Nepal AI Job Risk 2026: Which Occupations Are Most at Risk?
Nepal's approximately 7.5 million formal sector workers score a weighted average AI exposure of 3.78/10 - a moderate score in the South Asian context, shaped by an occupation mix that balances a growing urban knowledge economy against a large agricultural and informal worker base. The most economically distinctive feature of Nepal's labour market is not what happens inside the country but what hap
Nepal's approximately 7.5 million formal sector workers score a weighted average AI exposure of 3.78/10 - a moderate score in the South Asian context, shaped by an occupation mix that balances a growing urban knowledge economy against a large agricultural and informal worker base. The most economically distinctive feature of Nepal's labour market is not what happens inside the country but what happens outside it: remittances at 22-26% of GDP (World Bank 2024) make Nepal one of the world's most remittance-dependent economies, with an estimated 3.5 million Nepali workers employed in Saudi Arabia, the UAE, Qatar, Kuwait, Malaysia, and India. The domestic workforce data that drives this analysis covers the formal sector, but the full picture of Nepali labour includes this massive external component. Kathmandu's technology scene is growing with genuine depth: Fusemachines, founded by Nepali-American AI researcher Sameer Maskey, brings AI education and implementation back to Nepal; eSewa serves 3 million users as the country's largest digital payment platform; and Khalti has established a digital wallet ecosystem. Nepal's hydropower potential - ranked sixth globally at 83 gigawatts - is beginning to translate into industrial development that will reshape the energy-intensive manufacturing workforce over the coming decade.
Key Findings
Highest AI exposure: Clerical support workers at 8.0/10 - peak risk group, approximately 225,000 workers (~3%)
~7.5M formal sector workers; weighted average 3.78/10 (ILO ILOSTAT / CBS Nepal Labour Force Survey 2024-2025)
Safest group: Elementary occupations at 1.5/10 (~1.275M workers, ~17% of formal workforce)
Remittances 22-26% of GDP from 3.5M workers abroad - this external workforce is not captured in the domestic LFS data
7.5M
Formal workers (2025)
8.0/10
Highest AI score
3.78/10
Avg AI exposure
The most AI-exposed occupations in Nepal
Nepal's occupation data comes from ILO ILOSTAT and the Central Bureau of Statistics Nepal (CBS) Labour Force Survey 2024-2025. The CBS conducts periodic national LFS surveys using ISCO-08 occupation classification, covering Nepal's formal sector employment. A critical data context: the CBS estimates that 60% or more of Nepal's total workforce is in informal employment - self-employed subsistence farmers, informal traders, construction day labourers, and domestic workers. The 7.5 million figure used in this analysis represents the formal sector estimate; the total Nepali workforce including informal workers is substantially larger, and those informal workers are concentrated in the lowest AI exposure groups (agriculture, elementary work, basic trades).
Occupation Group
AI Score
Workers (est.)
Share (est.)
Clerical support workers (ISCO 4)
8.0/10
~225K
~3%
Professionals (ISCO 2)
6.4/10
~525K
~7%
Technicians and associate professionals (ISCO 3)
5.7/10
~375K
~5%
Managers (ISCO 1)
4.9/10
~150K
~2%
Service and sales workers (ISCO 5)
3.1/10
~975K
~13%
Craft and related trades (ISCO 7)
2.5/10
~1.05M
~14%
Plant and machine operators (ISCO 8)
2.6/10
~675K
~9%
Elementary occupations (ISCO 9)
1.5/10
~1.275M
~17%
Skilled agricultural workers (ISCO 6)
2.8/10
~1.875M
~25%
Armed forces (ISCO 0)
2.2/10
~375K
~5%
Nepal's armed forces share at approximately 5% is relatively elevated, reflecting the Nepal Army (approximately 95,000 personnel), the Armed Police Force Nepal, and the Nepal Police - which together create a large uniformed services workforce. Nepal also supplies Gurkha soldiers to the British Army (Brigade of Gurkhas) and Indian Army (Gorkha regiments), as well as Singapore Police Force contingents, under longstanding bilateral agreements. These external military service roles generate significant remittance flows and are not typically captured in domestic LFS data as they are employed by foreign governments.
