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What the paper says
The Centre for Finance and Economics Research (CFER) at Great Lakes Institute of Management published AI, Automation and India’s Workforce: Who Gains, Who’s Exposed, and How Policy Can Shape the Future as its fifth working paper in October 2026. Its authors are Vidya Mahambare, Vivek Jadhav and Vishnu Venugopalan. Great Lakes CFER’s paper listing summarizes its headline estimates using national worker-level data.
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- 46 million workers: In 2025, the publisher estimates, this many workers were in occupations where AI is likely to complement their work and raise productivity.
- 197 million workers: The publisher estimates this many were in high-routine jobs where automation could eventually take over a large share of tasks. It says full automation will take time.
- 39.4% of workers under 25: The publisher says this share worked in high-routine jobs in 2025.
These estimates distinguish two possible effects. AI can support or increase what a worker can do; automation can also take over tasks, especially where work is routine. A job may include a mix of tasks, so exposure to either possibility does not by itself show what will happen to a particular role.
Who may gain and who is exposed?
The publisher identifies young, urban and entry-level workers as facing both potential productivity gains and risks from task automation. The finding about workers under 25 points to high-routine work as one area of exposure; it does not mean that every worker in this age group is equally affected or will lose a job.
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CFER describes rural India as more insulated from substitution by AI and automation, but also less likely to benefit from productivity gains. The summary does not provide the detailed occupation classifications or exposure thresholds needed to make finer comparisons between groups.
Exposure is not a forecast of job losses
At the paper’s launch on Sunday, 11 October 2026, Chief Economic Adviser V. Anantha Nageswaran cautioned against treating exposure as a prediction of employment disappearing. As Financial Express reported, he said: “Exposure is not the same as job loss. In some jobs, of course, AI is likely to raise what a worker can do.”
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The distinction matters: the estimates concern occupations and the potential for tasks to be complemented or automated. They do not establish actual job losses, say when automation might occur, or show that all tasks in an exposed occupation will be automated. The publisher’s summary also does not specify the paper’s estimation method or whether its full report includes state-level estimates.
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What policies does the paper prioritize?
CFER names four priorities for managing employment effects: reskilling, AI literacy, social protection and urban housing. Together, these address workers’ ability to adapt to changing tasks as well as the broader conditions that can shape access to new opportunities. The publisher presents these as policy priorities; its summary does not endorse particular courses, providers or programs.
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