Key findings
- Clerical support workers score 8.5/10 on AI exposure, covering 88,120 workers (2.21%). Port Moresby's government administrative offices, the Bank of Papua New Guinea, commercial banks, and the administrative functions of mining and LNG companies employ this group. Despite the small percentage, these are the workers in PNG most directly in the path of AI productivity tools.
- Professionals at 9.7% (388,970 workers) is anomalously high for an economy at PNG's development level. The explanation is the enclave resource extraction economy: ExxonMobil's LNG project (operational from 2014), Ok Tedi, Porgera, and Lihir Island gold and copper mines all employ professional engineers, geologists, and project managers. The international NGO sector (World Vision, Care, Oxfam, and dozens of others) employs professionals in Port Moresby and provincial capitals. These are workers with real AI exposure at 6.5/10.
- Agriculture at 63.5% (2.54M workers) scoring 2.0/10 is the overwhelming reality of PNG employment. Subsistence farming, coffee and cocoa smallholding in the highlands, betel nut trade, and fishing across hundreds of islands and river systems - none of this is AI-threatened in any meaningful near-term timeframe.
- Technicians at 5.12% (205,140 workers) scoring 5.5/10 reflect the skilled technical workforce in the mining and LNG sector - laboratory technicians, mechanical technicians, and electrical technicians working in the resource extraction enclaves. This is a relatively exposed group for PNG's overall risk level.
- The weighted average of 3.52/10 is kept low by the massive agricultural base, despite the above-average professional share. PNG is a dual economy: a small, relatively modern resource and government sector in Port Moresby and mining towns, alongside a vast subsistence rural economy that covers most of the country's 8-9 million population.
4M workers in a dual economy
Papua New Guinea's labour market has a structure that does not fit standard developing economy patterns. Most lower-middle income countries have a large agricultural sector transitioning toward manufacturing and services. PNG has a large agricultural sector plus an internationally operated extractive enclave - but relatively little domestic manufacturing or formal services outside Port Moresby.
The NSO Labour Force Survey 2017 captures this structure. The resource extraction sector is operated largely by international companies (ExxonMobil, Barrick Gold, Ok Tedi Mining, Newcrest) with expatriate professionals and skilled technicians alongside local workforce. The professional share in the data reflects both Papua New Guinean professionals (particularly teachers, nurses, public servants) and the expat workforce in the extractive sector.
PNG's geography reinforces this dual structure. The country covers 462,840 km2 of mostly mountainous, heavily forested terrain accessible only by air to many highland communities. The limited road network means that formal sector employment is concentrated in Port Moresby, Lae, Madang, the Highlands Highway corridor, and the mining and LNG operation sites. Rural areas are overwhelmingly subsistence agricultural, with limited connectivity to formal labour markets.
The most AI-exposed jobs in Papua New Guinea
Clerical support workers score 8.5/10 on AI exposure in PNG, covering 88,120 workers (2.21%). The National Department of Health, the Department of Education, the Internal Revenue Commission, and Port Moresby commercial banks employ this category. Mining companies (ExxonMobil, Barrick) have administrative headquarters both in PNG and internationally that employ clerical staff. These workers are directly exposed to AI-driven productivity tools as digital infrastructure in Port Moresby's formal sector improves.
Professionals at 6.5/10 cover 388,970 workers (9.73%). Within the professional category, the breakdown matters significantly for AI exposure. PNG teachers - the largest professional sub-group - are in schools across 22 provinces, often with no electricity or internet. AI tools are irrelevant to most of them in the near term. Mining and LNG engineers, by contrast, work in modern, digitally connected environments and are the most AI-exposed professionals in PNG. Doctors and nurses in hospitals are a middle case - urban hospitals have some digital infrastructure, rural health posts have essentially none.
| Occupation Group (ISCO-08) | AI Score | Workers | % of Total |
|---|---|---|---|
| Clerical support workers (4) | 8.5/10 | 88.1k | 2.21% |
| Professionals (2) | 6.5/10 | 389.0k | 9.73% |
| Managers (1) | 5.5/10 | 29.0k | 0.73% |
| Technicians and assoc. professionals (3) | 5.5/10 | 205.1k | 5.12% |
| Service and sales workers (5) | 3.5/10 | 221.8k | 5.55% |
| Plant and machine operators (8) | 3.0/10 | 153.3k | 3.83% |
| Craft and related trades workers (7) | 2.5/10 | 89.9k | 2.25% |
PNG's 9.7% professional share is not a sign of a highly educated formal economy - it is the signature of an enclave resource extraction sector operated by international companies, alongside a teacher and health worker workforce distributed across hundreds of remote communities.
