Siemens, the German technology giant with 320,000 employees worldwide, is leveraging artificial intelligence to transform industrial manufacturing through predictive maintenance and worker assistance tools. According to Peter Koerte, Siemens’ Chief Technology Officer and Chief Strategy Officer, the company has been investing in AI for approximately 50 years and now employs around 1,500 AI specialists working on industrial applications.
The company’s AI strategy addresses critical challenges facing the industrial sector, including security regulations, environmental sustainability, and skilled labor shortages. Koerte emphasizes that industrial AI must be “safe, reliable, and trustworthy” to meet the demanding requirements of manufacturing environments.
Siemens’ flagship AI product, Senseye Predictive Maintenance, integrates with manufacturers’ data sources to analyze machinery performance and predict maintenance issues before they occur. The results have been impressive: companies using the platform have achieved 40% reductions in maintenance costs, 55% increases in maintenance staff productivity, and 50% decreases in machine downtime.
Real-world implementations demonstrate the technology’s impact. BlueScope, an Australian steel company, deployed Senseye in 2021 to minimize downtime across its plants. The system’s IoT sensors detect abnormal equipment vibrations early, preventing costly breakdowns. Similarly, Schaeffler Group, a German automotive supplier, implemented Siemens’ Industrial Copilot—a generative AI assistant that helps engineers generate code faster, automate repetitive tasks, and reduce errors.
Industrial Copilot represents Siemens’ move into generative AI for manufacturing. The tool enables engineers to use verbal commands in any language to create work orders, breaking down communication barriers across global operations. This “human-machine collaboration” helps companies address workforce shortages while maintaining competitiveness.
Siemens collaborates with major tech partners including Google, Microsoft, Nvidia, Amazon Web Services, and Meta to develop these industrial AI solutions. The company’s approach focuses on three key areas: predictive maintenance, worker assistance technology, and generative product design.
Looking ahead, Siemens is developing AI systems that can process computer-aided design data through large language models to automatically generate product variations. While still in early development, this technology could revolutionize how design engineers, particularly in the automotive sector, create and iterate on products.
Key Quotes
What’s most important for AI is that in the industrial context, it needs to be safe, it needs to be reliable, and it needs to be trustworthy
Peter Koerte, Siemens’ Chief Technology Officer and Chief Strategy Officer, emphasized the unique requirements for AI in industrial settings, where failures can have serious safety and financial consequences, distinguishing industrial AI from consumer applications.
We believe if we can take data from the real world, simulate it, understand it in the digital world, we can be much faster for our customers, and our customers can be more competitive, more resilient, and more sustainable
Koerte explained Siemens’ digital twin philosophy, where real-world industrial data is analyzed in virtual environments to accelerate innovation and improve customer outcomes across competitiveness, resilience, and sustainability metrics.
AI breaks down barriers and democratizes many of the technologies because we take the complexity out of them
Koerte described how Industrial Copilot enables workers to use verbal commands in any language to create work orders and manage equipment issues, removing technical and language barriers that traditionally limited technology adoption in global manufacturing operations.
Our Take
Siemens’ AI implementation offers a masterclass in enterprise AI adoption done right. Unlike many companies experimenting with AI for marginal gains, Siemens has identified high-impact use cases where AI directly addresses existential challenges: equipment downtime costs billions annually, and skilled labor shortages threaten manufacturing competitiveness globally.
The 50-year investment timeline is particularly instructive—it reveals that today’s “AI revolution” in industry builds on decades of machine learning, data infrastructure, and domain expertise. Companies cannot simply bolt on generative AI and expect transformation; they need foundational data capabilities and operational knowledge.
The partnership ecosystem with Google, Microsoft, Nvidia, AWS, and Meta demonstrates that even industrial giants recognize they cannot build everything in-house. This collaborative approach, combining Siemens’ industrial expertise with tech companies’ AI capabilities, may become the dominant model for specialized AI applications across sectors. The measurable results provide ammunition for AI advocates facing budget scrutiny in uncertain economic times.
Why This Matters
This case study illustrates how established industrial companies are successfully deploying AI beyond the hype to solve real operational challenges. Siemens’ 50-year AI investment demonstrates that industrial AI adoption is not a recent trend but an evolving discipline that’s now reaching maturity with generative AI capabilities.
The quantifiable results—40% cost reductions and 55% productivity gains—provide concrete evidence that AI delivers measurable ROI in manufacturing environments, countering skepticism about AI’s practical value. This matters for the broader AI industry as it shifts focus from experimental projects to production deployments that impact bottom lines.
The emphasis on safety, reliability, and trustworthiness highlights a critical distinction between consumer AI and industrial AI, where failures can have serious safety and financial consequences. This sets important precedents for AI governance and quality standards across sectors.
Furthermore, Siemens’ approach to addressing workforce shortages through AI augmentation rather than replacement offers a model for how companies can navigate labor challenges while maintaining human expertise. As skilled worker shortages intensify globally, this human-machine collaboration framework could become essential for industrial competitiveness and economic resilience.
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