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

Across governments worldwide the AI-in-public-services market is booming - Grand View Research puts the sector at USD 22.41 billion in 2024 and growing toward nearly USD 98.13 billion by 2033 - so small island states like Fiji need to pay attention to both risk and reward (AI in public services market forecast - Grand View Research).

2025 trends such as multimodal models, AI agents, assistive search and AI-powered citizen experiences (see Google Cloud public-sector AI trends for 2025) make routine back-office work, permit processing and data entry far more automatable, while also offering tools for climate and disaster planning - imagine a real-time early-warning pipeline that turns meteorological feeds into a 72-hour evacuation plan (real-time disaster early-warning AI use case).

For Fiji's public servants, the practical path forward is skills: short, workforce-focused training such as the AI Essentials for Work syllabus program equips staff to use AI responsibly and stay indispensable as roles evolve.

Selection combined empirical exposure and mobility evidence with practical, Fiji-focused testing: international patterns in the IMF study on Exposure to Artificial Intelligence and Occupational Mobility guided which occupations historically struggle to transition when automation rises, while EY's playbook on agency AI literacy, guardrails and training informed how readiness and governance change the risk calculus.

IMF study: "Exposure to Artificial Intelligence and Occupational Mobility" - IMF article on AI exposure and occupational mobility

EY guide: "Agency AI literacy, using guardrails and frameworks" - EY playbook for agency AI readiness

To ground those lenses locally, Nucamp's Fiji use cases - everything from real-time disaster early-warning to DigitalFIJI pilot ideas - tested whether automation is operationally plausible on the islands (Fiji real-time disaster early-warning AI use case).

Roles were scored on routineness and data intensity, prevalence of automated management tools, local deployment feasibility, and likely retraining pathways; positions high on routine/data exposure but low on mobility or governance readiness rose to the top.

The outcome is a concise, pragmatic shortlist of five back-office roles where an automated workflow could meaningfully shave routine hours - making the case for targeted upskilling now.

For a Tax Processing Officer at the Fiji Revenue and Customs Service (FRCS), routine VAT workflows are the clearest near-term exposure to automation: Fiji's bold move to digital invoicing and a VAT Monitoring System (VMS) plus real-time data analysis has already shifted VAT work from paper chases toward streamed transaction feeds, and those repeatable tasks - matching invoices, spotting non-lodgers or duplicate claims, and reconciling returns - are precisely what AI and ML excel at (RegTech article on Fiji VAT digitization and VMS).

That matters because VAT is the engine of Fiji's coffers (over 50% of tax revenue) and lodgment compliance has fallen notably, creating both risk and an opportunity for smarter automation to reduce leakage while improving taxpayer service (FRCS VAT compliance overview).

International experience shows AI can cut fraud and speed processing, but only when data quality, analytics and governance are strong - so officials should pair pilots and upskilling with guardrails to keep humans in the loop (PwC analysis: Role of AI in transforming tax administration).

Picture a desk once piled with stamped returns replaced by a live dashboard that flags one suspicious invoice in red before it becomes a costly refund - small, pragmatic automation like that preserves expert roles while shrinking the routine burden.

“Code is law” - Larry Lessig

Medical Records/Data Entry Clerks at the Ministry of Health and Medical Services sit squarely in the crosshairs of digitisation: regional analysis shows many Pacific countries - including Fiji - already run electronic patient record systems in hospitals while primary care often remains paper-based, so automation is uneven but inevitable without investment WHO report on Electronic Health Information Systems in the Pacific.

That uneven rollout matters because routine tasks - typing charts, transcribing lab results, and reconciling duplicate entries - are precisely what well‑implemented EHRs and simple automation can absorb, yet Fiji's path to scale is blocked by familiar barriers such as funding, infrastructure, capacity, legal frameworks and interoperability challenges that slow safe, equitable deployment EHR implementation challenges and solutions.

Local research also highlights public appetite for web-based records and online booking in Fijian hospitals, which raises both efficiency gains and governance questions that must be solved before clerks are simply displaced Study on management of electronic medical records in Fijian hospitals.

A vivid way to picture the trade-off: a clinic that once stacked paper files could become a dashboarded workflow where a single flagged mismatch - rather than ten hours of keystrokes - alerts a clinician, so the immediate policy task is targeted training, tested pilots and robust data standards to preserve jobs while letting systems shoulder repetitive work.

