AI-Driven Automation and the Future of Work in India - Jobs, Skills and Policy for a Changing Economy

Sindhu Vissamsetti
Sindhu Vissamsetti
Intern - Policy & Advocacy, CyberPeace
PUBLISHED ON
Dec 10, 2025
10

Introduction

AI is transforming the way work is done and redefining the nature of jobs over the next decade. In the case of India, it is not just what duties will be taken over by machines, but how millions of employees will move to other sectors, which skills will become more sought-after, and how policy will have to change in response. This article relies on recent labour data of India's Periodic Labour Force Survey (PLFS, 2023-24) and discusses the vulnerabilities to disruption by location and social groups. It recommends viable actions that can be taken to ensure that risks are minimised and economic benefits maximised.

India’s Labour Market and Its Automation Readiness

According to India’s Periodic Labour Force Survey (PLFS), the labour market is changing and growing. Participation in the labour force improved to 60.1 per percent in 2023-24 versus 57.9 per cent the year before, and the ratio of the worker population also improved, signifying the increased employment uptake both in the rural and urban geographies (PLFS, 2023-24). There has also been an upsurge of female involvement. However,  a big portion of the job market has been low-wage and informal, with most of the jobs being routine and thus most vulnerable to automation. The statistics indicate a two-tiered reality of the Indian labour market: an increased number of working individuals and a structural weakness.

AI-Driven Automation’s Impact on Tasks and Emerging Opportunities 

AI-driven automation, for the most part,  affects the task components of jobs rather than wiping out whole jobs. The most automatable tasks are routine and manual, and more recent developments in AI have extended to non-routine cognitive tasks like document review, customer query handling, basic coding and first-level decision-making. There are two concurrent findings of global studies. To start with, part of the ongoing tasks will be automated or expedited. Second, there will be completely new tasks and work positions around data annotation, the operation of AI systems, prompt engineering, algorithmic supervision and AI adherence (World Bank, 2025; McKinsey, 2017).

In the case of India, this change will be skewed by sector. The manufacturing, back-office IT services, retail and parts of financial services will see the highest rate of disruption due to the concentration of routine processes with the ease of technology adoption. In comparison, healthcare, education, high-tech manufacturing and AI safety auditing are placed to create new skilled jobs. NITI Aayog estimates huge returns in GDP with the adoption of AI but emphasises that India has to invest simultaneously in job creation and reskilling to achieve the returns (NITI Aayog, 2025).

Groups with Highest Vulnerability in the Transition to Automation

The PLFS emphasises that a large portion of the Indian population does not have any formal employment and that the social protection is minimal and formal training is not available to them. The risk of displacement is likely to be the greatest for informal employees, making up almost 90% of India’s labour force, who carry out low-skilled, repetitive jobs in the manufacturing and retail industry (PLFS, 2023-24). Women and young people in low-level service jobs also face a greater challenge of transition pressure unless the reskilling and placement efforts can be tailored to them. Meanwhile, major cities and urban centres are likely to have openings for most of the new skilled opportunities at the expense of an increasing geographic and social divide

The Skills and Supply Challenge

While India’s education and research ecosystem is expanding, there remain significant gaps in preparing the workforce for AI-driven change. Given the vulnerabilities highlighted earlier, AI-focused reskilling must be a priority to equip workers with practical skills that meet industry needs. Short modular programs in areas such as cloud technologies, AI operations, data annotation, human-AI interaction, and cybersecurity can provide workers with employable skills. Particular attention should be given to routine-intensive sectors like manufacturing, retail, and back-office services, as well as to regions with high informal employment or lower access to formal training. Public-private partnerships and localised training initiatives can help ensure that reskilling translates into concrete job opportunities rather than purely theoretical knowledge (NITI Aayog, 2025)

The Way Forward

To facilitate the change process, the policy should focus on three interconnected goals: safeguarding the vulnerable, developing competencies on a large-scale level, and directing innovation towards the widespread ability to benefit.

  1. Protect the vulnerable through social buffers. Provide informal workers with social protection in the form of portable benefits, temporary income insurance based on reskilling, and earned training leave. While the new labour codes provide essential protections such as unemployment allowances and minimum wage standards, they could be strengthened by incorporating explicit provisions for reskilling. This would better support informal workers during job transitions and enhance workforce adaptability.
  2. Short modular courses on cloud computing, cybersecurity, data annotation, AI operations, and human-AI interaction should be planned through collaboration between public and private training providers. Special preference should be given to industry-certified certifications and apprenticeship-based placements. These apprenticeships should be made accessible in multiple languages to ensure inclusivity. Existing government initiatives, such as NASSCOM’s Future Skills Prime, need better outreach and marketing to reach the workforce effectively.
  3. Enhance local labour market mediators. Close the disparity between local demand and the supply of labour in the industry by enhancing placement services and government-subsidised internship programmes for displaced employees and encouraging firms to hire and train locally.
  4. Invest in AI literacy, AI ethics, and basic education. Democratise access to research and learning by introducing AI literacy in schools, increasing STEM seats in universities, and creating AI labs in the region (NITI Aayog, 2025).
  5. Encourage AI adoption that creates jobs rather than replaces them. Fiscal and regulatory incentives should prioritise AI tools that augment worker productivity in routine roles instead of eliminating positions. Public procurement can support firms that demonstrate responsible and inclusive deployment of AI, ensuring technology benefits both business and workforce.
  6. Supervise and oversee the transition. Use PLFS and real-time administrative data to monitor shrinking and expanding occupations. High-frequency labour market dashboards will allow making specific interventions in those regions in which the acceleration of displacement occurs.

Conclusion

The integration of AI will significantly impact the future of the Indian workforce, but policy will determine its effect on the labour market.  The PLFS indicates increased employment but a structural weakness of informal and routine employment. Evidence from the Indian market and international research points to the fact that the appropriate combination of social protection, skills building and responsible technology implementation can change disruption into a path of upward mobility. There is a very limited window of action. The extent to which India will realise the productivity and GDP benefits predicted by national research, alongside the investments made in labour market infrastructure, remains uncertain. It is crucial that these efforts lead to the capture of gains and facilitate a fair and inclusive transition for workers.

References

PUBLISHED ON
Dec 10, 2025
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