The International Labour Organisation has released fresh research revealing the scale of artificial intelligence's potential impact across Southeast Asia's labour markets, with findings that paint a complex picture of widespread exposure tempered by the absence of immediate, large-scale employment disruption. According to the ILO's detailed examination of how generative AI will reshape work across ASEAN's eleven member states, approximately 80 million people—representing 22.9 per cent of total regional employment—occupy job roles with at least some measurable vulnerability to AI transformation. Yet the assessment also underscores a crucial distinction: while exposure is broad, the intensity of that risk remains concentrated, with only 11.7 million workers, or 3.3 per cent of the ASEAN workforce, classified as facing the highest levels of AI-driven occupational change.
This measured analysis emerges from the comprehensive ILO report titled "Generative AI and labour markets in ASEAN: Significant exposure, limited disruption, uneven preparedness," which examined both the technical exposure of different job categories and the current adoption patterns of generative AI technologies across the region. The findings suggest that while generative AI will undoubtedly reshape how tens of millions of Southeast Asians work, the transformation will likely be gradual rather than cataclysmic. Notably, roughly two-thirds of ASEAN's total employment—approximately 67 per cent—operates within occupational categories where AI poses virtually no direct threat, providing a substantial buffer against wholesale labour market upheaval.
The geographic distribution of AI exposure across ASEAN reveals significant variation, with Singapore emerging as the region's most AI-exposed labour market. The city-state reports that 42.2 per cent of its workforce operates in occupations vulnerable to generative AI, reflecting its advanced technology infrastructure and service-oriented economy. The Philippines follows at 28.1 per cent exposure, partly attributable to its substantial IT and services sectors that concentrate workers in roles where AI capabilities are most applicable. Indonesia, the region's demographic giant, registers 21.7 per cent exposure, while Vietnam trails slightly lower at 20.8 per cent, and Thailand rounds out the available data at 20.6 per cent. These variations illuminate how economic structure and sectoral composition determine vulnerability levels, with more digitally advanced and service-oriented economies naturally registering higher exposure indices.
A striking gender dimension emerges from the ILO analysis, revealing disparities that warrant urgent policy attention across the region. Women are more than twice as likely as men to work in occupations classified as highly exposed to generative AI, a disparity rooted primarily in occupational segregation. Women's concentration in clerical, administrative, and professional roles—sectors where AI applications are particularly mature—places them at disproportionate risk. This pattern raises important questions for labour policy makers across Malaysia, Singapore, Indonesia, and elsewhere, as the transition to AI-augmented workplaces could exacerbate existing gender-based wage gaps or working conditions if not carefully managed through targeted upskilling and workforce development programmes.
Younger workers, however, do not appear to face markedly different risk profiles than their older counterparts. The ILO found that workers aged 15 to 24 exhibit exposure levels broadly comparable to adult workers across the 25-64 age range, suggesting that age alone is not a primary determinant of AI vulnerability. This finding somewhat challenges conventional narratives that position young people as inherently advantaged in technological transitions, instead indicating that occupational choice and educational background matter more than generational placement.
Crucially, the ILO emphasises that despite high occupational exposure, adoption of generative AI technologies remains nascent and unevenly distributed across ASEAN. Current usage concentrates heavily in technology-intensive sectors and specialist roles, while white-collar and administrative positions—despite their theoretical exposure and potential compatibility with AI tools—have seen comparatively limited uptake so far. This lag between potential and actual deployment suggests that significant time may elapse before the full labour market implications of generative AI become apparent, providing regional policymakers with a window to develop appropriate frameworks and workforce strategies.
The preparedness to manage this transition, however, varies dramatically across the region. Singapore stands apart as a globally competitive artificial intelligence ecosystem, combining sophisticated digital infrastructure, a deep talent pool versed in technology, and a coordinated whole-of-government strategy for AI development and integration. This positioning affords Singapore advantages in capturing AI-driven productivity gains and creating quality employment in emerging sectors. By contrast, other ASEAN members face steeper challenges in building the institutional capacity, digital infrastructure, and skilled workforces necessary to manage AI transitions effectively, raising concerns about widening technological divides within the region.
The ILO report identifies several regional priorities essential for ensuring that artificial intelligence benefits workers rather than displacing them wholesale. Human-centred governance frameworks must be established to guide AI implementation in ways that protect worker interests and maintain employment quality. Inclusive skills development emerges as paramount, requiring substantial expansion of upskilling and reskilling programmes that explicitly target women and young people vulnerable to displacement. Micro, small, and medium enterprises—which dominate employment across much of ASEAN—require targeted support to overcome the technical and financial barriers that currently prevent them from adopting AI technologies, a gap that risks concentrating AI's productivity benefits among larger firms.
Knowledge exchange and coordinated human resource development across ASEAN's member states represents another critical pillar. The region's significant variation in AI readiness and exposure suggests that countries further advanced in technology adoption could productively share experiences and best practices with others, while the ASEAN framework could facilitate dialogue on cross-border labour mobility, standards for AI-driven automation, and unified approaches to worker protection in an era of rapid technological change.
For Malaysia specifically, these findings carry particular relevance. As a middle-income country with a diversified economy spanning manufacturing, services, and increasingly technology sectors, Malaysia faces exposure levels likely falling in the mid-range of ASEAN countries. The challenge ahead involves neither panicking at the scale of potential exposure nor complacently assuming that limited disruption to date guarantees limited disruption ahead. Instead, proactive investment in lifelong learning infrastructure, targeted support for workers in high-exposure occupations, and strategic development of Malaysian capacity in AI design and deployment could position the country to harness AI's productivity potential while protecting worker interests.
The ILO's conclusions suggest that the ASEAN region stands at an inflection point. Generative AI will undoubtedly transform work across the region, touching the lives of tens of millions of workers. Yet the outcome—whether this transformation reduces inequality and creates quality employment or concentrates gains among the technologically advantaged—remains substantially within the control of policymakers, business leaders, and labour movements. The narrow window between current limited disruption and the fullness of AI integration provides opportunity for deliberate, inclusive policy design rather than reactive scrambling. ASEAN's response to this moment will shape the region's labour market trajectory for decades to come.
