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Craft and related trades workers · code 7224

Metal polishers, wheel grinders and tool sharpeners

Sensors can watch the grinder. Knowing when a blade is truly sharp is touch and experience.

Keeping an eye on the machines is mostly out of AI's reach. It barely touches the other six tasks, including polishing, grinding and checking surfaces by hand.

What they actually do (and what else they get called)

Metal polishers, wheel grinders and tool sharpeners polish and grind metal surfaces and sharpen tools. Tasks include - (a) operating fixed or portable buffing and polishing machines; (b) sharpening cutting tools and instruments using grinding wheels or mechanically operated grinding machines; (c) repairing, adjusting and sharpening saw blades and metal teeth of cylinders in textile carding machines; (d) dressing grinding wheels according to specifications; (e) monitoring machine operations to determine whether adjustments are necessary, and stopping machines when problems occur; (f) inspecting, feeling and measuring work pieces to ensure that surfaces and dimensions meet specifications; (g) selecting and mounting grinding wheels on machines according to specifications, using hand tools and applying knowledge of abrasives and grinding procedures. Examples of the occupations classified here: - Knife sharpener - Metal finisher - Metal polisher - Tool grinder Some related occupations classified elsewhere: - Metal finishing machine operator - 8122 - Metal polishing machine operator - 8122

  • metal polisher
  • tool grinder

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

83 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

92 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 7 tasks for how much of it AI could do. They average 16.9 out of 100, so it holds up at 100 − 16.9 = 83.1.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Pinch of salt: this number is probably too rosy

It only asks what AI software could do, and this job is 78 out of 100 hands-on. A chatbot can't do that part. A robot might, one day, and nobody measures how close that is.

Man v Bot · compare

Who holds up better?

Metal polishers, wheel grinders and tool sharpeners 83

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Machine monitoring with sensors.
  • Measuring parts against specifications.
  • Production records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Feeling and inspecting surfaces.
  • Sharpening specialist blades and tools.
  • Setting up and dressing grinding wheels.

Both

What's arriving

New work that only exists because the AI does.

  • Supervising robotic polishing.
  • Precision parts for medical and aerospace work.
  • Tool sharpening and repair services.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

26 could do without you

It could take over very little of the work on its own. It could help with 60 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

78 how hands-on

Most of this job needs hands, a body or turning up. That's robot territory, and nobody measures how far robots have got well enough to give you a number. We won't make one up.

So could a robot do the physical part?

Less fine handwork and fewer cramped spaces than a typical hands-on job. That clears one hurdle for machines. It doesn't mean machines are doing this work yet.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 39 out of 100. The higher it is, the longer the robot waits.

78 How
hands-on
39 How fiddly
and cramped

Kind of hands-on work Hands-on, less fiddly work

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 2 US jobs .

finger skill 51.8 · hand skill 50 · cramped spaces 15 · averaged · rule: hands-on 50+ · fiddly under 39.4

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 28, against an average of 17. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 28

    Monitoring machine operations to determine whether adjustments are necessary, and stopping machines when problems occur

    Low
  2. 16

    Inspecting, feeling, and measuring work pieces to ensure that surfaces and dimensions meet specifications

    Very Low
  3. 16

    Sharpening cutting tools and instruments using grinding wheels or mechanically operated grinding machines

    Very Low
  4. 15

    Repairing, adjusting and sharpening saw blades and metal teeth of cylinders in textile carding machines

    Very Low
  5. 15

    Dressing grinding wheels, according to specifications

    Very Low
  6. 15

    Operating fixed or portable buffing and polishing machines

    Very Low
  7. 14

    Selecting and mounting grinding wheels on machines according to specifications, using hand tools and applying knowledge of abrasives and grinding procedures.

    Very Low

7 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
60
AI does it for you
26

Sidekick wins, by 34 points.

How good a match is this?

3 Australian jobs match this one, and we've simply averaged them. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 3221, 3233, 7123

So, should metal polishers and tool grinders be worried?

Short answer: not really. Put the kettle on.

No. The most exposed task is monitoring machines, rated Low; sharpening, polishing and inspecting rate Very Low. Automated grinding is common for standard parts; specialist sharpening and finishing stay hands-on.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 65 jobs in the “Craft and related trades workers” family, averaging 84. This one is below the family average.

Pit this against any other job →