comparative results without an engineer having to configure every simulation individually. SimScale’ s platform also automates simulation reporting, making it easier to analyse, compare and share results across the engineering team.
The important distinction is that this does not remove engineering judgement from the process. Engineers still need to understand the problem, define appropriate constraints and determine whether the results make physical sense. The opportunity is to reduce the repetitive work around that process so engineers can spend more time interpreting results and making design decisions.
Turning previous engineering work into reusable knowledge
There is another opportunity that is potentially just as important: engineering memory.
Engineering teams accumulate knowledge continuously. A simulation from an earlier project may contain an approach or insight that could help solve a problem months or years later. The challenge is making that knowledge easy to find and reuse.
As part of this programme, the agents will capture simulation knowledge from previous projects to create a searchable engineering memory. The aim is to help engineers reuse proven approaches and preserve expertise as the business grows.
This could change the value of simulation data. Rather than each project existing largely as a record of a particular engineering problem, previous work can become a body of knowledge that engineers can draw on when tackling new ones.
AI still has a blank-sheet-of-paper problem
The bigger question, however, is not simply what AI can automate. AI currently has what I think of as a“ blank sheet of paper problem” in engineering. We know the technology can automate certain tasks, but engineering organisations are still working out where it creates the greatest practical value. This distinction matters.
The objective should not be to insert AI into every engineering workflow simply because the technology is available. We need to understand which activities benefit from automation, where human engineering judgement remains essential, and where AI can genuinely improve the speed or quality of engineering decisions.
That is one reason our work with SimScale’ s Engineering AI is not focused solely on productivity. We also want to learn where AI agents deliver the greatest value for our engineering organisation and establish best practices that can guide future adoption.
The engineer’ s role will evolve
AI’ s impact on engineering may therefore be less about replacing individual engineering tasks and more about changing how engineers interact with the design and validation process.
If agents can configure and execute more routine simulation work, engineers can potentially explore more design options and focus more of their attention on interpreting what those simulations tell them. This makes engineering expertise more important, not less. Faster analysis is only useful if someone understands the physics behind the result, recognises when something looks wrong and knows which question to ask next.
We are still early in understanding exactly how that relationship will develop. That is precisely why engineering teams need to start learning now. For us, deploying Engineering AI agents is not simply about solving today’ s engineering challenges faster. It is an opportunity to understand how AI could reshape engineering over the coming years and, importantly, where it can genuinely make engineers better at what they do.
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