Top 5 Jobs in Government That Are Most at Risk from AI in Australia - And How to Adapt

Australian public servants should care about AI because it's already changing how services are delivered, how decisions are made, and how jobs are defined: legal analysis outlines emerging guardrails such as a Voluntary AI Safety Standard and proposals to treat employment-related systems as “high‑risk” (Baker McKenzie analysis: AI and the future of work in Australia), while the Digital Transformation Agency is publishing government guidance to shepherd safe deployments (Digital Transformation Agency guidance on safe and responsible AI in government).

With agencies under budget and capability pressure - yet seeing big wins such as halved Medicare processing times - the pragmatic path is risk-aware adoption combined with targeted upskilling; practical courses like the Nucamp AI Essentials for Work bootcamp registration teach staff to use AI tools, write effective prompts, and protect privacy so public trust isn't the casualty of efficiency.

“There are foundational elements that I've seen done really well in some federal government agencies, which is information governance. We can't do cyber effectively without really exceptional information governance officers... knowing where your data is, and what's important, is a foundation to do effective cyber after that.” - Carl Heine

The top-five list was built on Australia‑specific evidence rather than alarm: economy‑wide modelling and occupation‑level “exposure” scores underpin the selection, with the Jobs and Skills Australia Generative AI Capacity Study serving as the anchor for which roles showed repeated automation risk (notably office clerks, receptionists, bookkeepers and entry‑level communications roles) - see the Jobs and Skills Australia coverage for details Jobs and Skills Australia study: Aussie jobs most vulnerable to AI.

That modelling used Computable General Equilibrium methods and exposure scores across 998 occupations to estimate how much tasks might be automated versus augmented; other analysis found only about 4% of the workforce in high‑automation roles while roughly one in five occupations have medium‑to‑high likelihood of automation, so the emphasis was on task‑level risk, demographic concentration (older workers, First Nations and people with disability), and real‑world signals from firms and sectors to prioritise administrative and clerical roles for practical adaptation planning Business Insider analysis of JSA generative AI modelling.

The result is a pragmatic shortlist aimed at where targeted training and governance will do the most good.

“While we haven't yet seen an impact on entry-level roles in Australia, it will be important that the labour market continues to provide these valuable formative roles, which provide foundational experiences in their careers.” - Commissioner Barney Glover

Administrative officers and office clerks sit squarely in the firing line because so much of their day is structured, repetitive and rules-based - the Jobs and Skills Australia modelling flagged routine clerical roles (general clerks, receptionists, accounting clerks and bookkeepers) as among the most automatable, a finding summarised in the Business Insider coverage of the JSA report (Business Insider: Jobs and Skills Australia clerical automation risk analysis).

Practical examples are already visible: AI systems that extract fields from forms or generate meeting summaries can turn a 40‑minute admin task into a 40‑second review, while scheduling, email triage, document processing and data‑entry are being handled by tools described in industry rundowns of AI-driven office automation (SwipedOn guide to AI scheduling, email triage, and document automation).

The sensible response for Australian agencies is threefold: retrain clerical staff to supervise and validate AI outputs (prompting, verification, exception handling), invest in governance and responsible‑AI checklists such as the Responsible AI Self‑Assessment to protect privacy and fairness (Responsible AI Self‑Assessment (NAIC) guidance for government agencies), and redesign roles so human strengths - judgement, stakeholder relationships and complex problem solving - absorb the value freed by automation.

“The quality of adoption and implementation will be instrumental in achieving the benefits of labor-augmenting tools.”

Receptionists and front‑desk staff are the first human contact for citizens, and AI receptionists are already reshaping that moment: automated systems can answer simultaneously, offer 24/7 booking, integrate with calendars and practice‑management systems, and reliably cut no‑shows (AI receptionists typically reduce no‑show rates by 22–25% while capturing far more calls than traditional desks) - see AI receptionist no‑show reduction and booking efficiency (Arini).

That speed - turning a 10‑minute hold into an instant confirmation - can recover lost revenue and free staff for complex tasks, yet cautionary reporting shows the human touch still matters when callers need empathy or local context: KFF Health News analysis of AI call centers and human medical receptionists.

The practical middle path for Australian agencies is hybrid deployment: let AI handle routine routing and reminders while upskilling people to manage escalations, audit outputs and protect privacy, as industry guides recommend in hybrid AI receptionist deployment best practices (MyAIFrontDesk).

“The rapport, or the trust that we give, or the emotions that we have as humans cannot be replaced.” - Ruth Elio

Bookkeepers and accounts clerks in Australian government agencies are squarely in the line of fire because much of their work is repeatable and rules‑based - research summarised by Accountants Daily warns that bookkeepers could see almost 40% of task‑time affected by AI, so routine data entry, transaction categorisation and reconciliations are the immediate targets (Accountants Daily: bookkeepers may lose almost 40% of tasks to AI).

