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Clerical support workers · code 4322

Production clerks

Planning software builds the schedule. Chasing the late delivery is still a person.

Turning customer orders into production schedules is the one task AI could take a fair chunk of. Scheduling materials, tracking work between departments and working out material needs are mostly out of its reach.

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

Production clerks compute quantities of materials required at specified dates for manufacturing, construction and similar production programmes, and prepare and check production operation schedules. Tasks include - (a) computing quantities, qualities and types of materials required by production programmes; (b) preparing production requirements schedules, ensuring that materials are available when needed, and keeping relevant records; (c) preparing or assisting in the preparation of production operation schedules on the basis of customers’ orders and production capacity and performance; (d) verifying stocks, arranging deliveries and investigating delays; (e) recording and coordinating the flow of work and materials between departments. Examples of the occupations classified here: - Production clerk - Schedule clerk (materials)

  • machinery assembly coordinator

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

56 / 100

Changing significantly

AI could do a real share of the everyday tasks. The job is changing shape, not vanishing.

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: Exposed: Gradient 2

348 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 5 tasks for how much of it AI could do. They average 43.6 out of 100, so it holds up at 100 − 43.6 = 56.4.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Exposed: Gradient 2”: A good share of the tasks are affected.

This job rates 26 out of 100 for how hands-on it is. The score above only looks at AI tools, not robots, but for work this far from the physical that matters less.

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

Man v Bot · compare

Who holds up better?

Production clerks 56

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.

  • Production schedules.
  • Materials calculations.
  • Records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Chasing delays.
  • Coordinating between departments.
  • Fixing supply problems.

Both

What's arriving

New work that only exists because the AI does.

  • AI production planning.
  • Real-time factory data.
  • Supply chain disruption.

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: Medium

46 could do without you

It could take over some of the work, and help with more. It could help with 69 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

26 how hands-on

Not much of this job needs hands, so the robot can sit this one out.

So could a robot do the physical part?

Less than half of this job is hands-on. Robots still matter, but for a smaller part of the work, so we don't sort it any further.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 18 out of 100. The higher it is, the longer the robot waits.

26 How
hands-on
18 How fiddly
and cramped

Kind of hands-on work Less than half hands-on

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, matched to one US job .

finger skill 26.9 · hand skill 23.1 · cramped spaces 3 · averaged · rule: hands-on under 50

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 54, against an average of 44. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 54

    Preparing or assisting in the preparation of production operation schedules on the basis of customers' orders and production capacity and performance

    Medium
  2. 46

    Preparing production requirements schedules, ensuring that materials are available when needed, and keeping relevant records

    Low
  3. 40

    Verifying stocks, arranging deliveries and investigating delays

    Low
  4. 40

    Recording and coordinating the flow of work and materials between departments.

    Low
  5. 39

    Computing quantities, qualities and types of materials required by production programme

    Low

5 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
69
AI does it for you
46

Sidekick wins, by 24 points.

How good a match is this?

2 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 3129, 5911

So, should production clerks be worried?

Short answer: not worried, but paying attention. The job is changing.

Parts of it are exposed. Production schedules are rated Medium; materials schedules, stock checks, work flow and calculating materials are Low. Planning systems take on scheduling; coordination and problem-solving stay human.

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 29 jobs in the “Clerical support workers” family, averaging 46. This one is above the family average.

Pit this against any other job →