ChatGPT won't run your plant, but here's what industrial AI needs instead

Every manufacturer seems to be in the same conversation right now. It's a fair question, and many operations are already starting to use artificial intelligence more in their day-to-day work beyond just writing emails.

Pulling data exports, asking questions, or writing code, etc. But there's a ceiling, and plants are hitting it faster than anyone expected.

What Happened

Your company has an enterprise agreement, so your data isn't feeding into a public model. That handles the security piece, but it doesn't handle everything.

• Role-based access and audit trails aren't just IT concerns.

• Who can access what, and is there a record of how data was used?

• Without that layer, AI can pull data but can't interpret what it means.

Key Details

An engineer using ChatGPT or Copilot to analyze process data is still working from exports. They pull data into a spreadsheet, paste it into the tool, and ask what's driving a yield drop.

• Consistent naming, useful metadata, and an asset hierarchy that matches how your plant is organized.

• A single environment where those sources feed in together is what changes the picture.

• That's the norm at a lot of plants, and it's what makes unified AI analysis impossible.

Why It Matters

The tool gives a reasonable answer, maybe even a useful one. Now ask the same question two days later and you're starting over.

• Lab data in a LIMS, production data in an MES, process data in the historian, none of it connected.

• Platforms like dataPARC or other Industrial Historians can give you what you need if configured and maintained properly.

What Reports Say

Coverage of the story so far points to:

• Continued reporting by Smart Industry as more details emerge