A comprehensive study by the International Labour Organisation has found that generative artificial intelligence stands to reshape the working landscape for nearly 80 million people across the ASEAN region, yet the organisation cautions that large-scale employment disruptions have not materialised despite these significant exposure levels. The research, conducted across all 11 ASEAN member states, presents a nuanced picture of technological transformation—one marked by substantial workforce vulnerability but tempered by the reality that economies have adapted without dramatic job shedding thus far.
According to ILO projections for 2025, roughly 22.9 per cent of total employment in ASEAN falls within occupations showing more than minimal exposure to generative AI applications. This translates to the nearly 80 million workers identified in the study. The scale of this potential exposure underscores how pervasively AI technologies are likely to influence employment patterns across the region, from Singapore's developed services sector to Indonesia's sprawling informal economy. Yet the data reveals important nuances that temper apocalyptic narratives about technological unemployment. Only 3.3 per cent of the regional workforce—approximately 11.7 million people—occupy positions classified as facing the highest exposure category, suggesting that while AI's reach is broad, the intensity of disruption remains concentrated in specific occupational clusters.
The geographic distribution of AI exposure across ASEAN reveals stark disparities that mirror each country's economic development trajectory. Singapore emerges with the highest share of workers in more-than-minimal-exposure occupations at 42.2 per cent of total employment, reflecting its position as a technology-driven financial hub. The Philippines follows at 28.1 per cent, a figure partly explained by its substantial business process outsourcing sector and growing information technology industry. Indonesia, the region's most populous economy, shows 21.7 per cent exposure, while Vietnam and Thailand register 20.8 per cent and 20.6 per cent respectively. Malaysia, though not explicitly mentioned in the study data, occupies a middle position within this regional spectrum given its manufacturing and services orientation.
A striking feature of the research involves the concentration of employment in occupations with no identified GenAI exposure. Approximately two-thirds of ASEAN's workforce remains engaged in roles where artificial intelligence poses no immediate threat—predominantly in agriculture, construction, domestic services, and manufacturing sectors that remain labour-intensive and difficult to automate. This geographical and sectoral distribution has profound implications for Malaysia, where rural and traditional manufacturing segments still employ substantial numbers of workers. The persistence of these lower-exposure sectors suggests that economic disruption, if it occurs, will follow an uneven pattern across geography, income levels, and educational attainment rather than affecting the workforce uniformly.
The gender dimension of AI exposure presents a counterintuitive but concerning finding. Women are more than twice as likely as men to work in occupations classified as facing high GenAI exposure, a pattern driven by their overrepresentation in clerical, administrative, and professional roles that are amenable to automation. This concentration carries significant implications for Malaysia's workforce, where women's labour force participation has grown substantially over recent decades. If generative AI adoption accelerates in these sectors, female workers could face disproportionate pressure to reskill or transition to different occupations, potentially reversing hard-won gains in women's economic participation and advancement. Young workers aged 15 to 24, by contrast, exhibit exposure levels broadly comparable to the overall workforce, suggesting that age alone does not predict vulnerability to AI disruption.
Crucially, the ILO study distinguishes between potential exposure and actual disruption, a distinction that has proven accurate thus far. GenAI adoption across ASEAN remains geographically and sectorally uneven, concentrated primarily in technology-intensive occupations while seeing comparatively limited uptake in office and administrative roles despite those sectors' theoretical vulnerability. This lag between exposure potential and actual implementation adoption suggests that workforce displacement, should it occur, may unfold more gradually than technological determinists predict. The report notes that employment in highly exposed occupations has actually continued to expand across the region, indicating that AI complementarity—where workers use AI to enhance productivity rather than being replaced—may be occurring alongside technological adoption.
The preparedness gap identified in the study carries particular weight for Malaysia and other mid-tier ASEAN economies. Singapore stands out as possessing a globally competitive artificial intelligence ecosystem, combining advanced digital infrastructure, readily available AI talent, and a coordinated government implementation strategy. This positions the city-state to capture disproportionate benefits from AI-driven productivity gains while potentially attracting regional talent and investment. In contrast, other ASEAN economies face significant structural barriers to AI adoption and workforce adaptation, including digital infrastructure gaps, skills shortages, and fragmented policy approaches across government agencies. Malaysia's position as a more developed economy than most ASEAN peers but less advanced than Singapore creates specific strategic challenges and opportunities in this transitional period.
To navigate this transformation constructively, the ILO has outlined a comprehensive set of regional priorities that extend beyond simple upskilling narratives. Human-centred governance frameworks must guide AI deployment, ensuring that decisions about automation prioritise worker welfare alongside economic efficiency. Inclusive skills development programs must expand dramatically, targeting not only young workers entering the labour force but also mid-career professionals and older workers facing technological displacement. A particular emphasis on women's reskilling and advancement reflects acknowledgment of the gendered nature of AI exposure. Critically, the report emphasises that micro, small, and medium enterprises—which employ significant portions of ASEAN's workforce including in Malaysia—require targeted support to overcome adoption barriers, preventing AI benefits from concentrating solely among large multinational corporations.
Knowledge exchange and coordinated human resource development across ASEAN member states emerges as another priority, recognising that labour mobility within the region can either amplify or mitigate AI-driven disruption. If some countries develop more advanced AI ecosystems than others, labour flows could concentrate in those jurisdictions, draining talent from less-prepared economies. Alternatively, coordinated regional approaches to skills certification, recognition of qualifications, and strategic labour mobility could ensure that AI-driven transformation benefits the entire region rather than widening disparities. For Malaysia specifically, this coordination carries significance given the country's role as both source and destination for skilled workers within ASEAN.
The ILO's finding that widespread labour market disruption has not yet materialised, despite AI's growing technological capabilities, suggests that predictions of imminent mass unemployment remain premature. However, this conclusion carries an implicit warning: the absence of disruption to date reflects a lag between technological possibility and actual economic implementation. As GenAI systems mature and adoption accelerates beyond early-stage technology adopters, the risk of significant labour market transformation intensifies. The window for proactive workforce preparation and policy intervention narrows with each passing quarter, making the regional priorities outlined in the study not merely aspirational recommendations but essential prerequisites for managing AI's transition from technological curiosity to economic reality.
For Malaysian policymakers, workers, and businesses, this study offers both reassurance and urgency. Reassurance comes from evidence that AI adoption remains measured and that labour market catastrophes have not occurred despite years of technological advancement. Urgency emerges from recognition that preparedness gaps are widening, that certain groups—particularly women in administrative roles—face disproportionate exposure, and that ASEAN's competitive position relative to other global regions depends on coordinated action rather than fragmented national responses. The next phase of AI development will likely determine whether ASEAN workers share broadly in productivity gains or experience deepening inequality and exclusion from the benefits of technological progress.
