# AI in manufacturing and operations: safe, lawful and under control

*Where AI helps on the line, how it fails, what the HSE expects, and how to keep a person in charge of the machine.*

## Production summary

- Modules to record: 2
- Total script: 995 words, about 7 minutes of finished audio
- Voices: Emma (host) and George (practice educator)
- Level: Operators, team leaders, engineers, quality and safety staff, and operations managers

## Accreditation wording that must appear in the description

- **The CPD Certification Service** (planned): Application scheduled.
- **Health and Safety Executive: regulatory approach to AI** (aligned): Written against the HSE's published statement. Our own mapping, with no endorsement from the HSE implied.
- **BS ISO/IEC 42001 artificial intelligence management systems** (aligned): The governance material is consistent with the published management system requirements. Our own mapping, with no certification implied.

> Do not upgrade any of these words in a description or a thumbnail. Aligned is not accredited, and planned is not approved.


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## Where AI meets the line, and how it fails

**Runtime** about 3 minutes. **Words** 491. **Starts at** 00:00 in the full course recording.

### Learning outcomes to state on camera

- Describe four common uses of AI in manufacturing
- Explain false accepts and false rejects in AI inspection
- Explain drift and what causes it
- State what the Health and Safety Executive expects
- Apply management of change to an AI system

### Script


`[CUE 1]` *A production line with four AI uses marked: a camera, a vibration sensor, a schedule and a collaborative robot*

**EMMA**  [00:00]
George, we've just had a camera system put on line three to inspect parts. Everyone's delighted. Should I be?

**GEORGE**  [00:07]
Probably, with your eyes open. Let me set out where AI turns up on a shop floor. Visual inspection, like yours. Predictive maintenance, watching vibration and temperature. Planning and scheduling. And robots and guided vehicles that work near people.

**EMMA**  [00:23]
Are they all the same risk?

**GEORGE**  [00:25]
No. The first three give advice or decide about product. The fourth moves in the same space as a person. That difference decides how much proof you need before you trust it.


`[CUE 2]` *Two bins, one of good parts wrongly scrapped and one of bad parts wrongly passed, with the second reaching a customer*

**EMMA**  [00:38]
So what can go wrong with my camera?

**GEORGE**  [00:41]
It can be wrong in two directions. A false reject scraps a good part. That costs money and you notice quickly. A false accept passes a bad part. That costs far more, and you may not find out until a customer does.

**EMMA**  [00:58]
The supplier told me it's ninety nine per cent accurate.

**GEORGE**  [01:02]
Then ask the better question. How often does it pass a defect, on which defects, and how do we know? A system tuned to reduce scrap will pass more bad parts. Ask for the false accept rate on the defects your customer cares about most.


`[CUE 3]` *A graph of accuracy falling quietly after a change of material, with no alarm*

**EMMA**  [01:20]
And how would I know?

**GEORGE**  [01:22]
Only one way. Keep testing it against parts whose true state you've established some other way. Known good, known bad, fed through regularly.

**EMMA**  [01:31]
It's been perfect for a month.

**GEORGE**  [01:34]
Which is where drift comes in. The system learned from data gathered at one time, under one set of conditions. When conditions change, what it sees no longer matches what it learned from, and its accuracy falls.


`[CUE 4]` *A list of everyday causes of drift around a camera and a part*

**EMMA**  [01:48]
Wouldn't it tell me?

**GEORGE**  [01:50]
No. Nothing breaks. No alarm. That's what makes it dangerous.

**EMMA**  [01:54]
What sort of change?

**GEORGE**  [01:56]
Ordinary ones. A new material or supplier. A different surface finish. Someone changes a light fitting or the lens gets dirty. A tool wears. A new product variant. A sensor swapped for a different model.


`[CUE 5]` *A management of change form with four triggers: install, retrain, update, process change*

**EMMA**  [02:10]
We changed steel supplier last week.

**GEORGE**  [02:12]
Then that's a change the inspection system should have been checked against. Which brings me to the law.

**EMMA**  [02:19]
Is there a law on AI in factories?

**GEORGE**  [02:22]
The Health and Safety Executive says existing law applies. The 1974 Act is goal setting, so it covers a risk whatever technology creates it. They expect a risk assessment for uses of AI that affect health and safety, and controls so far as is reasonably practicable. Including against cyber threats.

**EMMA**  [02:42]
So nothing special. Just do it properly.

**GEORGE**  [02:45]
That's their stated aim. That AI risk stops being novel and is managed like any other. In practice, management of change. Putting the system in is a change. Retraining it is a change. Updating its software is a change. And changing the process it watches is a change.

**EMMA**  [03:04]
And whose job is that? The supplier's?

**GEORGE**  [03:07]
Yours. The duty holder is the employer. Buying the system doesn't transfer the duty to assess and control the risk it creates.

