August 2026
New IMF analysis shows AI could boost productivity across the Asia-Pacific region, but its uneven reach threatens to deepen inequality between countries, sectors, and the workers within them.
New analysis from the International Monetary Fund finds that artificial intelligence is poised to reshape labour markets across the Asia-Pacific region, but its effects will not be evenly distributed. Advanced economies such as Singapore and Japan face far greater AI exposure than emerging markets, yet they are also better positioned to convert that exposure into productivity gains rather than job losses. Emerging and developing economies, by contrast, have far fewer roles that AI can meaningfully enhance.
The findings matter beyond the region. They illustrate a structural pattern likely to recur wherever AI adoption meets uneven digital infrastructure, skills, and regulation: technology amplifying existing advantage rather than levelling it. The analysis also surfaces a gender dimension, with women overrepresented in the occupational categories most exposed to displacement.
For governments, employers, and workforce planners, this is a call to design deliberate policy responses, safety nets, reskilling, and regulation, before disruption outpaces preparation.
The IMF’s analysis, drawn from its October 2024 Asia-Pacific Regional Economic Outlook, distinguishes between AI exposure and AI complementarity, a distinction that explains why the same technology can raise productivity in one economy while threatening livelihoods in another. Exposure alone does not determine outcome; what matters is whether a country’s existing job mix allows AI to augment work rather than substitute for it.
Advanced economies benefit from occupational structures weighted toward managerial, professional, and technical work, roles where AI tools tend to enhance output rather than replace the worker entirely. Emerging economies, with a higher share of manual and lower-skill roles, have fewer opportunities to convert AI exposure into productivity gains, leaving them more vulnerable to the downside of disruption without the upside of enhancement.
The gender pattern follows the same logic. Because women are more concentrated in service, sales, and clerical occupations, the categories most exposed to displacement, while men are more represented in roles AI is unlikely to touch in the near term, such as farm work and machine operation, the technology’s effects are filtered through existing occupational segregation rather than distributed neutrally.
For governments and regional institutions, the analysis reframes AI policy as an inequality-management challenge as much as a growth opportunity. Between-country gaps in AI-readiness, digital infrastructure, skills, and regulatory maturity, will determine which economies convert AI into productivity and which absorb its disruptive effects with less capacity to adapt.
For employers and workforce planners, the occupational and gender patterns identified are a signal to examine internal workforce composition now, rather than after disruption occurs. Roles concentrated in service, sales, and clerical functions warrant proactive reskilling investment, and organisations with gender-imbalanced staffing in these categories should treat that imbalance as a workforce-risk indicator, not an incidental detail.
For policymakers specifically, the IMF’s recommendations point to three levers: strengthening social safety nets, expanding reskilling infrastructure, and establishing regulatory frameworks for ethical AI use and data protection. Each requires action ahead of widescale AI adoption, not in response to it.
Organisations operating across Asia-Pacific, or advising governments and institutions that do, should treat this analysis as a prompt to map workforce exposure by occupation and by region. A single-country lens will understate the risk; the IMF’s findings show that AI’s labour market effects compound across borders, meaning regional strategies need country-specific calibration rather than a uniform approach.
Reskilling programmes should be prioritised for the occupational categories identified as high-risk, service, sales, and clerical roles, with particular attention to the gender composition of those categories. Generic upskilling initiatives that do not account for this concentration risk missing the workers most exposed.
Finally, the regulatory dimension should not be an afterthought. Institutions advising on AI adoption, whether governments, corporates, or NGOs, should build ethical-use and data-protection frameworks into AI strategy from the outset, rather than retrofitting governance once adoption is already underway.
Whether AI helps or harms a workforce depends on whether existing jobs can be complemented by the technology, not merely whether they are exposed to it.
Advanced economies are positioned to capture AI's productivity dividend, while emerging economies face disruption with fewer offsetting gains, a gap that could widen over time.
Women's overrepresentation in service, sales, and clerical roles, not any inherent vulnerability, explains their higher exposure to AI-driven disruption.
Social safety nets, reskilling programmes, and ethical-use regulation are most effective when established ahead of AI adoption, not as remedial measures afterward.
“Advanced economies have far more roles that AI can enhance rather than replace, while emerging economies have far fewer.” — IMF, Asia-Pacific Regional Economic Outlook, October 2024
How Artificial Intelligence Will Affect Asia’s Economies, IMF Blog, January 2025, based on the October 2024 Asia-Pacific Regional Economic Outlook, International Monetary Fund.
At Global Consultancy, we help organisations translate research like this into workforce and policy strategy grounded in evidence. Get in touch to explore what AI’s uneven reach means for your organisation or region.
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