The News: Siemens and Microsoft announced in late October that they have introduced Siemens Industrial Copilot, a generative-AI based assistant. The assistant is designed to allow users to generate, optimize, and debug complex automation code, as well as shorten simulation times that used to take weeks down to just minutes.
You can read more details about the partnership on the Microsoft website.
Siemens and Microsoft Introduce Siemens Industrial Copilot
Analyst Take: Microsoft and industrial automation and controls vendor Siemens introduced Siemens Industrial Copilot, a generative AI-powered assistant that is designed to enhance the development, optimization, and debugging of complex industrial automation code and drastically reduce the time to create simulations.
Siemens Industrial Copilot ingests automation and process simulation information from Siemens’ open digital business platform, Siemens Xcelerator, and then enhances it with Microsoft’s Azure OpenAI Service. End customers can maintain full control over their proprietary data, which is not used to train underlying AI models. The copilot tool is designed to improve productivity and efficiency of maintenance staff by using natural language to access and interact with detailed repair instructions and enable engineers with quick access to simulation tools.
Automotive Industry Use Cases Will Be Joined by Other Industry-Focused Copilots
According to the release, Schaeffler AG, an automotive supplier, is among the pioneers of generative AI adoption in the engineering phase, using it to generate reliable code for programming industrial automation systems such as robots. Schaeffler intends to incorporate Siemens Industrial Copilot during its operations, aiming to significantly reduce downtimes, and plans to deploy the technology for their clients at a later stage.
Schaeffler CEO Klaus Rosenfeld noted in the press release that the Siemens Industrial Copilot will be deployed to help its team work more efficiently, reduce repetitive tasks, and unleash creativity.
Generative AI Used to Facilitate Virtual Collaboration
Generative AI technology is also being used by Microsoft and Siemens to enhance virtual collaboration across teams. The companies note that Teamcenter for Microsoft Teams will be generally available beginning December 2023, with the new application leveraging generative AI to connect functions across the product design and manufacturing lifecycle such as frontline workers to engineering teams.
Generative AI will be used to link Siemens’ Teamcenter software for product lifecycle management (PLM) with Microsoft Teams to make data more accessible for factory and field service workers. The goal is to enable workers who do not have access to PLM tools today to contribute to the design and manufacturing process more easily as part of their daily work.
Using Copilots Across Additional Use Cases
Siemens and Microsoft indicate they will continue to work together to build additional copilots for the manufacturing, infrastructure, transportation, and healthcare industries. The expansion of use cases into other verticals might well be the catalyst for reaping additional value from generative AI at an organizational level. Many of the initial generative AI use cases that are focused on worker productivity – content summarization, content generation, and next-best-action recommendations – likely will provide incremental value to organizations that are able to roll out the technology as an assistant to human workers.
It appears, however, that generative AI use cases within backend functions, such as PLM, might result in greater efficiency gains. Complex, process-based functions such as PLM have traditionally required workers to be trained in the domain-specific vernacular to interact with the software as well as effectively collaborate with colleagues.
By using generative AI technology such as Siemens Industrial Copilot, it will be easier for other types of workers to interact, thereby improving collaboration and enabling even experienced workers to conduct product design iterations more efficiently at scale. Because any improvement in efficiency key performance indicators (KPIs), such as time to market, will be able to be attributed to the use of generative AI technology, getting buy-in from operations managers and the purchasing suite might be simpler.
Ultimately, although many office-productivity generative AI uses cases are garnering significant attention, using this technology to address tasks that have a greater payback in efficiency likely will help convince enterprise buyers to infuse it across less visible parts of the business.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
