Myanmar's approximately 22.3 million workers score a weighted average AI exposure of 3.19/10 - the lowest in this batch and one of the lowest in Southeast Asia. The dominant reason is structural: agriculture employs 36% of the workforce, the highest share in this group of five countries, producing a composition heavily weighted toward low-exposure physical work. But Myanmar's story in 2026 cannot be told through occupation data alone. The February 2021 military coup - which overthrew an elected civilian government and triggered international sanctions, banking system disruption, and over 1.5 million internal displacements - fundamentally altered the country's economic trajectory. Yangon was developing a nascent digital economy before 2021, with Wave Money (mobile payments), CB Bank, and KBZ Pay building fintech infrastructure that reached approximately 30 million mobile subscribers. Internet shutdowns imposed by the military government since 2021 have severely curtailed digital economy activity. The data in this analysis relies on ILO ILOSTAT and Central Statistical Organisation (CSO) Myanmar Labour Force Survey 2022-2023, which itself reflects a disrupted formal economy, and should be read with appropriate uncertainty about current conditions.
Myanmar's occupation data comes from ILO ILOSTAT and the Central Statistical Organisation (CSO) Myanmar Labour Force Survey 2022-2023. The CSO survey uses ISCO-08 classification and covers the formal economy across Myanmar's estimated 22.3 million employed workers. A critical data caveat applies: the 2021 military coup severely disrupted both the formal economy and the statistical infrastructure used to collect this data. CSO operations were partially suspended following the coup, and post-2021 surveys reflect a formally employed population that is smaller and differently distributed than the pre-coup baseline. Estimates in this analysis use available 2022-2023 data with the understanding that actual current conditions involve substantially more economic informality and disruption than the figures capture.
| Occupation Group | AI Score | Workers (est.) | Share (est.) |
|---|---|---|---|
| Clerical support workers (ISCO 4) | 8.5/10 | ~676K | ~3.0% |
| Professionals (ISCO 2) | 6.5/10 | ~723K | ~3.2% |
| Managers (ISCO 1) | 5.5/10 | ~140K | ~0.6% |
| Technicians and associate professionals (ISCO 3) | 5.5/10 | ~286K | ~1.3% |
| Service and sales workers (ISCO 5) | 3.5/10 | ~4.44M | ~19.9% |
| Skilled agricultural workers (ISCO 6) | 3.0/10 | ~8.10M | ~36.4% |
| Plant and machine operators (ISCO 8) | 3.0/10 | ~1.26M | ~5.7% |
| Armed forces (ISCO 0) | 2.5/10 | ~8.7K | ~0.04% |
| Craft and related trades (ISCO 7) | 2.5/10 | ~2.89M | ~13.0% |
| Elementary occupations (ISCO 9) | 2.0/10 | ~3.75M | ~16.9% |
The armed forces share is negligible in this dataset at approximately 0.04% of the formally classified workforce - the CSO Labour Force Survey counts civilian occupational employment, not military personnel, so this figure should not be read as a measure of the Tatmadaw's actual size or its broader economic influence, which extends well beyond what shows up in this specific occupation classification.
Within the clerical group (ISCO 4), pre-coup Myanmar had been building a formal banking sector workforce concentrated in Yangon. The Central Bank of Myanmar, along with private banks including KBZ Bank, AYA Bank, and CB Bank, employed thousands of clerical and administrative workers in account processing, loan documentation, and compliance roles. Following the coup, Western sanctions targeted state-owned Myanmar Economic Bank and Myanmar Foreign Trade Bank, while private banks faced runs and operational disruptions in early 2021. The surviving banking workforce in formal employment still faces AI exposure at 8.5/10 for clerical functions, but the absolute worker count has contracted since the pre-coup peak.
Understanding Myanmar's AI risk profile in 2026 requires understanding what the country was before February 2021 and what the coup changed. Between 2011 and 2021, Myanmar underwent one of the most rapid economic and digital transformations in Southeast Asian history. GDP grew at approximately 6% annually for most of the 2010s. Mobile penetration went from near zero in 2012 to approximately 130% SIM penetration by 2020, driven by the entry of Telenor and Ooredoo. Internet access expanded from essentially zero to over 30 million connected users. A fintech ecosystem emerged around Wave Money (a joint venture between Yoma Bank and First Myanmar Investment), KBZ Pay, and CB Pay, serving millions of unbanked Myanmar citizens for the first time.
