Practical GenAI for the shopfloor: four uses that save time today

Generative AI gets talked about as if it will run the factory by next year.

It won't. But it is already genuinely useful for people who work in and around the shopfloor, as long as it's pointed at the right problems and fed the right data.

Here are four uses I think are practical today.

1. Asking questions in plain English

Most operational data sits behind reports someone has to build, filter and export. GenAI lets people ask for what they need directly.

"Write a one-page report for my continuous improvement manager explaining OEE trends on Line 4 over the last 12 months, highlighting anything that could have affected the results."

That report used to take an engineer half a day. Now it's a first draft in seconds, which the engineer reviews and sharpens.

The time saved isn't the main win, though. It's that people ask questions they'd never have bothered to raise before, because getting the answer was too much effort.

2. Helping configure systems

Configuring shopfloor software is detailed work: equipment, materials, routings, reason codes, inspection plans. It's also where a lot of implementation time goes.

"Configure a new packing line in the packing department. Ask me any questions you need to complete the setup."

GenAI can walk someone through a setup conversationally, fill in the obvious parts and ask about the rest. It doesn't remove the need to understand your processes. It makes the mechanical part faster, and it lowers the bar for people who aren't system experts.

3. A knowledge assistant on every screen

Operators and engineers have questions all day:

"What's the calculation for Cpk?" "What is this screen showing me?" "What's the changeover procedure for product X on this machine?"

A GenAI assistant that knows your procedures, specifications and the system itself answers in seconds, at the point of work. For new starters that's huge. It means they're not interrupting the one experienced person on shift every ten minutes.

4. Shift summaries and handover

Shift handover is usually verbal, or a few lines in a log. Detail gets lost and the incoming crew starts partly blind.

GenAI can write the handover from what actually happened: which orders ran, where the stops were, which quality checks are outstanding, what's waiting. The outgoing supervisor reviews it and adds the things only a person knows. I cover handover in more depth in Shift handover is a process.

The condition that decides whether any of this works

Every one of these depends on the same thing: the AI has to have access to good, contextualised operational data.

A report on OEE trends is only as accurate as the downtime data behind it. A shift summary is only useful if the shift's activity was captured in a system, not on paper. A knowledge assistant needs current procedures, not the version from three years ago on a shared drive.

That's why GenAI built into, or connected to, your MES/MOM is far more useful than a general chatbot. It has the context: which order, which line, which material, which shift.

A word of caution

GenAI generates plausible text. Plausible is not the same as correct.

Be wary of letting it make decisions on its own in a production environment. Use it to draft, summarise, explain and assist, with a person reviewing anything that matters. And make sure answers can be traced back to the data they came from, so people can check them.

Start where a wrong answer is cheap, like a first-draft report, and build trust from there.

Where to start

Pick the task your engineers or supervisors complain about most. The weekly report. The handover. The questions new starters keep asking.

That's usually the best first use case: frequent, time-consuming, low-risk, and easy to measure.

Which of the four would save your team the most time?