Which Australian Jobs Are Most Exposed to AI?

The headline number: organisations adopting AI are seeing 3x the revenue per employee compared to those going slower. That’s not a forecast. That’s a measured gap, happening right now.

But what really landed for us was Atkinson’s bluntness about where we are in the cycle. In his words, the days of proofs of concept and experimentation are over. Organisations now need to scale AI’s impact. And he’s right – we see it every week with our clients here on the Sunshine Coast.

There’s another insight from PwC’s survey of over 50,000 employees globally: people who are actually using AI are more optimistic about their productivity and creativity than those who aren’t. Fear drops when skills go up. That’s not a marketing line, it’s survey data from one of the Big Four.

Atkinson puts it plainly – there’s an obligation sitting in the C-suite right now to equip people with the skills they need to make this transition. Because, as he says, if you do not have AI skills going forward, it’s going to be very difficult to have a positive impact.

He calls it the management challenge of our generation. We agree. And we think the first step is seeing the landscape clearly.

Which brings us to the dashboard we built.

How It Started

Last week our founder was scrolling through socials and kept seeing the same thing pop up across multiple accounts – an interactive tree-map visualising AI exposure across US occupations. Colour-coded from green (safe) to red (exposed), sized by employment numbers. The concept was being attributed to Andrej Karpathy, the former OpenAI co-founder and mind behind Tesla’s Autopilot vision system.

The idea was simple and the visual was immediately powerful. One look and you could see where AI hits hardest across an entire economy.

But every version we saw was built on US data. Every occupation, every salary, every growth projection – American.

We’re on the Sunshine Coast, not in San Francisco. Our clients are Australian SMEs, not Silicon Valley startups. And Australia’s job market is structurally different from the US in ways that matter enormously when you’re trying to figure out where AI hits hardest.

So we built the Australian version.

The Dashboard

What you’re looking at below is an interactive treemap covering 108 Australian occupations across all eight ANZSCO major groups. The size of each tile is proportional to employment (bigger tile = more people in that job). The colour shows AI exposure on a 0-10 scale – green means low exposure, red means high.

Click or tap any tile to see the full breakdown: median pay, employment outlook, education requirements, and a detailed explanation of why that occupation scored where it did.

Works better in Full-Screen Mode – link above the dashboard

What the Data Actually Shows

The weighted average AI exposure across Australian jobs comes in at 4.5 out of 10. That’s slightly below the US equivalent reportedly sitting around 4.9, and there’s a good structural reason for it: Australia has a proportionally larger workforce in trades, mining, agriculture, and care work. Plumbers, electricians, aged care workers, construction labourers – these are massive employment categories in Australia, and they all score low on AI exposure because the work is physical, unpredictable, and happens in environments robots can’t navigate.

The picture flips when you look at knowledge work. Bookkeepers, general clerks, payroll officers, software developers, paralegals, data analysts – these occupations produce digital outputs. Reports, code, spreadsheets, documents. And if your output is a file, your job is being repriced right now. The question isn’t whether AI can do parts of it. The question is how fast the economics shift.

Here’s the breakdown by risk band across the Australian workforce:

• Minimal exposure (0-1): ~700K jobs, 8% – trades, construction, emergency services
• Low exposure (2-3): ~3.2M jobs, 35% – healthcare, childcare, cleaning, hospitality
• Moderate exposure (4-5): ~2.3M jobs, 25% – retail, engineering, transport, manufacturing
• High exposure (6-7): ~1.1M jobs, 13% – management, real estate, reception, cashiers
• Very high exposure (8-10): ~1.8M jobs, 20% – admin, finance, legal, software, marketing

That last category represents nearly $230 billion in annual Australian wages. That’s not theoretical disruption. That’s real economic pressure on real businesses and the people who work in them.

Go back and watch the PwC clip again with that number in mind. When Atkinson talks about the management challenge of our generation, this is the scale he’s talking about.

The Speed Mismatch

One of the sharpest observations from the PwC interview is that technology is moving much faster than organisational change. There’s a mismatch, as Atkinson puts it, and that mismatch creates tension at every level – boards want to know if management is adopting AI, management wants to know if investment is generating returns, and employees want to know if AI means opportunity or threat.

Our dashboard puts concrete Australian data behind that tension. When a Sunshine Coast accounting firm sees that bookkeepers score 9 out of 10 on AI exposure while the electrician next door scores 2, the strategic conversation changes. It stops being abstract and becomes specific. What do we automate? What do we upskill? Where’s the opportunity?

That specificity is what’s been missing from most of the AI conversation in Australia. We’ve had plenty of global reports and US-centric data. What we haven’t had is a tool that lets an Australian business owner look at their own workforce composition and see the exposure profile clearly.

What This Means for Your Business

If you’re a business owner looking at this dashboard, here’s what we’d focus on:

If your team is mostly in green and yellow tiles – trades, healthcare, hospitality, care work – your core roles have strong natural barriers to AI displacement. Your opportunity is on the back-office side. Use AI to handle the admin, quoting, scheduling, and compliance work that eats into your productive hours. That’s where the 3x productivity multiplier PwC is talking about shows up for you.

If your team is mostly in orange and red tiles – professional services, admin, finance, legal, marketing – the exposure is real and it’s already in motion. The play isn’t to panic. It’s to become the team that uses AI to do the work better, faster, and more creatively than anyone who doesn’t. Remember the PwC survey finding: people using AI are more optimistic, not less. Early adopters gain a genuine competitive edge. Hesitation carries real opportunity cost.

Either way, Atkinson’s point about obligation applies. There’s a responsibility to equip your people with AI skills now, not in a year, not after the next strategy offsite. The businesses that will thrive aren’t the ones that ignore AI or chase every shiny tool. They’re the ones that take a clear-eyed look at where their operations are exposed, build the right skills into their team, and implement practical AI solutions that solve actual problems.

If you’ve just checked your industry’s exposure score and you’re thinking “okay, so what do I actually do about this?” – that’s exactly the conversation we have with clients every week.

We deploy AI voice agents, chat agents, and agentic automation that handle the routine so your team focuses on what actually requires a human. Live 24/7. Natural conversation. Not a chatbot from 2019.

Don’t take our word for it, call one of our live agents right now and hear for yourself. These aren’t demos. They’re running real business operations today.

Bundled AI solutions start from $1,497/month, and every deployment includes a Traffic Activation Package so your customers actually know your AI agent exists and your staff actually use it.

See what this looks like for your business →

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