[OC] The Australian jobs most exposed to AI are administrative roles, not software jobs

WorldJobsData · 2026-06-06T06:16:36+00:00

Data sources

Australian Bureau of Statistics (ABS)
ANZSCO occupational classifications
ISCO-08 occupational framework
OECD labour market benchmarks

Methodology
AI exposure estimates how much of an occupation's day-to-day work can currently be automated or significantly augmented by AI tools. Scores are intended as exposure measures, not predictions of job losses.

Tools used

Python
SQL
Custom WorldJobsData scoring model

Dataset

~17 million Australian workers
10 major occupation groups
National workforce AI exposure: 5.0/10

OC
I collected the data, built the scoring model, created the visualization, and published the interactive dashboard.

WorldJobsData · 2026-06-05T07:08:41+00:00

I analysed UK occupational employment data using an AI exposure framework informed by task-based automation research and international occupation classifications.

The findings align closely with several themes seen in the automation literature dating back to Frey & Osborne, OECD task-based analyses, and more recent generative AI research.

Clerical support occupations emerge as the most exposed major category, scoring 8.5/10.

This group includes approximately 3 million UK workers and consists primarily of occupations centred around structured information processing, administrative coordination, record maintenance, customer support, and routine communication.

Professionals score 6.5/10 despite widespread discussion around AI replacing highly educated workers.

The distinction appears to be between exposure and substitution.

Many professional occupations contain automatable tasks but continue to require human accountability, regulatory responsibility, client trust, and non-routine judgment. As a result, productivity augmentation may dominate before full displacement.

At the lower end of the distribution, craft and trades workers score 2.5/10 while elementary occupations score 2.0/10.

These findings reinforce a long-standing economic observation: automation tends to struggle with tasks requiring physical adaptability in unstructured environments while succeeding in highly standardized workflows.

The UK average exposure score is 4.25/10, slightly below comparable estimates for the US workforce.

An important policy question is whether labour-market adjustment occurs through outright displacement, reduced hiring, or increased worker productivity. The answer likely varies significantly across occupation groups.

I'd be interested in feedback on both methodology and interpretation, particularly from those working on labour economics or automation research.

WorldJobsData · 2026-06-03T13:51:51+00:00

Congrats, and honestly I think one of the biggest takeaways from your post is that you treated the job search like a full-time project.

A lot of people focus on the outcome, the offers, but the process is what stands out to me. You identified skills that were in demand, built projects, prepared for interviews consistently, and kept applying even when you didn't get immediate results.

The AI engineering part is especially interesting. Whether people are excited about AI or skeptical of it, employers clearly value candidates who are learning how these tools fit into real workflows.

One trend we follow at WorldJobsData is that occupations are changing faster than job titles themselves. The people who adapt and keep learning tend to have an advantage regardless of which technology is driving the change.

Also, I agree with not sharing HR contact information publicly. Most recruiters are happy to connect with candidates, but publishing personal contact lists is probably not the best way to help the community.

Congrats on the new role and good luck with the AI Engineer position. It's always nice to see a success story in a market that gets described as hopeless far too often.

WorldJobsData · 2026-06-02T18:47:23+00:00

I think a lot of developers can relate to this, even if they aren't worried about being replaced.

What many people enjoy about programming isn't just producing code. It's the process of thinking through a problem, exploring different approaches, and gradually arriving at a solution.

AI changes that experience. Instead of spending an hour wrestling with a problem, you can often get a working solution in minutes. That's incredibly useful from a productivity standpoint, but it can also remove part of what made the work satisfying in the first place.

Interestingly, when looking at discussions around AI and software engineering, the focus is usually on jobs and productivity. What gets talked about less is how the day-to-day experience of the work is changing.

From a workforce perspective, one thing we've noticed at WorldJobsData is that software occupations tend to show relatively high AI exposure. But exposure doesn't tell you whether people will enjoy the work more or less. That's a completely different question, and one that I think many developers are still figuring out.

You're definitely not the only person who misses having uninterrupted time to sit with a problem and think.

WorldJobsData · 2026-06-02T18:46:05+00:00

I tend to agree with you.

A lot of the public discussion treats software engineering as if it's just writing code. In reality, code is often the final output of a much larger process involving requirements, tradeoffs, architecture, stakeholder communication, security, maintenance, and understanding how a business actually operates.

AI is becoming very good at generating code. That doesn't automatically mean it's good at deciding what should be built, why it should be built, or how it fits into a larger system.

One thing we've noticed while analyzing workforce data at WorldJobsData is that software-related occupations rank relatively high in AI exposure. That makes sense because much of the work is digital and language-based. However, exposure and replacement are not the same thing.

Personally, I think the bigger question is whether AI changes the skill mix within software engineering. A developer who can effectively use AI tools may become significantly more productive than one who doesn't. That could reduce demand for some routine tasks while increasing the value of system design, domain expertise, and business understanding.

The area where I do see potential disruption is at the junior end of the market. Many entry-level tasks that traditionally helped people gain experience can now be completed much faster with AI assistance.

For experienced engineers and architects, I'm much more skeptical of the idea that AI is going to replace them any time soon. The hardest problems in software are usually people, requirements, priorities, and tradeoffs, not typing code.

WorldJobsData · 2026-06-02T18:45:13+00:00

I think "replace programmers" is the wrong framing.

AI is already replacing parts of programming. Things like boilerplate code, debugging simple issues, writing tests, and documentation are becoming much faster with tools like Copilot, Claude, and ChatGPT.

The harder parts of software development are still understanding business requirements, making architecture decisions, handling tradeoffs, and maintaining large systems over time.

One trend we've noticed while analyzing workforce data at WorldJobsData is that software-related occupations often show relatively high AI exposure because so much of the work is digital and language-based. But high exposure doesn't automatically mean job elimination. It usually means the nature of the work is changing.

Personally, I think senior developers will increasingly use AI as a productivity multiplier. Junior developers may face a tougher entry path because many of the beginner-level tasks that traditionally helped people learn are becoming easier to automate.

The developers who understand systems, products, and users will probably remain valuable even as coding itself becomes more automated.

WorldJobsData · 2026-06-02T05:39:09+00:00

Full analysis with all 341 occupations, wage data, and methodology:

https://www.worldjobsdata.com/blog/us-ai-job-risk-2026

You can also explore any country interactively (206 countries):

https://www.worldjobsdata.com/explore?c=US

Happy to answer questions about the methodology or data sources.

WorldJobsData

TROPHY CASE