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How Can AI Be Used Effectively in Mechanical Engineering?
AI is no longer a promise for the future in mechanical engineering. It takes over routine work, accelerates analysis, and helps evaluate solution options more quickly. At the same time, one thing remains true: not everything can be automated in a meaningful way. Where safety and repeatable quality matter, proof wins - not speed.
The real question is therefore not if but where AI - primarily Large Language Models (LLMs) - is already creating impact today, and under which conditions it can reliably fit into the rhythm of industrial processes.
What Are Large Language Models (LLMs)?
When people in industrial contexts talk about “AI,” they are usually referring to Large Language Models (LLMs). You can think of LLMs as language‑savvy assistants: they detect patterns in text and generate the most likely next meaningful expression.
This makes them especially strong at tasks such as:
- Drafting and summarizing
- Structuring documents
- Comparing variations
- Simplifying content (“Explain it shorter”)
But it’s important to keep one thing in mind:
LLMs operate probabilistically — their outputs are based on likelihoods rather than fixed rules. This means the result can vary even when the prompt stays the same. That very characteristic makes them powerful for supportive tasks such as structuring technical documents, generating first drafts, highlighting inconsistencies, or preparing potential solution approaches.
However, whenever reproducibility is required, deterministic methods are essential — clearly defined, repeatable processes like rule‑based validation chains, fixed generators, or traceable logic paths. Together, they can form a system that is both flexible and verifiable.
The key is not choosing one or the other, but orchestrating both in a meaningful way.
How companies are already putting this balance to work — through Digital Twins, predictive maintenance, and automated validation processes — is explored in detail in our 2026 trend report.
Event Insights: AI Live in Duisburg
Packed room, lots of questions: Together with the Niederrheinische IHK and the Zentrum für angewandte KI Duisburg (ZaKI.D)
we spent an entire afternoon at our Duisburg site discussing real‑world AI use cases in mechanical engineering. After two expert presentations, the group moved straight into open discussion: Where do processes get stuck? Where is acceptance still missing? Where do proof requirements and reproducibility become the bottleneck?
One sentence summed up the mood perfectly:
„Artificial intelligence isn't always intelligent, but it's already indispensible.“
(Niklas Kurtenbach)
Three key observations emerged from the discussions:
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AI already reduces workload today, especially in areas with higher error tolerance (documentation, analysis).
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Technology is rarely the bottleneck - unclear roles, processes, and data access usually slow things down.
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Agentic AI is gaining momentum: systems that make autonomous decisions and optimize processes.
The debates also highlighted this: organizations are increasingly ready to move from “Let’s try it out” to well‑governed applications. Especially with the rise of Agentic AI, the need for clear responsibilities, approvals, and fallback levels is growing.
AI in Mechanical Engineering
LLMs in Engineering Workflows
Integrating AI technologies is transforming how engineering processes are designed and optimized. Companies are embracing innovative solutions that not only boost efficiency but also challenge traditional methods.
AI is especially valuable where outcomes may vary and time matters. In many organizations, AI is already summarizing meetings and emails, structuring technical documents, or identifying inconsistencies in specifications. In engineering, it provides early assessments and alternatives that help fuel discussion. In all these cases, AI reduces manual effort without replacing critical decision‑making.
AI in Production
As processes move closer to production, error tolerance drops—changing the nature of AI’s role. Here, AI may support the optimization of process parameters or act as a co‑engineering “buddy”—a digital co‑engineer that participates in development, proposes ideas, and offers decision support. The actual evaluation and approval, however, remain the responsibility of expert teams.
In quality and safety inspections, error tolerance decreases even further. While non‑critical checks can be automated and accelerated, safety‑relevant inspections require reliable proof logic.
Implications for Mechanical Engineering
The closer AI gets to safety‑critical functions, the more important predictability, audits, and clear approvals become - especially wherever code is generated. “The lack of reproducibility is a major issue, especially in the generation of source code,” said Niklas Kurtenbach, expert for automation software and connected production systems at SCIO Automation. “Deterministic tools that generate PLC code already exist. In the future, AI will likely operate within these tools rather than replace them.”
This perspective points toward a hybrid approach: LLMs accelerate analysis, documentation, and variation creation; proof in critical testing remains deterministic.
Want to learn more about AI and machine learning in automation?
Conclusion
AI is a tool, not a miracle cure; but it has already become part of everyday engineering. For companies, that means: the time to shape its use is now.
In practice, LLMs already assist with preparation and documentation, copilots suggest process parameters, flag anomalies, and make information available directly at the HMI. Just like smartphones, AI will find its place and today, we have the opportunity to design processes, roles, and proof workflows so that they remain reliable and scalable.
Looking ahead, a pragmatic vision emerges: multiple agents and copilots could soon take over small tasks at our workplaces—documenting, suggesting, checking - and create more time for what truly matters.
Start shaping your automation strategy now - reach out to us, and let’s build future‑ready processes together.
