Artificial intelligence (AI) is rapidly reshaping labour markets across South Asia and Africa, two regions whose demographic and economic structures both amplify risk and create opportunity. Youthful populations entering the workforce at scale, large shares of informal and agricultural employment, and concentrated digital and outsourcing hubs create a complex patchwork of exposure: pockets of intense AI adoption sit beside vast swathes of low-digitisation employment. Drawing on quantitative syntheses from the World Bank, IMF, ILO, World Economic Forum, and industry studies, this paper examines how AI-driven displacement (the loss of existing roles through automation) and replacement (the creation of new roles either directly within AI ecosystems or indirectly through productivity-led growth) interact across sectors, demographic groups, and national contexts. We organise impacts through four channels: substitution (automation of routine tasks), productivity (cost reductions that may expand demand), task recomposition (reshaping jobs toward less automatable tasks), and new-job creation (emergent AI-adjacent occupations). Empirical patterns reveal strong sectoral concentration. Formal, export-oriented, and digitally intensive sub-sectors—IT–BPO, ready-made garments (RMG), customer support, and banking—carry the highest AI exposure. Bangladesh’s RMG and BPO-linked activities have shown notable job losses, disproportionately affecting women; India’s large IT–BPO workforce is flagged as highly susceptible to automation by 2030; and financial and BPO hubs in Nigeria, Kenya, Ghana, and South Africa present both displacement and emergent replacement dynamics. By contrast, agriculture and informal trade—which employ the majority of workers—remain comparatively insulated today due to low AI penetration, even as their structural vulnerability persists if adoption accelerates. Regional projections imply net job creation is possible but uneven and conditional. Global forecasts (WEF, IFC) point to large gross new-role estimates—AI engineers, data annotators, fintech and cybersecurity specialists, and expanded digital-services employment—while simultaneously underscoring vast reskilling needs. The net social outcome thus depends on whether labour-market institutions, education systems, and infrastructure can bridge the gap between displaced workers and newly created roles. Distributional heterogeneity is pronounced. Gendered patterns show women concentrated in export manufacturing and routine services are more exposed in many countries, though India’s agricultural employment profile produces an exception. Young and moderately educated entrants face elevated entry-level displacement risk, undermining traditional first-step pathways into formal employment. Educational and digital-skill divides amplify these effects: limited access to computer-science curricula, low mobile-internet penetration in rural areas, and inconsistent electricity constrain upward mobility into AI-complementary occupations. Consequently, social impacts centre on heightened income inequality, deeper rural–urban and regional disparities, and persistent skill gaps, with transition costs falling on displaced garment, BPO, and customer-service workers. Policy responses emerging from the statistical synthesis prioritise targeted reskilling and TVET reform, expanded digital education and connectivity, labour-market adjustment supports, and inclusive AI deployment strategies that favour augmentation over outright substitution. Robust monitoring systems—disaggregated by sector, gender, age, and geography—are essential to track real-time displacement and replacement. The principal policy challenge is not simply managing aggregate job counts but ensuring equitable transitions that close infrastructure and skills gaps and protect those most vulnerable to the labour-market disruption AI is already beginning to deliver.

Keywords: AI, labour markets, displacement, job replacement, South Asia, Sub-Saharan Africa, IT-BPO, ready-made garments, skills gap, digital infrastructure, gender disparities, youth employment.