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Clerical support workers · code 4413

Coding, proof-reading and related clerks

AI already checks spelling and codes data. This is one of the most exposed jobs.

AI could take a fair chunk of three of the five tasks, led by coding and classifying information and checking proofs against originals. Sorting forms and addressing envelopes by hand are mostly out of its reach.

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

Coding, proofreading and related clerks convert information into codes, verify and correct proofs, and perform a number of miscellaneous clerical duties. Tasks include - (a) converting information into codes and classifying information by codes for data-processing purposes; (b) comparing proofs of texts and related material prepared for printing with original material, correcting errors and marking texts for printer according to the established rules; (c) sorting forms and marking them with identification numbers; (d) sorting documents for filing or to collate sets of pages; (e) addressing circulars and envelopes by hand. Examples of the occupations classified here: - Coding clerk - Proofreading clerk

  • proofreader

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

49 / 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 3

382 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 51.5 out of 100, so it holds up at 100 − 51.5 = 48.5.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Exposed: Gradient 3”: Most of the tasks are affected.

This job rates 6.3 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?

Coding, proof-reading and related clerks 49

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.

  • Coding data.
  • Proofreading.
  • Sorting documents.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Specialist proofreading.
  • Final checks where errors are costly.
  • Judgement about style.

Both

What's arriving

New work that only exists because the AI does.

  • Checking AI-generated text.
  • Editorial quality roles.
  • Accessibility checks.

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: High

68 could do without you

It could take over a lot of this job on its own. It could help with 75 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

6 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 12 out of 100. The higher it is, the longer the robot waits.

6 How
hands-on
12 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 19.7 · hand skill 14.3 · 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 70, against an average of 52. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 70

    Converting information into codes and classifying information by codes for data-processing purposes

    Medium
  2. 68

    Comparing proofs of texts and related material prepared for printing with original material, correcting errors and marking texts for printer according to the established rules

    Medium
  3. 60

    Sorting documents for filing or to collate sets of pages

    Medium
  4. 35

    Sorting forms and marking them with identification numbers

    Low
  5. 25

    Addressing circulars and envelopes by hand.

    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
75
AI does it for you
68

Sidekick wins, by 7 points.

How good a match is this?

One Australian job matches, but only partly: not everyone in it does this job. 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 5999

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Out of every 100 conversations

44
56

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job. This job makes up 0.41% of the conversations.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should proofreaders and coding clerks be worried?

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

Yes, this is one of the most exposed jobs. Coding information, proofreading and sorting documents are rated Medium; forms and addressing envelopes are Low. AI takes on much of this; careful specialist proofreading remains.

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 47. This one is above the family average.

Pit this against any other job →