Artificial Intelligence (AI) is reshaping production systems, tasks, and skill requirements, but its effects on employment are neither uniform nor technologically predetermined. In India, where services-led growth coexists with weak employment elasticity and a large informal workforce, labour-market outcomes from AI depend critically on policy choices and institutional design.
This paper examines AI–labour interactions in India through a structural and a task-based lens. Using Indian databases such as KLEMS, PLFS, and NCO classifications, the analysis groups the economy into four structural categories and applies the productivity, inclusivity, and entrepreneurship framework to assess sector-wise AI exposure and adjustment capacity. The findings suggest that AI diffusion in India will be uneven. In employment-intensive, low-productivity sectors, displacement risks remain limited for now, but exclusion from AI-enabled productivity gains is a concern. In manufacturing, AI creates opportunities for upgrading through the servicification of production alongside selective automation. In knowledge-intensive services, AI may augment human capabilities, but this may require high-level skilling of labour, leading to shifting the nature of jobs. In public and social sectors, AI may lead to huge productivity gains. This paper argues for an AI strategy that prioritises diffusion and broad-based participation through coordinated investments in skills, infrastructure, and institutions.
This paper can be accessed here.
This paper is authored by Payal Malik and Nikita Jain, ICRIER.