Practical automation is already mature: automated transaction categorisation, invoice processing, payroll checks and anomaly detection free up time and improve accuracy, and case studies show specialised tools can shave entire month‑end closes from weeks to minutes (CFO Magazine: AI use cases in accounting, including Docyt's GARY).

In Australia the shift is uneven - some firms and agencies pilot AI while others stall - creating a policy and training opportunity for Chartered Accountants and finance teams to lead on governance, validation and ethical deployment (Outbooks: AI in accounting - adoption, tools and governance in Australia).

The sensible adaptation is clear: treat AI as a productivity partner, retrain clerical staff to validate outputs and handle exceptions, and refocus human roles on judgement, client advice and fraud detection - in other words, trade repetitive ledger work for higher‑value financial insight and controls.

“Those in white collar roles should take it on themselves to upskill and evolve – enhancing soft skills like creativity, communication and leadership, skills that can't be easily replicated by generative AI.”

Generative AI is already rewriting routine comms work in Australia - automating media monitoring, first‑draft press releases and social media scheduling so teams spend less time hunting mentions and more time shaping strategy; Adoni Media explains how tools handle content creation and monitoring but can't replace the local newsroom relationships that win coverage (Adoni Media article on AI making PR professionals more important in Australia), while LexisNexis notes that AI's gift is speed - turning a blank page and a blinking cursor into usable first drafts and sharper media intelligence (LexisNexis analysis of AI's impact on public relations and media intelligence).

The sector sees this as an opportunity: a Sandpiper survey found 86% of communications professionals view AI positively, yet training and governance lag (only around half have any AI training and few have formal policies), so the practical Australian pathway is clear - treat generative tools as time‑savers that free people to steward relationships, add judgement and audit outputs, backed by targeted upskilling and clear use guidelines to protect trust and accuracy (Sandpiper survey on AI adoption and governance in communications).

A vivid takeaway: when AI cuts an hour of drafting to minutes, that reclaimed hour should buy a conversation with the editor who actually decides the story.

“The power of knowing who to talk to, knowing the editors, and more importantly, knowing how to talk their language can not be underestimated, Leisa said.”

Records, registry and data‑entry teams are especially exposed because their work is predictable, high‑volume and rule‑bound: modern AI pipelines can ingest PDFs, photos and emails, run OCR and NLP to extract fields, validate and enrich records, then push clean entries straight into case or CRM systems - a practical how‑to is laid out in an AI data-entry automation guide (AI data-entry automation guide - AI Acquisition).

Real‑world public‑sector examples show the scale of change: Tyler Technologies clients report 50–85% reductions in manual review, while RSM's record‑digitisation work cut discovery and service‑request times from weeks to days and even showed that a human would have needed decades to trawl archives (Tyler Technologies podcast on document automation, RSM record-digitisation case study).

Accuracy gains - modern IDP systems can approach 99% extraction rates - mean agencies can scale without linear headcount, but only if people are reskilled to manage exceptions, audit trails, and governance so sensitive records stay secure and trustworthy; imagine replacing a months‑long backlog with near‑real‑time search, not by magic but by engineering and oversight.

“AI doesn't maintain human touch in prep public services. It improves it by allowing the county government to utilize their scarce and valuable human resources to provide more contact with the public and get them out of doing heads down keyboard data entry, document review jobs.” - Henry Sal

Closing the gap between risk and opportunity in Australian public service means moving from broad principles to a short, practical checklist: embed AI governance into existing decision hierarchies and name accountable owners; mandate human intervention points for any high‑impact use and publish simple transparency materials so citizens can see when a machine helped make a decision; treat data quality as a first‑order policy (use data statements and measurable metrics); require tailored risk assessments and monitoring plans for each use case; and invest in targeted upskilling so routine tasks are supervised, validated and redesigned for higher‑value work.

These priorities echo the Commonwealth's guidance on adopting AI in the public sector and the DTA's Policy for the responsible use of AI in government, which stress accountability, contestability and privacy as core safeguards (Australian Government guidance: Adoption of AI in the Public Sector, Digital Transformation Agency policy: Responsible use of AI in government); they also align with national plans to grow capability and adoption while protecting trust.

For teams ready to act now, practical training such as the Nucamp AI Essentials for Work bootcamp teaches promptwriting, tool use and governance skills in 15 weeks - an immediate step to turn policy into on‑the‑job capability (Nucamp AI Essentials for Work bootcamp registration), because a robust governance checklist plus trained staff is what prevents a single automated error becoming a reputational crisis.

“Australia needs to make the most of the opportunities that artificial intelligence and other emerging technologies like robotics and quantum provide.” - Tim Ayres