Generative AI for Manufacturing Engineering: What's Actually Useful

Generative AI—ChatGPT, Claude, Copilot, Gemini—has become impossible to ignore. Everyone’s talking about it. Many people are experimenting.

But for manufacturing engineers, what’s actually useful? Beyond the hype, where does generative AI help with real engineering work?

I’ve been watching how engineers use these tools. Here’s what’s proving valuable.

Documentation and communication

This is where generative AI delivers the clearest value.

Technical writing

Engineers often need to produce documentation—procedures, specifications, reports, training materials.

Generative AI helps with:

  • Drafting initial content from bullet points
  • Editing and improving existing drafts
  • Converting technical content for different audiences
  • Creating consistent formatting and structure

An engineer might spend an hour crafting a procedure. With AI, they spend 20 minutes reviewing and refining a draft.

The AI won’t understand your specific equipment or processes perfectly. But it can create a structured starting point that you polish.

Email and communication

Engineering involves constant communication—with suppliers, colleagues, customers, management.

AI helps draft emails, translate technical concepts for non-technical audiences, and communicate more clearly.

Time savings add up when you send dozens of emails daily.

Presentation creation

Summarising technical information for presentations, creating outlines, generating speaker notes.

Not glamorous, but it saves time and often improves quality.

Code and automation

PLC and automation code

This is where I’ve seen some surprisingly useful applications.

Generative AI can:

  • Generate initial PLC logic from natural language descriptions
  • Help translate between programming languages
  • Suggest modifications to existing code
  • Debug issues by explaining code behaviour

A controls engineer described using ChatGPT to write initial ladder logic for a new station. “It got about 70% right. The other 30% I had to fix. But it saved me a couple hours.”

Important caveats: AI-generated automation code must be thoroughly reviewed and tested. It can contain subtle errors. Safety-critical code especially needs human verification.

Data analysis scripts

Manufacturing engineers often need to analyse data but aren’t programmers.

AI can write Python, R, or Excel scripts to:

  • Process production data
  • Create visualisations
  • Perform statistical analysis
  • Automate repetitive calculations

“I describe what I want to analyse, the AI writes the code, I run it, we iterate.”

SCADA and HMI development

Similar to PLC programming, AI can help with initial screen layouts, scripting, and database queries for SCADA systems.

Research and learning

Technical research

Engineers constantly need to learn—new materials, new processes, new standards, new equipment.

AI can:

  • Summarise technical papers and documentation
  • Explain unfamiliar concepts
  • Compare alternatives
  • Answer specific technical questions

It’s like having a knowledgeable colleague to ask questions.

But verify important information. AI can confidently state things that aren’t quite right.

Troubleshooting support

When equipment isn’t working, AI can help brainstorm causes and solutions.

“The bearing temperature on line 3 is running high. What could cause this?”

The AI suggests possibilities. You apply your specific knowledge to evaluate them.

This works best when you have enough expertise to assess AI suggestions.

Standards and regulations

Understanding how standards apply to your situation. AI can help interpret requirements and identify relevant sections.

Again, verify anything consequential. AI summarising a standard isn’t the same as reading it yourself.

Design and analysis assistance

Generative design

Some CAD platforms now include AI-powered generative design—specifying constraints and having AI explore possible geometries.

This is more specialised than general generative AI, but it’s increasingly accessible.

Design for manufacturing

AI can review designs and suggest modifications to improve manufacturability, though this capability is still developing.

Failure mode analysis

Brainstorming potential failure modes for new designs or processes. AI can suggest possibilities you might not have considered.

Material selection

Given requirements, AI can help explore material options and trade-offs.

What generative AI doesn’t do well

Precision engineering calculations

AI makes numerical mistakes. Stress calculations, thermal analysis, tolerances—these need proper engineering tools, not chatbots.

Use AI to help structure a problem or understand an approach. Don’t trust its calculations.

Deep process understanding

AI has general knowledge about manufacturing processes. It doesn’t know your specific equipment, materials, and constraints.

It can suggest possibilities. It can’t replace process expertise.

Safety-critical decisions

AI shouldn’t make decisions where errors have safety consequences.

It can help you think through safety considerations. Final judgment must be human.

Proprietary or recent information

AI training data has cutoffs. It doesn’t know about your company’s specific equipment or very recent developments.

Practical tips for engineers

Be specific in prompts

Vague requests get vague responses. Provide context:

  • Your industry and application
  • Specific constraints and requirements
  • What you’ve already tried
  • What format you want the response in

Iterate

First responses are rarely perfect. Follow up with clarifications, corrections, and additional requests.

Verify

AI is confidently wrong surprisingly often. Check important information against authoritative sources.

Learn what it’s good at

Each engineer finds different applications valuable. Experiment to discover what works for your specific work.

Keep proprietary information private

Be careful about sharing confidential information in AI prompts. Understand the privacy implications of the tools you use.

Organisational considerations

Policies and guidelines

Companies need clear policies about AI use:

  • What’s acceptable and what isn’t
  • Confidentiality requirements
  • Verification expectations for AI-generated content
  • Documentation of AI assistance

Training and sharing

Help people learn to use these tools effectively. Share what works.

Quality assurance

AI-generated content—especially code or technical documents—needs review processes.

Getting started

If you’re not using generative AI yet:

  1. Try it for low-stakes tasks: Drafting emails, summarising documents, explaining concepts.

  2. Find your use cases: Experiment to discover where AI helps your specific work.

  3. Build habits: Integrate AI into your workflow where it adds value.

  4. Stay current: These tools improve rapidly. What doesn’t work today might work in six months.

For manufacturers exploring how generative AI fits broader AI strategy, AI consultants Sydney can help connect productivity tools with operational AI.

The bigger picture

Generative AI is a tool. Like any tool, its value depends on how you use it.

For manufacturing engineers, the clearest value is in documentation, communication, and coding assistance. Research and design support are useful but require more judgment.

These tools won’t replace engineering expertise. They make engineers more productive at certain tasks.

The engineers who learn to use AI effectively will be more productive than those who don’t. That’s worth some experimentation to discover what works for you.

And if you’re thinking about generative AI as part of broader manufacturing AI adoption, firms like Team400 can help you develop a comprehensive approach—from productivity tools to operational systems.

Start experimenting. See what helps. Build from there.