Advancing social justice, promoting decent work
ILO is a specialized agency of the United Nations
English
Policy brief
AI and developing economies: Promoting decent work in a time of rapid technological change
This ILO policy brief examines the implications of artificial intelligence for employment, productivity and decent work in developing economies. It explores the opportunities and challenges of AI adoption, highlighting the policies and investments needed to bridge digital divides, protect workers and ensure that technological progress supports inclusive economic growth.
The labour market effects of artificial intelligence (AI) to date are modest and slow-moving, even in advanced economies. As even the most AI-ready economies are seeing gradual, rather than immediate, aggregate effects, developing economies have time to build the foundations for inclusive AI-enabled growth. But delay would allow existing gaps in infrastructure, skills, data, compute resources and governance to widen further.
Not all effects of AI are alike. Exposure, adoption and realized labour market outcomes are distinct phases, and current evidence points less to immediate, economy-wide job losses than to uneven task transformation, slow-moving productivity gains and sector-specific changes in work organization.
Developing economies face a dual challenge: lower near-term automation exposure, but weaker capacity to capture AI gains. Gaps in connectivity, compute, context/data and competency can produce “disruption without dividend”, where workers and firms face AI-related disruption before they can fully benefit from AI-enabled productivity growth.
The AI divide is a capability and governance divide, not simply an access gap. Investment, infrastructure and usage remain concentrated in higher-income economies and large firms, while many developing economies lack the compute, data, skills and bargaining power needed to adapt AI to local needs and shape global AI rules.
With deliberate policy design, AI can support job growth. Steering adoption toward labour-complementing uses – backed by investments in digital public infrastructure, adoption of AI in micro, small and medium-sized enterprises, and locally relevant applications in agriculture, health, education, logistics and public administration – can create jobs directly and indirectly where policies are designed explicitly for that outcome.
Job quality and distributional outcomes require as much attention as job quantity. Platform work, data annotation, content moderation and AI business process outsourcing work can expand income opportunities, but combined with increasing use of algorithmic management, they can also raise risks related to pay, surveillance, autonomy, social protection, mental health and career progression.
Policy choices, not the technology itself, will determine whether AI narrows or widens development gaps. An inclusive AI strategy should combine infrastructure investment, lifelong learning, worker safeguards, social protection, decent work standards in AI value chains, support for local firms and international cooperation on compute, data, competition and governance.
