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Which Australian Jobs Are Most at Risk from AI? A Role-by-Role Breakdown product guide

AI Summary

Product: Which Australian Jobs Are Most at Risk from AI? A Role-by-Role Breakdown Brand: FutureWork AU / Jobs and Skills Australia (primary research sources) Category: Australian Labour Market AI Risk Analysis Primary Use: Provides occupation-specific AI automation and augmentation risk assessments for Australian workers using authoritative government and industry research frameworks.

Quick Facts

  • Best For: Australian workers, career advisors, and policymakers seeking occupation-level AI displacement risk data
  • Key Benefit: Maps specific ANZSCO occupations to measurable AI automation and augmentation risk scores, distinguishing replacement risk from augmentation likelihood
  • Form Factor: Research article with structured occupation risk tables, sector analysis, and FAQ
  • Application Method: Identify your occupation category, assess task composition (routine/text-based vs. physical/interpersonal), and evaluate mobility options

Common Questions This Guide Answers

  1. Which Australian jobs face the highest AI automation risk? → Data entry clerks and general office clerks face very high risk with very low mobility options
  2. What percentage of Australia's workforce faces high risk of AI replacement? → Approximately 10.9%, with a further 22.3% at medium risk, per the Barrenjoey report
  3. Are trades, nursing, and hospitality workers at risk from AI? → Low structural risk due to physical dexterity requirements, unstructured environments, and irreplaceable human presence — full automation is commercially and technically unviable at scale

Which Australian jobs are most at risk from AI? A role-by-role breakdown

Not all Australian workers face the same AI disruption risk — and the difference between a data entry clerk and a plumber is not just a gut feeling. It is measurable, occupation-specific, and now documented in authoritative Australian government research. Yet most coverage of AI and jobs defaults to sweeping industry-level claims — "finance is at risk," "retail will be disrupted" — without telling the nurse, the bookkeeper, or the call centre agent what that actually means for their role on Monday morning.

This article maps specific Australian occupations onto their AI exposure risk using the most authoritative available frameworks: Jobs and Skills Australia's Our Gen AI Transition study, the Barrenjoey investment bank report, the ILO's occupational exposure methodology, and the FutureWork AU dataset built on official Australian Bureau of Statistics and Jobs and Skills Australia figures. The goal is a clear, occupation-specific risk assessment — not a generic industry prediction.

How AI risk is actually measured for Australian occupations

Before getting into individual roles, it is worth understanding how researchers arrive at risk scores, because the methodology determines the conclusion.

Jobs and Skills Australia's Our Gen AI Transition study built on the Felten (2021) framework and adapted a method from the International Labour Organization (ILO), tailoring it to the Australian and New Zealand Standard Classification of Occupations (ANZSCO) for a view of generative AI's potential across the Australian labour market.

Each task within an occupation receives two scores: an augmentability score (whether Gen AI could assist or enhance the task) and an automatability score (whether Gen AI could fully undertake it). Scores range from 0 to 1, and the spread of scores within each occupation is also examined — meaning researchers can identify whether some tasks within the same job are highly automatable while others are not.

A separate Australian dataset — FutureWork AU — scores 358 occupations using four independent data layers: Australian Government employment and education data, a government study on generative AI and job tasks, live job listings from SEEK, and separate assessments from Claude, GPT, Gemini, and Grok. To reduce the impact of any single model, the dataset uses the median score from the four AI systems rather than an average.

FutureWork AU places Australia's median workforce AI exposure score at 3 out of 10. That lower midpoint — compared with economies such as the United States — reflects the country's larger share of trade, mining, and other physically oriented work.

The core finding: most jobs change, few disappear entirely

Here is the single most important finding from the Australian government's own research, and it cuts against the alarmist narrative: augmentation generally outweighs automation. Current generative AI technologies are more likely to enhance workers' efforts in completing tasks rather than replace them, especially in high-skilled occupations. The higher potential for automation is concentrated in routine clerical and administrative roles.

