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Technicians and associate professionals · code 3422

Sports coaches, instructors and officials

AI can track every pass. Motivating a team at half-time is still a coach.

Compiling scores and records and planning fixtures are where AI could take a fair chunk. Motivating athletes and refereeing are mostly out of its reach.

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

Sports coaches, instructors and officials work with amateur and professional sportspersons to enhance performance and encourage greater participation in sport, and organize and officiate in sporting events according to established rules. Tasks include - (a) identifying strengths and weaknesses of athletes or teams; (b) planning, developing and implementing training and practice sessions; (c) developing, planning and coordinating competitive schedules and programmes; (d) motivating and preparing athletes or teams for competitive events or games; (e) formulating competitive strategy, developing game plans and directing athletes and players during games or athletic events; (f) analysing and evaluating athletes’ or teams’ performances and modifying training programmes; (g) monitoring and analysing technique and performance, and determining how future improvements can be made; (h) officiating at sporting events or athletic competitions to maintain standards of play and to ensure that game rules and safety regulations are observed; (i) recording lapsed time and keeping score during events or competitions; (j) judging the performance of competitors, awarding points, imposing penalties for infractions and determining results; (k) compiling scores and other athletic records. Examples of the occupations classified here: - Referee - Ski instructor - Sports coach - Sports official - Swimming instructor Some related occupations classified elsewhere: - Fitness instructor - 3423 - Horse riding instructor - 3423

  • sports instructor
  • sports coach
  • sport official
  • artistic coach
  • lifeguard instructor

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

63 / 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: Minimal Exposure

305 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 11 tasks for how much of it AI could do. They average 37.4 out of 100, so it holds up at 100 − 37.4 = 62.6.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Minimal Exposure”: A few tasks are affected, but the heart of the job isn't.

This job rates 32.1 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?

Sports coaches, instructors and officials 63

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.

  • Scores and records.
  • Scheduling competitions.
  • Performance analysis from data and video.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Motivating athletes.
  • Coaching technique in person.
  • Officiating decisions on the day.

Both

What's arriving

New work that only exists because the AI does.

  • Wearables and performance data.
  • Video review systems.
  • Coaching online.

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

34 could do without you

It could take over very little of the work on its own. It could help with 66 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

32 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 17 out of 100. The higher it is, the longer the robot waits.

32 How
hands-on
17 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, averaged across 3 US jobs .

finger skill 23.9 · hand skill 19.1 · cramped spaces 7.3 · 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 67, against an average of 37. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 67

    Compiling scores and other athletic records.

    Medium
  2. 50

    Developing, planning and co-ordinating competitive schedules and programmes

    Medium
  3. 44

    Monitoring and analysing technique and performance, and determining how future improvements can be made

    Low
  4. 39

    Planning, developing and implementing training and practice sessions

    Low
  5. 37

    Analysing and evaluating athletes' or teams' performances and modifying training programmes

    Low
  6. 32

    Identifying strengths and weaknesses of athletes or teams

    Low
  7. 30

    Formulating competitive strategy, developing game plans and directing athletes and players during games or athletic events

    Low
  8. 30

    Recording lapsed time and keeping score during events or competitions

    Low
  9. 30

    Judging the performance of competitors, awarding points, imposing penalties for infractions and determining results

    Low
  10. 29

    Motivating and preparing athletes or teams for competitive events or games

    Low
  11. 26

    Officiating at sporting events or athletic competitions to maintain standards of play and to ensure that game rules and safety regulations are observed

    Low

11 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
66
AI does it for you
34

Sidekick wins, by 32 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 4523

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

53
47

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 0.15% of the conversations.

The data is sorted by US job, and this one matches 2 of them. We've combined them, counting each by how often it comes up in the conversations, so the busiest one counts most.

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 sports coaches and officials be worried?

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

Parts of it are changing. Keeping scores and planning schedules are rated Medium; analysing performance, planning training, strategy, timing, judging and officiating are Low. Performance analytics and video tools take on more analysis; coaching people stays 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

How are the neighbours doing?

There are 82 jobs in the “Technicians and associate professionals” family, averaging 65. This one is below the family average.

Pit this against any other job →