Enumerators at the Fiji Bureau of Statistics face one of the clearest task‑level risks from automation because much of their work - routine household interviews, structured questionnaires and repetitive data cleaning - maps to the kinds of routine manual and services tasks the World Bank flags as vulnerable in the East Asia and Pacific region (World Bank report: Future Jobs in East Asia and the Pacific); yet that same research shows the region can capture net gains if workers gain digital skills.

J.P. Morgan's analysis of the AI labour shift reinforces that not all displacement is inevitable: roles combining human judgement, local knowledge and rapport are harder to replace, so enumerators' community‑facing strengths are a real defensive asset (J.P. Morgan analysis: Jobs in the AI Revolution - Disruption Today, Growth Tomorrow).

Worker surveys also underline the mood on the ground - most people expect AI to touch their jobs, and many see training as the hinge between risk and opportunity - so practical steps like DigitalFIJI pilots that pair tablets with validation rules can turn a day of post‑survey cleaning into a ten‑minute verification, preserving field jobs while shrinking routine drudgery (DigitalFIJI pilot: tablets with validation rules for survey data (Fiji government AI guide 2025)).

Permit and Licensing Officers at the Ministry of Local Government, Housing and Community Development are on the front line of a fast‑moving digital shift: between January and November 2024 the Ministry logged 2,186 building permit applications worth about $1.16 billion (25% commercial), and the rollout of a unified Building Permit Approval System (BPAS) is meant to plug municipal portals into one live workflow to cut delays (Fiji building permit applications worth over $1B - FBC News).

That matters because current processing averages 141 days - a bottleneck that forces sites to sit idle - and the National Fire Authority has set a public target to chop that to 70 days with a September digital launch (NFA digital plan to reduce permit delays - FBC News).

Commercial off‑the‑shelf platforms built for councils - for example, Infor Pathway's end‑to‑end permitting, digital plan review and citizen portal capabilities - show how automated plan checks, online submissions and mobile inspection apps can turn repetitive file‑routing into exception handling and field verification (Infor Pathway local government permitting software), meaning officers can focus on judgement, compliance and community engagement rather than data chasing.

“It takes about 141 days for a developer to submit the applications for building permits to the very end. So 141 days. So with the introduction of BPAS, we've challenged ourselves to deliver this in accordance with what we have been challenged by the Deputy Prime Minister himself, which is to chop down from 141 days to 70 days. That is a demand.”

At the National Disaster Management Office, Administrative & Relief Coordination Officers sit at the intersection of life‑saving logistics and mountains of paperwork - a perfect target for AI and automation that can speed decision cycles without sidelining human judgement.

Fiji's shift toward anticipatory action and integrated risk planning means the NDMO now coordinates not just response but pre‑positioning of supplies and early evacuations, so routine tasks like beneficiary verification, grant eligibility checks and damage logging (all high‑volume, rule‑based work) are ripe for automation UNDRR case study: Fiji risk‑informed disaster management.

Satellite and drone imagery - paired with AI - can turn post‑storm confusion into near‑real‑time damage maps, helping coordinators prioritise villages that need boats or tarpaulins first (Earth observation technology for disaster damage assessment), while visual‑AI tools trialled for claims verification can speed relief payments to the worst‑affected households (Tractable and UN visual AI partnership for disaster claims verification in Fiji).

The practical takeaway for Fiji's NDMO: deploy pilots that automate back‑office flows and mapping, train coordinators to interpret AI outputs, and keep clear human checkpoints - so that a dashboard flags a pre‑positioned kit for pickup hours before a cyclone makes landfall, instead of a clerk burning the night entering forms.

Fiji has transitioned from a reactive model of disaster response to a proactive, risk-informed development paradigm - saving lives, safeguarding infrastructure.

Fiji's pragmatic next steps are clear: make AI readiness an explicit, funded priority in government planning (the Deputy Prime Minister has already signalled new education and communications funding and an Education Commission to guide integration - see reporting on national calls for AI readiness), benchmark progress against international tools like the Government AI Readiness Index, and fast‑track small, high‑value pilots that pair sturdy guardrails with measurable outcomes - for example, DigitalFIJI tablet pilots or a 72‑hour meteorological → evacuation pipeline that turns feeds into action, not paperwork (DigitalFIJI Stack and Nucamp disaster use case).

Parallel to pilots, equip staff with practical, job‑focused skills: short courses that teach prompt design, tool use and governance can keep roles resilient - Nucamp's AI Essentials for Work is a 15‑week, workforce‑oriented pathway (early bird $3,582; paid in 18 monthly payments) that converts routine risk into human‑led value.

Taken together - policy, pilots, and people - this three‑track approach turns a national readiness gap into an achievable roadmap for safer, faster public services in Fiji.