Within clerical support (ISCO 4), Kathmandu's commercial banking sector is the primary employer. Nepal Rastra Bank (central bank) oversees 27 commercial banks and 17 development banks, all of which employ clerical workers in account operations, loan documentation, and compliance functions. The Nepal Stock Exchange (NEPSE) and associated brokerage firms employ financial clerks and data entry workers in a market that has seen retail investor participation grow substantially with mobile trading apps. These workers at 8.0/10 AI exposure are in roles where AI tools for document verification, account reconciliation, and customer query handling are directly applicable and likely to be deployed as Nepali banks adopt next-generation core banking systems over the 2026-2030 period.
Remittance economy, Kathmandu fintech, and hydropower
Nepal's remittance economy is the defining structural feature of its labour market. The World Bank estimates Nepal received approximately $9-10 billion USD in remittances in 2024, representing 22-26% of GDP - one of the highest ratios globally, comparable to countries like Tajikistan and Tonga. The majority originates from Nepali migrant workers in Gulf Cooperation Council countries (Saudi Arabia, UAE, Qatar, Kuwait, Bahrain, Oman), Malaysia, and India. Approximately 500,000 Nepali workers receive labour migration approvals annually through the Department of Foreign Employment (DoFE), predominantly for construction, domestic service, manufacturing, and hospitality roles abroad.
The AI exposure question for this remittance workforce is different from the domestic question. Nepali workers in Gulf construction - scaffolding, concrete work, masonry, infrastructure labor - face minimal AI software displacement risk (2.5/10 equivalent), but they are subject to labour market changes in the destination countries including Vision 2030 localization policies in Saudi Arabia (Nitaqat) and Emiratisation in the UAE that may reduce demand for certain migrant roles. The more direct AI concern for the remittance economy is what happens to the remittance transfer channels themselves: AI-powered fintech platforms (Wise, Remitly, and local Nepal-facing apps like IME Pay, Prabhu Money) are automating remittance processing, reducing employment in money transfer operations - but this is a relatively small worker count.
Fusemachines stands out as Nepal's most internationally recognised AI company. Founded by Dr. Sameer Maskey (Columbia University AI PhD), Fusemachines has built an AI education program across Nepal through its Fusemachines AI Fellowship, training hundreds of Nepali engineers in machine learning. The company also provides AI implementation services to clients in the US and Nepal, positioning Kathmandu as a potential AI services hub. eSewa - Nepal's largest digital payment platform with 3 million registered users - is owned by F1Soft Group, which also runs a significant fintech portfolio including Fonepay (interoperable payment network). These companies create technology employment that, while higher-exposure (6-8/10 for software roles), also represents Nepal's pathway to higher-value economic activity.
The safest jobs from AI in Nepal
Nepal's agricultural, trades, and elementary workforce - the physical economy majority - represents approximately 65% of the formal workforce and essentially all of the informal sector.
Occupation Group
AI Score
Workers (est.)
Share (est.)
Elementary occupations (ISCO 9)
1.5/10
~1.275M
~17%
Armed forces (ISCO 0)
2.2/10
~375K
~5%
Craft and related trades (ISCO 7)
2.5/10
~1.05M
~14%
Plant and machine operators (ISCO 8)
2.6/10
~675K
~9%
Skilled agricultural workers (ISCO 6)
2.8/10
~1.875M
~25%
Agricultural workers at 25% of the formal workforce - and a much larger share of total including informal - are distributed across Nepal's three geographic zones: the Terai plains (rice, wheat, sugarcane), the hill zone (maize, millet, vegetables, tea), and the mountain zone (potatoes, barley, livestock). Terraced hill farming in Nepal's Middle Hills - the visual iconic landscape of the country - is among the most difficult agricultural settings globally to mechanise, let alone automate with AI. The combination of steep terrain, micro-plot sizes, diverse crop varieties, and monsoon-dependent water management means these agricultural roles at 2.8/10 face essentially no meaningful AI software displacement risk in any near-term horizon. Construction workers in Nepal's building sector - which is active given reconstruction after the 2015 earthquake and ongoing urban development in Kathmandu Valley - score 2.5/10 and are critical to infrastructure development for hydropower projects.