### Sources for the on screen credit

- HSE's regulatory approach to artificial intelligence (AI), Health and Safety Executive
- Health and Safety at Work etc Act 1974, legislation.gov.uk
- Provision and Use of Work Equipment Regulations 1998 (PUWER), Health and Safety Executive

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## Keeping a person in charge of the machine

**Runtime** about 3 minutes. **Words** 504. **Starts at** 03:16 in the full course recording.

### Learning outcomes to state on camera

- Decide what an AI system may advise on and what it may control
- Explain validation before use and monitoring in use
- Describe the cyber security risk an AI system adds to plant
- Explain how the EU Machinery Regulation and AI Act affect machinery with AI
- Keep the records that show a system is under control

### Script


`[CUE 1]` *Two screens: one advising an operator who decides, one acting on the line by itself*

**EMMA**  [03:16]
George, last time you told me the camera can go quietly wrong. So how do I stay in charge of it?

**GEORGE**  [03:24]
Start with one question about any AI system on your site. Does it advise, or does it control?

**EMMA**  [03:32]
What's the difference in practice?

**GEORGE**  [03:34]
A system that advises shows a person something and the person acts. A system that controls acts itself. Rejects the part, stops the line, changes a setpoint.


`[CUE 2]` *A validation record beside a regular tray of known good and known bad challenge parts*

**EMMA**  [03:44]
Ours rejects parts automatically.

**GEORGE**  [03:46]
Then it controls, for quality. That's a decision someone should have made deliberately. Anything new should start as advice. Moving it to control needs its own assessment. And where the action affects safety, it's part of the safety system and has to be designed and validated as one, by competent people.

**EMMA**  [04:06]
What usually goes wrong?

**GEORGE**  [04:08]
Not a bad decision to automate. It's an advisory tool that drifts into control because people stop checking it. If the operator always accepts what the screen says, it's controlling, whatever the procedure calls it.


`[CUE 3]` *An override log with a rising line marked drift and a flat zero line marked nobody is looking*

**EMMA**  [04:22]
So how do I keep it honest?

**GEORGE**  [04:25]
Validate before use and watch in use. Before you rely on it, test it against cases whose true answer you know. Include the hard ones and the defects that matter most. Record the result, the conditions, and who signed it off.

**EMMA**  [04:41]
And afterwards?

**GEORGE**  [04:42]
Challenge samples. Known good and known bad, fed through at a set interval. And track overrides. How often people overrule it, and why.


`[CUE 4]` *An AI system on the plant network with a line out to a supplier and three questions beside it*

**EMMA**  [04:51]
What's a good override rate?

**GEORGE**  [04:53]
It should never be zero. A rising rate is an early sign of drift. But a rate of zero means nobody's looking. And keep the manual method alive. If the team's forgotten how to inspect by hand, you've no fallback.

**EMMA**  [05:09]
Our IT manager is nervous about the supplier's remote access.

**GEORGE**  [05:13]
Rightly. It's software connected to plant, usually with a route back to a supplier for updates. That makes it part of your cyber security problem. The HSE names cyber threats explicitly, and manufacturers of things like machinery and vehicles are among the sectors the EU's NIS2 Directive covers.


`[CUE 5]` *A calendar marking 20 January 2027 and 2 August 2028, and four records in a folder*

**EMMA**  [05:32]
What should I ask?

**GEORGE**  [05:34]
How is it updated and by whom? What can it reach on the network? And what happens to the line if it's unavailable, or gives wrong answers on purpose?

**EMMA**  [05:46]
We also build machines and sell some into Europe. Anything coming?

**GEORGE**  [05:50]
Two things. The EU Machinery Regulation applies from 20 January 2027 and replaces the old Directive. It addresses machinery whose safety functions rely on systems that learn. And the EU AI Act treats AI used as a safety component of a regulated product as high risk. After the July 2026 amendment, that applies from 2 August 2028.

**EMMA**  [06:13]
Is that UK law?

**GEORGE**  [06:14]
Neither is, and Great Britain has its own machinery regulations. If you export, take advice on your own product. What I've given you is direction, not a compliance route.

**EMMA**  [06:26]
And what do I keep on file?

**GEORGE**  [06:29]
Four records. What the system's allowed to do. How it was validated. How it's being checked. And every change made to it.

### Sources for the on screen credit

- HSE's regulatory approach to artificial intelligence (AI), Health and Safety Executive
- Regulation (EU) 2023/1230 on machinery, EUR-Lex
- Regulation (EU) 2024/1689 (Artificial Intelligence Act), Article 6 and Annex I, EUR-Lex
- Directive (EU) 2022/2555 (NIS2), Annex II: manufacturing, EUR-Lex

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