Large language models that once wrote emails are now drafting structured text code and explaining alarms to plant technicians. Used with careful review, they can take routine engineering load off teams that are already short of skilled people.
Automation vendors now ship assistants that write, explain and document control software from plain language prompts. This guide covers where they help, where they fail and how to estimate the engineering time they save.
What Is Generative AI in Automation?
Generative AI refers to models, mostly large language models, that create new content such as text, code or images from a prompt. In automation it is used to draft PLC logic, documentation and troubleshooting answers, building on the ideas in AI and machine learning.
Unlike classic machine learning that predicts a number or class, generative AI produces whole answers. That makes them powerful for engineering text, but it also means they can be confidently wrong.
Drives and Controls reports that the Siemens Industrial Copilot became downloadable from the Xcelerator marketplace in summer 2024 after pilot projects. The CEO of machine builder Grenzebach called it a must have for dealing with labour shortages.
5 Smart Uses on the Plant Floor
Structured text is the easiest target because it looks like ordinary code, see PLC programming languages. Graphical ladder logic is harder for text models, though tools are improving.
The same assistants increasingly sit inside PLC, SCADA and DCS platforms. They can read tag lists and project structure to give answers that fit the actual system.
How Generative AI Writes PLC Code
The LLM4PLC research from UC Irvine and Siemens Technology adds a verification loop around the model. Compiler errors and model checking results are fed back so the model can repair its own code.
Testing against a digital twin before download catches logic that compiles but behaves wrongly. That step is essential for any machine that can move or heat.
Where Generative AI Fits Best
Code and comment help inside engineering tools.
Answers from manuals and work orders.
Explain alarms using live context.
Draft shift and incident summaries.
Whether the model runs locally or in a data centre matters for latency and data privacy, as compared in edge AI vs cloud AI. Many plants prefer on premise hosting for project files.
Risks Engineers Must Control
| Risk | What Happens | Control |
|---|---|---|
| Hallucination | Invented instructions or tags | Compile, simulate, review |
| Weak verification | Logic passes but is unsafe | Formal checks and tests |
| IP leakage | Project code sent outside | Private or local models |
| Cybersecurity | Prompt injection or tampering | Access control, audit logs |
| Over trust | Review skipped under pressure | Mandatory human sign off |
Security teams should treat AI tools as new attack surface, alongside the threats listed in types of cyber attacks. Never paste plant credentials or network maps into public chat tools.
Human in the loop review is not optional, even as companies scale up AI and automation infrastructure. Safety related logic in particular must follow the normal verification process, whoever or whatever wrote it.
Hours Saved Estimate
Example:
40 tasks, 6 h each = 240 h of work
30 percent saved = 72 h gross
Extra review effort = 20 h
Net saving = 52 h
Always count the extra review time for generative AI output honestly. If review eats most of the saving, the tool is not yet worth it for that task type.
Engineering Hours Calculator
Track real numbers over a few projects before scaling up. Savings differ widely between code, documentation and troubleshooting work.
- Faster first drafts of code and documents.
- Helps junior engineers learn quickly.
- Captures expert knowledge in answers.
- Eases skilled labour shortages.
- Can hallucinate plausible but wrong code.
- Needs strict verification.
- Data privacy and IP concerns.
- Licence and compute costs.
Start generative AI with low risk tasks like comments and reports, then move to code with simulation. Pair the tools with good PLC programming software for practice.
LLM4PLC Research Paper PDF
Siemens Industrial Copilot Video
Generative AI FAQ
Related Articles
- Artificial Intelligence and Machine Learning
- AI in PLC, SCADA and DCS
- Edge AI vs Cloud AI
- PLC Programming Languages
- Digital Twin in Industrial Automation
External References
- LLM4PLC, UC Irvine and Siemens Technology
- Generative AI Is Now an Industrial Tool, Drives and Controls
- Generative Artificial Intelligence, Wikipedia
What We Learn Today
- Generative AI drafts PLC code, documents and troubleshooting answers.
- Verification, simulation and human review are mandatory.
- Estimate net hours saved after counting review effort.