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Plant and machine operators, and assemblers · code 8183

Packing, bottling and labelling machine operators

The line fills the bottles. Someone still keeps it running.

AI barely touches any of the three tasks: running machines that label, wrap, seal, fill and pack products.

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

Packing, bottling and labelling machine operators monitor and operate machines which weigh, pack and label various products or fill different containers with products. Tasks include - (a) operating and monitoring machines that weigh, wrap, seal and pack various products; (b) operating and monitoring machines that fill and seal tubes, bottles, cans, boxes, bags and other containers with products such as food, beverages, paints, oils and lotions; (c) operating and monitoring machines that, by gluing or other methods, label products, packages and various containers. Examples of the occupations classified here: - Bottle filler - Canning machine operator - Labelling machine operator - Packing machine operator - Wrapping machine operator Some related occupations classified elsewhere: - Hand packer - 9321

  • cylinder filler
  • footwear finishing and packing operator
  • cigar brander
  • canning and bottling line operator
  • packaging and filling machine operator
  • heat sealing machine operator
  • leather goods packing operator

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

78 / 100

Mostly holds up against AI

AI can take a few chores off your plate, but most of the day is still yours.

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

172 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 3 tasks for how much of it AI could do. They average 22.3 out of 100, so it holds up at 100 − 22.3 = 77.7.

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 63 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?

Packing, bottling and labelling machine operators 78

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.

  • Automated filling and labelling.
  • Vision systems checking labels and seals.
  • Production records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Clearing jams and fixing faults.
  • Changing lines over between products.
  • Checking quality.

Both

What's arriving

New work that only exists because the AI does.

  • Supervising robotic packing.
  • Recyclable packaging.
  • Serialisation and track-and-trace.

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

29 could do without you

It could take over very little of the work on its own. It could help with 53 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

63 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?

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 46 out of 100. The higher it is, the longer the robot waits.

63 How
hands-on
46 How fiddly
and cramped

Kind of hands-on work Hands-on, fiddly work in tight spaces

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 48.3 · hand skill 50 · cramped spaces 39 · averaged · rule: hands-on 50+ · fiddly 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 23, against an average of 22. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 23

    Operating and monitoring machines that, by gluing or other methods, label products, packages and various containers.

    Very Low
  2. 22

    Operating and monitoring machines that weigh, wrap, seal and pack various products

    Very Low
  3. 22

    Operating and monitoring machines that fill and seal tubes, bottles, cans, boxes, bags and other containers with products such as food, beverages, paints, oils and lotions

    Very Low

3 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
53
AI does it for you
29

Sidekick wins, by 24 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 8321

So, should packing and bottling line operators be worried?

Short answer: not much. Some of the chores will change, and good riddance.

Not about AI software. Every task rates Very Low: running machines that label, wrap, fill and seal. Packaging lines are already automated, so operators keep them running, clear jams and check quality.

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 39 jobs in the “Plant and machine operators, and assemblers” family, averaging 80. This one is below the family average.

Pit this against any other job →