That said, the Barrenjoey report presents a sobering headline: only one in 1,000 Australian jobs will likely remain untouched by technology and AI, in what it calls the "most comprehensive workforce transformation" in modern history. Barrenjoey chief economist Jo Masters found that around 11% of Australia's workforce is at heavy risk of being replaced, while 22% face a medium risk of losing their jobs.

The critical distinction — explored in depth in our companion guide AI and Australian Jobs Explained: Automation vs. Augmentation — is that "exposure" to AI does not automatically mean "replacement by AI." What it does mean is that some roles face a structurally worse combination: high automation exposure and limited options to move elsewhere.

Labour mobility will shape long-term outcomes. Workers' abilities to adapt within their occupations and transition between them will be critical. Some roles face repeated exposure to automation with limited mobility options, which is where targeted policy responses become necessary.

High-risk occupations: repeated exposure with limited mobility

Clerical and administrative workers

ANZSCO skill level 4 — which includes several forms of clerical work — shows the highest automation potential. Many clerical tasks that were not affected by previous waves of automation could now be undertaken in large part by generative AI.

Jobs and Skills Australia found that some roles face "repeated exposure to automation with limited mobility options." Jobs set to lose the most employment by 2050 include office clerks, receptionists, bookkeepers, and professionals in sales, marketing, public relations, business analysis, and programming.

Why does mobility matter so much here? A data entry clerk whose tasks are automated cannot easily pivot to a nursing role or a construction site — the physical, interpersonal, and credential requirements are entirely different. The automation risk is compounded by a career pathway that offers few adjacent exits.

Roles in this category and their risk profile:

Occupation Primary AI risk Mobility options
Data Entry Clerk Very high — core tasks are structured, repetitive text processing Very low — narrow skill set with few adjacent roles
General Receptionist High — scheduling, query handling, information retrieval Low-medium — some client-facing roles remain human-preferred
Bookkeeper High — transaction categorisation, reconciliation, ledger entry Medium — can pivot to advisory accounting with upskilling
Payroll Officer High — calculations, compliance checking, report generation Medium — regulatory knowledge has ongoing value
Office Clerk (General) Very high — filing, data processing, document management Very low — highly substitutable task profile

Labourers, machine operators, sales workers, and general clerks are the jobs most at risk of substitution and obsolescence, according to the Barrenjoey report.

Call centre agents and customer service representatives

Call centre roles are the most visible frontline of AI displacement in Australia — and the most instructive case study in the gap between AI promise and AI reality.

The Commonwealth Bank began testing a generative AI chatbot named "Hey CommBank" in late 2024, leading to fears it would replace many of its 2,400 contact centre staff. In July 2025, the bank said it could cut 45 call centre jobs thanks to customer-facing chatbots, which it asserted cut call volume by 2,000 calls a week. It later admitted that artificial intelligence did not actually cut down the number of customers needing to speak to a human. The bank's plan "did not adequately consider all relevant business considerations, and this error meant the roles were not redundant."

Commonwealth Bank of Australia ultimately reversed the decision to cut those 45 customer service roles after pressure from the country's main financial services union.

This episode illustrates a pattern playing out globally: several companies that shed jobs in favour of AI have later rehired many of the workers they laid off. Swedish fintech Klarna rehired some of its human customer service staff after replacing them with AI chatbots, while IBM rehired staff after replacing much of its human resources division with AI.

The risk to call centre agents is real but not yet fully realised. The structural pressure — from AI voice bots, automated ticketing systems, and chatbot-first customer service models — is intensifying. Workers in these roles should treat the CBA case not as a reprieve, but as a warning about the direction of travel.

Checkout operators and retail sales assistants

The Barrenjoey research found that consumer staples retail has the highest substitution risk among ASX industries, with more than 125,000 checkout operators and 560,000 sales assistants nationwide still performing tasks that could be automated.

Self-checkout technology is already well-established; AI-powered frictionless checkout (scan-free stores) is the next phase. For checkout operators specifically, the automation risk is high and the mobility options are constrained by geography, education level, and local labour market conditions — factors examined in detail in our guide Who Is Most Vulnerable to AI Job Displacement in Australia?