The safest jobs in Papua New Guinea
Agricultural workers score 2.0/10 on AI exposure in PNG, covering 2.54 million workers (63.5%). Papua New Guinea's highlands produce coffee and tea exported internationally, but the majority of agricultural employment is subsistence - sweet potato, taro, and sago production for household consumption. The Ramu and Markham valleys produce rice and oil palm commercially. Coastal and island communities depend heavily on fishing. None of these activities are AI-threatened in any near-term scenario, and many are inaccessible to digital technology at all.
Elementary occupations at 2.0/10 cover 385,790 workers (9.65%). Craft and trades workers at 2.5/10 cover 89,930 workers (2.25%) - carpenters, auto mechanics, plumbers, and artisans concentrated in urban areas. Plant and machine operators at 3.0/10 cover 153,310 workers (3.83%), operating mining equipment, LNG facility machinery, and agricultural processing plants.
| Occupation Group (ISCO-08) | AI Score | Workers | % of Total |
|---|---|---|---|
| Skilled agricultural workers (6) | 2.0/10 | 2,537.7k | 63.50% |
| Elementary occupations (9) | 2.0/10 | 385.8k | 9.65% |
| Craft and related trades workers (7) | 2.5/10 | 89.9k | 2.25% |
| Plant and machine operators (8) | 3.0/10 | 153.3k | 3.83% |
What this means for Papua New Guinea workers
For the 63.5% of PNG workers in agriculture, AI job risk is not a near-term concern. The primary employment challenges they face are entirely different: market access for cash crops, climate-driven disruptions to subsistence agriculture, land tenure disputes, and the lack of rural financial services. AI tools might eventually help with agricultural advisory services delivered via mobile phone (a model pioneered in Kenya and India), but direct AI displacement of agricultural labor in PNG is many decades away.
The formal sector workers in Port Moresby, Lae, and mining towns face a different picture. As PNG's digital infrastructure improves - particularly the Coral Sea Cable System connecting PNG to Australia with high-speed internet, operational since 2019 - AI tools become progressively more accessible to formal sector employers. Clerical workers in government departments and mining company offices are the most immediately exposed. Professional engineers in the resource sector already use AI-assisted modeling and analysis tools from their international parent companies.
The structural risk for PNG's development trajectory is that AI-driven productivity gains in the professional and technical sectors may reduce the employment absorption capacity of the formal economy just as PNG's young population (median age approximately 21) enters the workforce. The country's development model relies on resource extraction revenues funding government services and infrastructure, while employment growth happens in agriculture and the informal sector. AI changes the calculus for the formal sector component of this model.
See PNG's full occupation breakdown
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Methodology
Employment figures are from the Papua New Guinea Labour Force Survey 2017, conducted by the National Statistical Office (NSO) of Papua New Guinea. ILO ILOSTAT publishes the aggregated data (CC BY 4.0). Data year: 2017. AI exposure scores are research-based estimates per ISCO-08 group, informed by Frey-Osborne (Oxford), OECD, and IMF studies on task-level automation.
Frequently asked questions
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Related analyses
Data sources
- ILO ILOSTAT - Papua New Guinea Labour Force Survey 2017 (CC BY 4.0)
- National Statistical Office (NSO), Papua New Guinea
- Frey, C.B. and Osborne, M.A. (2017). The future of employment. Technological Forecasting and Social Change.
- OECD - The Future of Work and Skills
- IMF - Gen-AI: Artificial Intelligence and the Future of Work (2024)