AI does not replace systems engineering: it shifts the work
At Conquering Complexity, I spoke with Prof. Dr. Thomas Meenken about the current state of AI in Systems Engineering. There, he, like I, held a workshop on the topic. As a professor and entrepreneur, his insights are illuminating for readers of SE-Trends and demonstrate the AI-driven shift away from manual analysis towards control and orchestration.
Below is the video recording of the conversation and a summary.
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About Prof. Dr. Thomas Meenken
Thomas Meenken is Chief Technical Officer at Schlateck LLC and Professor of Scale Technology at the Deggendorf Institute of Technology. He advises companies on the implementation of AI in R&D and works on accelerating development processes through data-driven methods. Under ai-powered-engineering.com He helps companies implement AI in engineering. Here's the full interview to watch:
Three statements stand out and show where Systems Engineering is currently changing.
AI unlocks legacy data without onboarding
Thomas confirmed a trend that we are also seeing in other domains. AI makes large, established data sets directly and immediately usable. Requirements and architectures emerge from legacy data without an engineer needing years to get up to speed.
AI can do that right out of the box.
Prof. Dr. Thomas Meenken
The consequence lies in a shift of work. The creation of artifacts loses importance. The evaluation, selection, and classification of results become the central task. Systems Engineering remains necessary because direction, context, and quality assurance do not arise automatically.
Standards become machine-readable
Thomas described norms and standards as one of the biggest leverages in engineering. AI filters the relevant requirements from extensive rule sets and checks requirements, models, and other artifacts for deviations.
AI answers the question: What about this huge standard actually concerns my product?
Prof. Dr. Thomas Meenken
This reduces search effort and interpretation errors. At the same time, responsibility remains with the user. The timeliness and legal use of standards must be consciously managed. AI does not replace a compliance decision, but it greatly accelerates the analysis.
Broad capability trumps individual tools.
Thomas emphasized that individual tools are not enough. Companies must empower their employees to work with AI. Initial productive results emerge after a short onboarding period and give the team a sense of what is possible.
They need one or two days of training, then they can do the first things.
Prof. Dr. Thomas Meenken
Broad application creates an understanding of possibilities and limitations. Only then can informed decisions be made about platforms and investments. Without this experience, AI initiatives remain isolated and have no effect.
Conclusion
AI is shifting Systems Engineering from creation to control. Models, requirements, and analyses emerge faster. The bottleneck lies in selection, integration, and evaluation. Companies gain speed when they accept this shift and align their organization accordingly.