The coup reversed much of this. The military government imposed internet shutdowns - most severely in 2021, when mobile internet was suspended entirely for extended periods. International payments infrastructure was cut off following Western sanctions. Telenor sold its Myanmar operations under duress in 2021. Foreign direct investment collapsed. An estimated 1.5 million people were internally displaced by conflict between the military and resistance forces across multiple regions. The formal economy contracted by approximately 18% in 2021 per World Bank estimates, with further contraction in subsequent years as conflict continued.
Myanmar's jade and gem mining sector - supplying approximately 90% of the world's gem-quality jade and a significant share of rubies - continued operating under military control despite Western import bans. The sector employs substantial numbers of workers in Kachin and Shan states in extraction, sorting, and transport roles. These roles score at or below 3.0/10 on AI exposure and are physically intensive enough that they are unlikely to face software-based AI displacement regardless of political conditions. The garment sector - employing several hundred thousand workers primarily in Yangon and Mandalay - partially recovered after initial post-coup disruption, with some brands returning to Myanmar sourcing despite ethical concerns. Garment assembly workers, part of the craft and related trades group at 2.5/10 AI exposure, face long-run robotics risk but not near-term AI software disruption.
Myanmar's low-exposure majority - agriculture, elementary work, and trades - represents roughly 71% of the estimated workforce, making it one of the most physically-intensive large workforces in Southeast Asia.
| Occupation Group | AI Score | Workers (est.) | Share (est.) |
|---|---|---|---|
| Elementary occupations (ISCO 9) | 2.0/10 | ~3.75M | ~16.9% |
| Armed forces (ISCO 0) | 2.5/10 | ~8.7K | ~0.04% |
| Craft and related trades (ISCO 7) | 2.5/10 | ~2.89M | ~13.0% |
| Plant and machine operators (ISCO 8) | 3.0/10 | ~1.26M | ~5.7% |
| Skilled agricultural workers (ISCO 6) | 3.0/10 | ~8.10M | ~36.4% |
Agricultural workers at 36.4% - concentrated in rice, pulses, vegetables, and fisheries across the Irrawaddy Delta, Shan plateau, and coastal regions - score 3.0/10. Myanmar's agricultural workforce is among the largest in Southeast Asia in absolute terms. Small-hold farmers managing monsoon rice cycles, fishermen on the Irrawaddy River and Gulf of Martaban, and market gardeners supplying Yangon's urban food markets all perform physical, seasonal work that current AI technology cannot address. Elementary workers at 16.9% - construction laborers, market vendors, domestic workers - score 2.0/10 and face essentially no meaningful AI software displacement risk in any realistic near-term scenario.
Myanmar's 3.19/10 average is the lowest in this batch, but this figure should not be interpreted as "low risk" in any simple sense. For most Myanmar workers in 2026, the risks to their livelihood come from conflict, economic contraction, currency depreciation, and institutional breakdown rather than from AI software displacing their specific occupation tasks. The political situation is the dominant economic risk variable, and no occupation exposure score captures it. The risk velocity score of 0.2/10 - one of the slowest disruption timelines in the entire dataset - reflects exactly this: the coup's damage to internet access, banking infrastructure, and foreign investment has slowed the pace at which AI tools could realistically reach Myanmar's formal economy, not accelerated it.
For the estimated 1.7 million workers in professional, clerical, and technical roles in Yangon and other cities who operate within the formal economy in 2026, AI exposure is a secondary but real consideration. If and when political and economic stability is restored, Myanmar's white-collar workforce will resume participation in the broader Southeast Asian digital economy - and at that point the clerical (8.5/10) and professional (6.5/10) exposure scores will become practically relevant again. Workers with professional qualifications and digital skills who have maintained those skills through the disruption period will be better positioned for whatever economic recovery eventually occurs.
Recovery resilience at 5.5/10 is moderate - not the lowest in this dataset, contrary to what might be assumed given the severity of the coup's disruption. This reflects the underlying methodology, which weighs workforce human-capital investment and social-protection coverage rather than directly measuring political stability or institutional damage. It should not be read as a claim that Myanmar's institutions are currently functioning well; rather, it means that whatever structural capacity exists for workforce transitions has not collapsed to the degree the political situation might suggest. Workers in exposed roles who have the ability to build digital skills through online platforms (internet access permitting) or international networks are taking the most practical steps available given the structural constraints.
Interactive breakdown of every occupation group, sortable by AI exposure score and worker count.
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AI exposure, robotics, offshoring and work-from-home scores are model-estimated: an AI language model (Claude Opus 4.7) scored each ISCO-08 occupation group on 28 May 2026, guided by published research. They are informed judgements, not measurements, and not forecasts of job losses. "Risk velocity" and its time windows come from a digital-infrastructure index (broadband and secure servers), not from occupations or measured AI adoption. Read the full methodology and how the scores were checked.
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