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Craft and related trades workers · code 7215

Riggers and cable splicers

Software can calculate the load. A rigger decides whether to trust it.

AI barely touches any of the six tasks, from sizing up loads and rigging stage gear to splicing cables and building drilling derricks.

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

Riggers and cable splicers assemble rigging gear to move and position equipment and structural components, or install and maintain cables, ropes and wires on construction sites, buildings or other structures. Tasks include - (a) estimating the size, shape and weight of objects to be moved and deciding on the type of equipment to move them; (b) installing and repairing cables, ropes, wires, pulleys and other tackle; (c) joining, repairing and fitting attachments to wires, ropes and cables; (d) working as member of a crew erecting and repairing derricks for drilling water, gas and oil wells; (e) lifting and mounting scenery, lighting and other equipment in theatres and on film sets; (f) installing and maintaining communication towers, aerial cableways, funicular railways, ski lifts and similar infrastructure. Examples of the occupations classified here: - Cable and rope splicer - Rigger - Ship rigger - Theatrical rigger - Tower rigger Some related occupations classified elsewhere: - Cable splicing machine operator - 8189 - Crane, hoist and related plant operators - 8343

  • rigging supervisor
  • event scaffolder
  • tent installer
  • rigger

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

87 / 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

41 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 6 tasks for how much of it AI could do. They average 12.6 out of 100, so it holds up at 100 − 12.6 = 87.4.

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 68 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?

Riggers and cable splicers 87

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.

  • Lift plans and load calculations.
  • Inspection records and certification.
  • 3D models of stages and structures.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Judging weight, balance and rigging points on site.
  • Working safely at height with heavy loads.
  • Splicing and fitting cables by hand.

Both

What's arriving

New work that only exists because the AI does.

  • Automated stage and event rigging systems.
  • Maintaining telecoms towers and wind turbines.
  • Drone inspections of cables and towers.

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

17 could do without you

It could take over very little of the work on its own. It could help with 51 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

68 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 53 out of 100. The higher it is, the longer the robot waits.

68 How
hands-on
53 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 42.9 · hand skill 44.6 · cramped spaces 70.8 · 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 16, against an average of 13. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 16

    Estimating the size, shape and weight of objects to be moved and deciding on the type of equipment to move them

    Very Low
  2. 16

    Lifting and mounting scenery, lighting and other equipment in theatres and on film sets

    Very Low
  3. 12

    Joining, repairing and fitting attachments to wires, ropes and cables

    Very Low
  4. 11

    Installing and maintaining communication towers, aerial cableways, funicular railways, ski lifts and similar infrastructure.

    Very Low
  5. 11

    Installing and repairing cables, ropes, wires, pulleys and other tackle

    Very Low
  6. 11

    Working as member of crew erecting and repairing derricks for drilling water, gas and oil wells

    Very Low

6 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
51
AI does it for you
17

Sidekick wins, by 34 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 3991, 8217

So, should riggers be worried?

Short answer: not really. Put the kettle on.

No. Every task rates Very Low, from estimating loads to mounting stage equipment and repairing cables. Planning software helps with lift plans, but the work is at height, under load and different every time.

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 83. This one is above the family average.

Pit this against any other job →