- Siemens uses AI to tackle industrial challenges like safety and workforce shortages.
- Siemens says its AI tools, such as Senseye, boost productivity and reduce costs for global clients.
- This article is part of “CXO AI Playbook” — straight talk from business leaders on how they’re testing and using AI.
Siemens is a German technology company that operates in many sectors, including industry, infrastructure, transportation, and healthcare. It has about 320,000 employees worldwide.
Situation analysis: What problem was the company trying to solve?
The industrial sector faces several challenges, including security and safety regulations, environmental sustainability, and a shortage of skilled experts. Peter Koerte, Siemens’ chief technology officer and chief strategy officer, said the company aims to solve many of these issues with artificial intelligence.
“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,” he told Business Insider. Siemens, which has been investing in AI for about 50 years, offers several industrial AI products that help manufacturers across industries, such as automotive and aerospace, to predict maintenance issues and improve worker productivity using data.
“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 said.
Key staff and stakeholders
Koerte said Siemens works with a number of tech partners on its industrial AI products and services, including Google, Microsoft, Nvidia, Amazon Web Services, and Meta. The company has about 1,500 employees with AI expertise who work closely with these tech companies, and Siemens’ internal product development teams are also involved.
AI in action
Siemens’ industrial AI work focuses on predictive maintenance, technology to assist workers, and generative product design.
One product is Senseye Predictive Maintenance, a tool that integrates with a manufacturer’s data sources and uses AI to analyze the information. The company said the platform provides insights into how well machinery, tools, and other infrastructure are running. The tech can also help predict maintenance issues, which increases productivity and helps companies speed up the adoption of technology across their businesses.
Courtesy of Siemens
Recently, Siemens debuted Industrial Copilot, a generative AI-powered assistant for engineers in industrial environments. The assistant can generate code automatically, identify problems quickly, and provide advice to support engineering tasks, such as troubleshooting equipment maintenance. The company said the tool can boost “human-machine collaboration” and enable companies to address workforce shortages while staying competitive.
Koerte said that when Industrial Copilot notifies a worker of an issue with equipment or software, that employee can use verbal commands in any language to create a work order, which is automatically sent to a team in a different country to take action to solve the issue. “AI breaks down barriers and democratizes many of the technologies because we take the complexity out of them,” he said.
Did it work, and how did leaders know?
Siemens found that companies using Senseye Predictive Maintenance have reduced maintenance costs by 40%, increased maintenance staff productivity by 55%, and decreased the amount of time a machine is unavailable for maintenance by 50%.
The Australian steel company BlueScope implemented the predictive maintenance platform in 2021 to minimize downtime across its plants, increase operating time, improve the rate at which it can produce products, and lower costs. Together, Senseye and BlueScope’s IoT sensors can detect abnormal vibrations in equipment early, preventing maintenance problems and saving the company money.
Schaeffler Group, a German automotive and industrial supplier, augmented a production machine with Industrial Copilot. Its engineers are now able to generate code faster for programmable logic controllers, the devices that control machines in factories. Siemens said the technology is helping Schaeffler Group automate repetitive tasks, reduce errors, and free up engineers for “higher-value work.”
What’s next?
Koerte said Siemens continues to research and develop new use cases for AI.
The company is working on a project that feeds computer-aided design data, such as models and digital drawings, into large language models and prompts it to create products.
The project is still in the early stages of development, but Koerte said it could enable design engineers, particularly in the automotive sector, to create more product variations and produce higher-quality items faster.
