217;s based on a AI-generated transcription of a real talk on stage. While we strive for accuracy, we encourage readers to verify important information.)</aside>
<img decoding="async" src="https://web-summit-engine.imgix.net/6nmm6zjqq13ylzlyl3z5n7oh5618?auto=format&amp;fit=fill&amp;fill=solid&amp;fill-color=white&amp;w=600&amp;h=600&amp;fm=jpg&amp;dl=Peter Koerte.jpg" alt="Peter Koerte" align="left" hspace="10" width="200" style="float:left;margin-right: 15px">
With global AI investments reaching $2.7 trillion, Mr. Peter Koerte, Member of the Managing Board of Siemens AG, asserted at Web Summit Lisbon 2026 that Europe can lead in industrial AI. He emphasized focusing on the application layer, leveraging Europe&#8217;s manufacturing expertise, industrial data, and skilled workforce. This aims to make &#8220;AI made in Europe&#8221; a global standard, built on trust and accountability.

Siemens&#8217; 60-year-old Erlangen electronics factory serves as a blueprint for industrial AI. Here, AI enhances human judgment and provides comprehensive operational insights. AI agents improve production planning by generating trusted data and alternative scenarios, validated against a digital twin, leading to 20% savings in planning time and boosting competitiveness.

An innovative AI engineering agent streamlines automation setup, cutting development time from days to hours by orchestrating workflows and freeing engineers from repetitive tasks. AI also accelerates product development; digital twins allow rapid simulation and inspection, reducing physical testing and speeding up innovation cycles.

In robotics, AI enables automation of complex, unpredictable tasks. Mr. Koerte showcased robots packaging soft or transparent materials, a challenge due to unpredictable object behavior. AI helps robots anticipate movement, adjust grip in real-time, and automate these processes, marking a significant advancement in &#8220;physical AI.&#8221;

These AI technologies extend to critical infrastructure. In buildings, AI optimizes energy consumption. For rail mobility, AI predicts maintenance issues, extending train service life. Data centers, vital for AI, also benefit; an AI model in Estonia reduced cooling energy requirements by an impressive 40%, demonstrating AI&#8217;s recursive efficiency.

Europe faces hurdles: slow permitting, fragmented regulations, and lagging AI infrastructure. Mr. Koerte advocated for smart, industry-specific standards to foster growth. AI can also mitigate its environmental impact, such as noise from data centers. Physics-based digital twins simulate acoustics before construction, allowing design adjustments to keep noise within community limits.

Europe&#8217;s high-quality industrial data is crucial, yet 80% remains in silos. For industrial AI, this data must be exceptionally accurate and reliable, as errors can have immediate, irreversible consequences. Mr. Koerte stressed that AI model producers should be held accountable for safety and accuracy, similar to aviation or pharmaceuticals.

To unlock this data, Siemens is developing the world&#8217;s first industrial foundation model, built on design, simulation, and manufacturing data. Recognizing the need for collective effort, Mr. Koerte emphasized critical alliances and ecosystems for data sharing, citing Siemens&#8217; alliance with machine tool manufacturers. Europe&#8217;s leadership hinges on collaboration and strategic focus.

The race for industrial AI: can Europe win?

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