Industrial AI for the Physical World: Siemens’s Peter Koerte

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Apr 21, 2026

Peter Koerte, Siemens chief strategy and technology officer focused on industrial AI and sustainable tech. He describes how AI quietly upgrades factories, grids, trains, and buildings. He contrasts industrial needs for near-perfect accuracy and proprietary domain data. He discusses data-sharing, predictive maintenance like train-door forecasts, simulation acceleration with partners, and workflow and workforce transformation.

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INSIGHT

Industrial Models Demand Near Perfect Accuracy

Industrial models require far higher accuracy and different data types than consumer models.
Siemens targets 99%+ reliability and trains on time series, CAD, simulation, and domain-specific sensor data rather than web text or LLM corpora.

ADVICE

Start With Industry Ontologies Before Modeling

Build around domain ontologies and specific semantics for each industry before modeling.
In buildings use energy and temperature ontologies; in production focus on quality and throughput parameters tied to domain know-how.

ANECDOTE

Train Doors Are The Operational Bottleneck

Trains illustrate domain insight: doors, not brakes, are the most critical operational component.
Siemens uses motor voltage profiles to predict door failures up to 10 days ahead so operators can preemptively fix them.

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In this episode, Sam talks with Peter Koerte, member of the managing board and chief strategy and technology officer of Siemens, about how industrial AI is quietly transforming the infrastructure that powers everyday life.

While consumer AI grabs headlines, Peter explains how artificial intelligence is improving factories, transportation systems, energy grids, and buildings behind the scenes. The conversation explores what makes industrial AI different — from the need for near-perfect accuracy to the challenge of working with proprietary, domain-specific data.

Peter shares examples like predicting train door failures days in advance, optimizing building energy use, and accelerating complex engineering simulations. Peter and Sam also discuss the importance of domain expertise, the value of data-sharing partnerships across companies, and why transformation is as much about people and workflows as it is about technology. Read the episode transcript here.

Guest bio:

As a member of the managing board, chief strategy officer, and chief technology officer of Siemens, Peter Koerte is responsible for developing the company’s strategy and leading its worldwide research and development activities. His current priorities include accelerating development of innovative sustainable technologies and continuing development of the Siemens Xcelerator business platform.

Koerte previously headed Digital Health, a Siemens Healthineers unit that develops AI-supported diagnostic procedures for health care. He joined the corporate strategy side of the company in 2007 after working for the Boston Consulting Group. Koerte holds a master’s degree in business and engineering from the Karlsruhe Institute of Technology and a doctorate in strategy and international management from the WHU-Otto Beisheim School of Management. He also completed the General Management Program at Harvard Business School.

Me, Myself, and AI is a podcast produced by MIT Sloan Management Review and hosted by Sam Ransbotham. It is engineered by David Lishansky and produced by Allison Ryder.

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