PROSTEP's expertise now includes artificial intelligence

Industry's growing interest in AI is primarily economic. According to forecasts from McKinsey Global Institute, generative AI technologies alone, which include ChatGPT and DALL-E, could result in an annual increase in productivity of up $4.4 trillion. This is roughly equivalent to Germany's gross national product. In addition to marketing and sales, the use cases on which generative AI will have the greatest impact include R&D and software engineering.

Leading carmakers such as Mercedes-Benz are using generative AI to develop chatbots that help engineers access existing information more easily and quickly, among other things. Other conceivable use cases for AI include eliminating inconsistencies in the data structures of different IT systems that should actually be identical, checking the upward and downward compatibility of software, and determining which software goes with which electrical/electronic hardware (E/E HW). Read the interesting interview with Ralf Rentschler on this subject.

Not all of these use cases can be handled with generative AI. For example, what Mercedes-Benz has in mind also requires technologies such as machine learning, deep learning and computer vision. That is why our customers today expect us to be familiar with the different AI technologies and to know which combination of technologies can provide the best support for which use cases.

PROSTEP has been dealing with AI in a number of different customer projects for some time now. We have put together an AI incubator team at PROSTEP and tasked it with defining a holistic AI strategy for the company. The aim is to increase the visibility of this subject area and give it a boost. This strategy will address three dimensions. Firstly, the use of AI for our internal processes, secondly, expanding the range of services we offer to include new AI offerings and AI-specific expertise, and thirdly, the integration of AI-based tools in our existing product portfolio.

Our strategic goal is for AI to become an integral part of our expertise and for its use to be understood as a self-evident part of the work we perform.

The incubator team's task is to clarify how we can use AI to make our processes in internal cross-cutting departments, such as IT, HR and Finance & Controlling, and in software development more efficient and cut costs. But even more important for us as a leading PLM consulting and software company is the question of which AI-related services we want to offer our customers in the future, which tools we should use to do this, and which new skills our employees will need in order to provide these services. I think that, in the future, we will all need to have or to develop a fundamental understanding of what can be achieved with AI today and what will be possible tomorrow.

For many companies, our software solutions are a prerequisite for their ability to collaborate across domain and company boundaries. They are the key to end-to-end digitalization and to ensuring traceability in heterogeneous system landscapes. Here we are faced with the question of which additional AI-based functions we can integrate, for example to increase the level of automation for the cross-system and cross-domain linking of data or make searching for information easier. The interview with Ralf Rentschler gave me a lot of food for thought.