• Siemens uses AI to address industrial challenges such as safety and labor shortages.
• Siemens says its AI tools, such as Senseye, increase productivity and reduce costs for global customers.
• This article is part of “The CXO AI Playbook” — live conversations from business leaders about how they’re testing and using AI.

Siemens is a German technology company active in many sectors, including industry, infrastructure, transportation and healthcare. It has approximately 320,000 employees worldwide.

Situation Analysis: What problem was the company trying to solve?

The industrial sector is facing several challengesincluding safety and security regulations, environmental sustainability and shortage of skilled experts. Peter Koerte, Siemens’ chief technology officer and chief strategy officer, said the company aims to solve many of these problems with artificial intelligence.

“What’s most important for AI is that in an industrial context it has to be safe, it has to be reliable and it has to be reliable,” he told Business Insider. Siemens, which has invested in AI for about 50 years, offers several industrial AI products which helps manufacturers in various industries, such as automotive and aerospace, to predict maintenance problems and improve worker productivity using data.

“We believe that 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 personnel and stakeholders

Koerte said Siemens is working with a number of technology partners on its industrial AI products and services, including Google, Microsoft, Nvidia, Amazon Web Services and Meta. The company has around 1,500 employees with AI expertise working 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.

A product is Senseye Predictive Maintenancea 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 machines, tools and other infrastructure are working. The technology can also help predict maintenance issues, increasing productivity and helping companies accelerate the adoption of technology in their operations.

Recently, Siemens debuted Industrial Copilota generative AI-powered assistant for engineers in industrial environments. The assistant can generate code automatically, identify problems quickly and provide advice to support technical tasks, such as troubleshooting equipment maintenance. The company said the tool could increase “human-machine collaboration” and enable companies to address labor shortages while remaining competitive.

Koerte said that when Industrial Copilot notifies a worker of a problem with equipment or software, the employee can use verbal commands in any language to create a work order, which is automatically sent to a team in another country to take action to resolve the problem. “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 the leaders know?

Siemens found that companies using Senseye Predictive Maintenance have reduced maintenance costs by 40%, increased maintenance staff productivity by 55% and reduced the time a machine is unavailable for maintenance by 50%.

Australian steel company BlueScope implemented the predictive maintenance platform in 2021 to minimize downtime across its factories, increase uptime, improve the speed 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 issues and saving the company money.

Schaeffler Group, a German automotive and industrial supplier, augmented a production machine with Industrial Copilot. Its engineers can now generate code faster for programmable logic controllers, the devices that control machines in factories. Siemens said the technology helps Schaeffler Group automate repetitive tasks, reduce errors and free up engineers for “higher-value work.”

What happens 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.