What this means for you
Nepal's 3.78/10 average places it in a moderate position among South Asian economies - above Bangladesh and Pakistan, below Sri Lanka. The risk velocity of 6.5/10 reflects the real pace of digital adoption in Kathmandu's urban economy: smartphone penetration above 60% per NTA (Nepal Telecommunications Authority) 2024, growing fintech adoption, and a young educated workforce that is actively consuming AI tools. For workers in Kathmandu's banking sector, government administration, and emerging tech sector, the AI exposure timeline is real and likely to compress as Nepal's digital infrastructure matures.
The most distinctive risk in Nepal's context is displacement of the remittance-generating workforce abroad rather than the domestic workforce. If Gulf construction demand declines due to Vision 2030 localization, or if Malaysian manufacturing automates, the shock to Nepal's $9-10 billion remittance inflow would be a macroeconomic disruption far larger than domestic AI displacement of clerical workers. Nepal's economy is structurally dependent on export of human labour - and AI-driven automation in destination countries is therefore an imported risk that does not show up in the domestic occupation exposure scores.
Recovery resilience at 4.3/10 reflects Nepal's genuine institutional limitations - political instability (Nepal has had over a dozen governments in 15 years), limited fiscal capacity, difficult geography for delivering retraining programs, and infrastructure gaps. The positive signals are Kathmandu's genuine tech ecosystem (Fusemachines, eSewa, F1Soft, CloudFactory which employs thousands in AI data annotation), the Gurkha military tradition that creates an internationally respected professional credential for armed forces workers, and Nepal's hydropower development that will create construction and electrical engineering demand over the 2026-2035 period. Workers who build digital skills within Nepal's growing tech sector, or who transition toward hydropower project support roles, are positioning for Nepal's most defensible economic growth drivers.
Explore Nepal's Full Occupation Data
Interactive breakdown of every occupation group, sortable by AI exposure score and worker count.
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Methodology: AI exposure scores are assigned at ISCO-08 sub-major group level and aggregated to major groups using employment-weighted averages. Employment data is from ILO ILOSTAT and the Central Bureau of Statistics Nepal (CBS) Labour Force Survey 2024-2025, covering approximately 7.5 million formal sector workers. Important caveat: CBS estimates that 60% or more of Nepal's total workforce is in informal employment and not captured in LFS data. The approximately 3.5 million Nepali workers employed abroad are also not included. Scores reflect task-level AI capability relative to occupation task profiles as of mid-2026. This analysis does not constitute career or financial advice.
Frequently Asked Questions
Clerical support workers score 8.0/10 - the highest in Nepal, with approximately 225,000 workers. Professionals score 6.4/10 with around 525,000 workers. Technicians score 5.7/10 with around 375,000 workers. Data from ILO ILOSTAT and Central Bureau of Statistics Nepal (CBS) Labour Force Survey 2024-2025.
Nepal has approximately 7.5 million formal sector workers, though 60% or more of the total workforce is informal. Weighted average AI exposure is 3.78/10. Risk velocity is 6.5/10. Recovery resilience is 4.3/10. Remittances at 22-26% of GDP reflect 3.5 million Nepali workers employed abroad in the Gulf, Malaysia, and India.
Elementary occupations score 1.5/10 and represent approximately 17% of Nepal's formal workforce at 1.275 million workers. Agricultural workers score 2.8/10 at 25% of the workforce. Craft and trades workers score 2.5/10 at 14%. Construction and agricultural roles in the informal economy face minimal AI software displacement.
Employment data comes from ILO ILOSTAT and the Central Bureau of Statistics Nepal (CBS) Labour Force Survey 2024-2025. This covers approximately 7.5 million formal sector workers using ISCO-08 occupation classification. Note that 60% or more of Nepal's total workforce is in the informal sector and not captured in formal LFS data.