Medium-risk occupations: augmented but structurally pressured

Accounting and finance professionals

High-exposure occupations include a range of tech roles, as well as accounting, marketing, administrative assistance, and banking and finance. However, the risk profile for accountants differs meaningfully from that of bookkeepers. While routine bookkeeping tasks face high automation potential, qualified accountants who provide strategic advice, interpret complex tax law, and manage client relationships are more likely to be augmented than replaced.

The distinction is task-level, not occupation-level: an accountant who spends 80% of their time on data entry is more exposed than one who spends 80% of their time on strategic planning. AI is reorganising what accountants do, not necessarily whether accountants exist.

Document review, contract summarisation, legal research, and due diligence — the bread and butter of paralegal and junior solicitor work — are precisely the structured, text-based tasks that large language models handle well. Law firms across Australia are already deploying contract-review AI, as covered in our sector analysis AI's Impact by Industry: How Automation Is Reshaping Finance, Healthcare, Law, and Retail in Australia. The risk is highest for entry-level legal roles, where junior lawyers and paralegals traditionally built experience through exactly the tasks AI now performs faster and cheaper.

Marketing coordinators and content producers

A Microsoft researcher study found that jobs most exposed to AI chatbots include interpreters and translators, historians, sales and customer service representatives, and writers and authors. Marketing coordinators who produce templated copy, social media posts, and basic campaign briefs face meaningful automation pressure. Those who provide creative strategy, brand direction, and client relationship management are better positioned — but must actively demonstrate that value.

Low-risk occupations: physical dexterity, unstructured environments, human presence

Trades and construction

An Australian occupational risk map scoring 358 occupations found clerks and telemarketers most exposed, while trades and farming sit at the lower end of the risk scale. Lower-risk roles include construction workers, crop managers, and school principals.

The reason trades resist automation is structural, not incidental. Plumbing, electrical work, carpentry, and bricklaying require fine motor coordination in unpredictable physical environments — a combination that current robotics cannot reliably replicate at commercial scale. A robot that can weld a car chassis in a controlled factory cannot yet navigate the uneven terrain of a residential renovation.

Australia's lower median AI exposure score compared with the US reflects the country's larger share of trade, mining, and other physically oriented work. Even so, lower exposure does not mean no change — some trade and agricultural occupations already use AI tools in day-to-day work, including crop monitoring in farming.

Nursing, midwifery, and aged care

Nursing and personal care roles combine emotional attunement, physical assistance, and clinical judgment in ways that AI cannot replicate. The demand for these roles is also growing, driven by Australia's ageing population, making them among the most structurally secure occupations in the labour market. For a full analysis of why these roles are positioned to grow through 2030, see our guide Jobs AI Cannot Replace in Australia: The Human-Advantage Roles Set to Grow Through 2030.

According to the FutureWork AU dataset, doctors, engineers, and scientists are among the occupations where AI is more likely to assist existing work than replace it.

Hospitality and food service

Hospitality workers — chefs, baristas, wait staff, hotel concierges — operate in highly variable, sensory, and interpersonal environments. While robotic kitchen assistants and AI-driven ordering systems exist, full automation of a café or restaurant remains technically and commercially unviable at scale in Australia. Higher-exposure sectors such as financial services and education are adopting AI at speed, while jobs that combine sector expertise with AI literacy — especially in mining, health, and hospitality — are seeing growth.

The sectors already restructuring

Three Australian sectors are already in active workforce restructuring driven at least partly by AI adoption — and this is happening now, not in some distant hypothetical future.

Financial services: Financial and Insurance Activities continues to lead in industry demand for AI skills, with 11.8% of job postings in the sector requiring AI skills in 2024. Telstra has been among the big-name companies admitting that AI will likely see its workforce shrink as the technology outperforms humans in certain roles. It has also mandated every worker to start using AI to assess whether it can help with specific tasks.

