GenAI in Engineering: Why Siemens Focuses on Mindset, Not Toolsets

GenAI in Engineering

The world of engineering is undergoing a paradigm shift. While many companies are still figuring out how to integrate generative AI into their processes, Siemens takes a different approach: it's not about the tools — it's about the mindset.

A recent post by Siemens on LinkedIn (Link to the article) illustrates how GenAI is no longer limited to generating images, text, or code. It is now actively reshaping the way engineering is conceived. This blog post is inspired by that article. GenAI isn't just another utility — it's a new kind of cognitive partnership between human and machine.

From Building to Prompting

Traditional engineering follows the pattern: specify – build – test. With GenAI, this shifts to: prompt – co-create – validate. Engineers are no longer limited to code and CAD; they interact through natural language, sketches, or context-rich data that GenAI systems interpret and translate into technical artifacts.

Siemens is embracing this shift in its Xcelerator platform, integrated with Azure AI and OpenAI technology. Notably, this is not about automating existing workflows — it's about opening new dimensions of thought and development.

Engineering Becomes Multimodal

What used to be distinct disciplines — mechanical design, electronics, software modeling — are now converging through multimodal GenAI. Text-to-code, sketch-to-test, requirements-to-simulation: all are becoming possible — if the mindset is right.

This transformation demands new skills: prompt engineering, systems thinking, and above all, critical evaluation of AI output. No matter how advanced the tech becomes, responsibility for safety and functionality remains with the human expert.

A Nod to RAG Systems?

While Siemens does not disclose technical specifics, the structured access to proprietary engineering knowledge (e.g., specifications, models, test data) suggests the use of Retrieval-Augmented Generation (RAG). This architecture combines LLMs with domain-specific databases — a potential key to embedding expertise in regulated, safety-critical environments.

My Takeaway

Engineering with GenAI is not a toolchain issue — it's a leadership challenge. Organizations that empower their teams not just with new tools, but with a new mindset, will accelerate innovation cycles and better handle complex systems.

Siemens demonstrates: GenAI is more than hype and fancy visuals. When implemented thoughtfully, it becomes a catalyst for a new engineering culture — one that embraces embedded AI partnerships within tools to systematically enhance human expertise.

Open question to readers: What mindset does your team need to apply GenAI productively and responsibly?