What role does AI play in your engineering solutions?

Artificial intelligence is becoming a growth driver—especially in industrial applications. Our goal is to make AI usable within existing solutions according to the specific requirements of industry and to automate the engineering process. AI leadership and software expertise, combined with deep industry knowledge, are the most relevant levers for industrial companies’ growth and competitiveness. At Eplan, we have been working with generative AI in engineering for years. One thing is clear: recurring tasks can be greatly optimized with AI. The possibilities are extremely diverse, ranging from intelligent error analysis to the automatic generation of a mounting panel layout.

How can AI accelerate the engineering process?

One factor is the reduction of planning time. With AI-supported tools, developers can simulate different scenarios within minutes—scenarios that previously would have taken days or even weeks. This not only saves time but also significantly improves the quality of results. And the goals are ambitious: in the future, AI agents will act as digital assistants to help engineers work more efficiently and precisely. They will take over time-consuming routine tasks, enabling development departments to focus on creative and strategic challenges. Together with Siemens, for example, we are working on standardizing data models to further improve interoperability and data consistency. Things become even more interesting when AI systems can interact autonomously across applications. This will take the benefits for our customers to an entirely new level. However, hurdles still need to be removed, and cloud-to-cloud connections must be established.

Are there concrete application examples?

Using the Microsoft Azure OpenAI Service, we tasked the Eplan Copilot at Hannover Messe with generating a mounting panel layout live on site. The demonstrated path to the 3D layout is fully automated and takes just a few seconds. The technological basis for this automatic generation is a well-defined bill of materials for all components to be installed—and the Eplan Copilot. In the use case presented, it interacts directly with the systems—integrated into the Eplan platform. The AI is also directly connected to the Eplan Data Portal, which provides more than two million component data sets plus another two million configurable data sets. This is just one example—we have presented further use cases, including with Siemens. With the help of the Siemens Engineering Copilot, code blocks for programmable logic controllers can be generated. The Industrial Copilot is also able to independently implement changes in the Eplan project.

How is AI changing your software tools?
Artificial intelligence makes software tools more intelligent in principle, because it provides functions that simplify engineering and other tasks. Repetitive work can thus be handed over to the AI, while users focus on the truly value-adding tasks. In this respect, AI not only changes the tools but also the way of working. Looking at the integration of language models, this also changes the way users interact with the software. With agents, AI can solve tasks across applications. This leads to deeper connections—or even the merging—of systems.

What data foundation is necessary for this?
The basis for any automation, including the use of AI, is first-class data. That is why we work intensively with partners, associations, and industry to put initiatives such as Eclass or the Asset Administration Shell into practice. The goal is to create a unified data foundation that ensures optimal data availability. This challenge matches the standards Eplan set years ago with the Eplan Data Standard (EDS)—fully described, standardized product data.

How do you support customers with implementation?
We generally support our customers in implementing software—whether it is AI-supported or not. For this purpose, we offer comprehensive training, consulting services, and targeted onboarding for new customers. Currently, however, we are presenting our AI development in use-case status. We are actively talking with customers and exchanging ideas on how AI can simplify processes in engineering. The step toward technical implementation or integration has not yet been completed—but we are actively addressing the topics in our agile development and exploring all possible opportunities.

What role does Explainable AI play?
That is exactly our goal in our own development work. Explainable AI is technically challenging, but it reflects our ambition to be as transparent as possible and provide users with all the possible background information behind AI decisions. After all, we operate in an industrial environment, and it is crucial that only valid, correct data is used in engineering—data that does not jeopardize safety in later production and manufacturing—keyword: machine downtime.

Which partners are involved?
We are in discussions with several global players. With Siemens, for example, we are intensively exploring the opportunities of end-to-end integration that digitizes and automates the entire engineering process. Already today, with the Siemens Industrial Copilot for Engineering, code blocks for programmable logic controllers can be generated. And of course, we are developing further use cases based on the Microsoft Azure OpenAI Service, including AI-based generation of mounting panel layouts.

How do you ensure data security and compliance?
For our AI services, we apply the same high requirements and standards that we generally apply to our product developments and services. It is important to us that customers using our solutions can rely on valid data—both in engineering and in operation.
Data sovereignty plays a key role, and our experience shows that customers are reluctant to give this up. This challenges us once again to implement the highest standards of data security. This, in turn, reflects our own claim: we only bring solutions to market that are tried and tested, and that give our customers confidence and security in choosing the right partner.
On compliance: we continuously monitor our systems for security requirements. Naturally, we adapt our solutions to changing conditions—whether in terms of standards, such as ISO certifications, or new legislation such as the AI Act.

Where is engineering with AI heading?
AI will simplify and accelerate engineering, but it will also fundamentally change the way software is used and processes are structured in the future. Users will increasingly delegate repetitive processes to the system. A major advantage lies in improved collaboration within interdisciplinary teams. AI can link various disciplines, companies, and tools, provide predictive suggestions and analyses, and detect errors at an early stage. This saves engineers time and improves the quality of their work.