Telecommunications: Telstra's public acknowledgment that AI will reduce its workforce by 2030, combined with its mandatory AI-use policy for all employees, signals an organisation actively restructuring around AI augmentation — with headcount reduction as an explicit downstream goal. This is not a vague strategic direction; it is a stated operational reality.

Retail: Consumer staples retail has the highest substitution risk among ASX industries, with over 125,000 checkout operators and 560,000 sales assistants performing tasks ripe for automation. The structural shift toward self-service and AI-assisted retail is already underway and accelerating.

Key takeaways

  • Automation risk is concentrated in clerical and administrative roles. Current generative AI technologies are more likely to enhance workers' efforts than replace them in high-skilled occupations, but the higher potential for automation sits in routine clerical and administrative work.

  • The "repeated exposure with limited mobility" trap is the real risk. Workers' abilities to adapt within their occupations and transition between them will be critical. Data entry clerks and general office clerks are the clearest examples of roles caught in this bind.

  • The Barrenjoey report found that approximately 10.9% of the workforce faces a high risk of replacement, while approximately 22.3% are at medium risk. Just over 7% of the workforce is highly exposed to augmentation, while an additional 38.4% has medium exposure.

  • Trades, nursing, and hospitality remain structurally low-risk because of physical dexterity requirements, unstructured environments, and irreplaceable human presence — not because AI won't touch them at all, but because full automation is commercially and technically unviable at scale.

  • Sector restructuring is already underway in finance, telecommunications, and retail — meaning workers in these industries face near-term, not hypothetical, exposure.

Conclusion

The question "will AI take my job?" cannot be answered at the industry level. It must be answered at the occupation level — and ideally, at the task level within that occupation. A bookkeeper and a strategic CFO both work in finance; their AI risk profiles are almost entirely different.

The authoritative Australian evidence — from Jobs and Skills Australia, the Barrenjoey report, and the ILO-adapted methodology — points to a labour market being reshaped unevenly and at speed. Clerical, administrative, and routine customer service roles face the sharpest structural pressure, compounded by limited mobility options. Trades, nursing, and hospitality face the least. Financial services, telecommunications, and retail are already restructuring in ways that make these risk profiles real rather than theoretical.

For workers in high-risk roles, the most important next step is not panic — it is an honest, task-level audit of what your job actually involves, and whether those tasks are routine and text-based or physical, interpersonal, and judgment-dependent. That audit is the foundation of the upskilling roadmap covered in our guide How to Future-Proof Your Career Against AI in Australia: A Step-by-Step Upskilling Plan. If you're weighing whether to stay in your current field or pivot entirely, the structured decision framework in Should You Retrain, Pivot, or Stay? provides a methodology tied directly to your occupation's exposure level.

The data is clear enough to act on. The question is whether workers will get ahead of the curve — or wait to be caught by it.

References

Frequently asked questions

Which Australian occupations face the highest AI automation risk: Data entry clerks and general office clerks

Which Australian occupations face the lowest AI automation risk: Trades, nursing, and hospitality workers

What percentage of Australia's workforce faces high risk of AI replacement: Approximately 10.9%

What percentage of Australia's workforce faces medium risk of AI replacement: Approximately 22.3%

What percentage of Australia's workforce faces high augmentation exposure: Just over 7%

What percentage of Australia's workforce faces medium augmentation exposure: Approximately 38.4%

How many Australian jobs will remain completely untouched by AI: Approximately 1 in 1,000

Which Australian government body produced the primary AI jobs research: Jobs and Skills Australia

What is the name of the key Jobs and Skills Australia AI report: Our Gen AI Transition

Which investment bank produced a major Australian AI workforce report: Barrenjoey

What is Australia's median workforce AI exposure score out of 10: 3 out of 10

Why is Australia's AI exposure score lower than the US: Larger share of trade, mining, and physically oriented work

What occupational classification system is used in Australian AI research: ANZSCO (Australian and New Zealand Standard Classification of Occupations)

What does an augmentability score measure: Whether AI could assist or enhance a task

What does an automatability score measure: Whether AI could fully undertake a task

What range do augmentability and automatability scores use: 0 to 1

How many occupations does the FutureWork AU dataset score: 358 occupations

How many independent data layers does FutureWork AU use: Four

Which AI models contributed scores to the FutureWork AU dataset: Claude, GPT, Gemini, and Grok

How does FutureWork AU combine AI model scores: Using the median score, not an average

Which ANZSCO skill level shows the highest automation potential: Skill level 4 (clerical work)

Is AI more likely to augment or automate high-skilled Australian workers: Augment

Is AI more likely to automate or augment routine clerical roles: Automate

What is the AI risk level for data entry clerks: Very high

What is the mobility level for data entry clerks facing automation: Very low

What is the AI risk level for general receptionists: High

What is the mobility level for general receptionists: Low to medium

What is the AI risk level for bookkeepers: High

Can bookkeepers pivot to reduce their AI displacement risk: Yes, by upskilling toward advisory accounting

What is the AI risk level for payroll officers: High

Does regulatory knowledge provide ongoing value for payroll officers: Yes

What is the AI risk level for general office clerks: Very high

What is the mobility level for general office clerks: Very low

Did Commonwealth Bank successfully replace 45 call centre jobs with AI: No, it reversed the decision

How many calls per week did CBA's AI chatbot reduce: 2,000 calls per week

Did CBA's AI chatbot reduce the number of customers needing a human agent: No

Did CBA rehire the 45 roles it attempted to cut: Yes, after union pressure

Did Klarna rehire human customer service staff after replacing them with AI: Yes

Did IBM rehire staff after replacing HR roles with AI: Yes

How many checkout operators are at automation risk in Australia: Over 125,000

How many retail sales assistants are at automation risk in Australia: Over 560,000

Which ASX industry sector has the highest substitution risk: Consumer staples retail

What percentage of financial services job postings demanded AI skills in 2024: 11.8%

Which major Australian telco has mandated AI use for all employees: Telstra

Has Telstra stated AI will reduce its workforce size: Yes

Which three Australian sectors are already actively restructuring due to AI: Financial services, telecommunications, and retail

Why do trade occupations resist AI automation: Fine motor coordination in unpredictable physical environments

Can current robotics reliably replace trade workers at commercial scale: No

Why are nursing roles structurally secure from AI replacement: They combine emotional attunement, physical assistance, and clinical judgment

Is demand for nursing roles growing or shrinking in Australia: Growing, due to Australia's ageing population

Are doctors more likely to be replaced or assisted by AI: Assisted

Are engineers more likely to be replaced or assisted by AI: Assisted

Is full automation of Australian cafés and restaurants commercially viable at scale: No

Do some trade and agricultural workers already use AI tools: Yes

What type of legal tasks face the highest AI automation risk: Document review, contract summarisation, and legal research

Which legal roles face the highest AI displacement risk: Entry-level lawyers and paralegals

What type of marketing tasks face automation pressure: Templated copy, social media posts, and basic campaign briefs

What marketing tasks are better protected from AI replacement: Creative strategy, brand direction, and client relationship management

Are interpreters and translators considered high AI exposure roles: Yes

Are writers and authors considered high AI exposure roles: Yes

What is the key distinction between bookkeeper AI risk and strategic accountant AI risk: Task composition, not occupation title

What is the "repeated exposure with limited mobility" trap: High automation risk combined with few adjacent career exits

What factor will most determine long-term AI job displacement outcomes: Labour mobility

Is "AI exposure" the same as "AI replacement": No

Does AI exposure automatically mean job loss: No

At what level must AI job risk be assessed for accuracy: The individual occupation level

At what level is AI job risk most precisely assessed: The task level within an occupation

What is the most important first step for workers in high-risk roles: Conduct an honest task-level audit of their role

Which task characteristics indicate lower AI replacement risk: Physical, interpersonal, and judgment-dependent tasks

Which task characteristics indicate higher AI replacement risk: Routine, structured, and text-based tasks

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