I. Introduction: Employment Displacement in the Indian Context
1. The rapid rise of AI driven automation is reshaping labour markets across the world, with its impact particularly visible in India, now one of the fastest-growing AI hubs with a projected market size of $28.8 billion by 2025. This shift reflects a dual trend, on one hand, the displacement of employment, where machines take over roles previously performed by human labour, and on the other hand, the creation of new forms of work driven by technological innovation.
2. In the Indian context, “Displacement of Employment” is increasingly evident in the IT and BPO sectors, where 96 percent of professionals now report using generative AI tools, yet 94% express concern regarding potential job obsolescence. Conversely, “Productiveness of Employment” is reflected in India’s AI-skilled workforce, which grew fourteen fold between 2016 and 2023, positioning the nation as a global talent powerhouse. Whilst routine tasks that can be turned into clear, rule based steps are the easiest to automate, the more complex tasks that need human judgment have been harder to replace. However, by 2026, this is changing, as “agentic AI” is starting to handle even the advanced, non-routine tasks[1].
II. India’s AI Landscape: The “Youth Dividend” and the “Skills Paradox”
3. As of 2026, India faces a unique structural challenge characterized by the “skills paradox”, with 20 percent overcapacity in legacy roles alongside an acute shortage of AI – critical competencies[2]. With over 65 percent of its population under the age of 35, India’s “youth dividend” provides a tech-savvy workforce, yet this demographic is also the most exposed to the erosion of entry-level “stepping-stone” jobs that are now being automated[3].
● IT and Manufacturing Transition: India’s digital economy, which contributed 13.42% of national income in 2024 -2025, is projected to reach 20 percent by 2030, driven by massive automation in manufacturing and retail.
● Government Initiatives: To mitigate these risks, the IndiaAI Mission backed by a INR.10,300 crore allocation seeks to democratize AI access and foster a resilient workforce through national upskilling strategies.[4]
● Socio-Psychological Impact: Research highlights that displacement in India is not merely an economic event but a psychological shock, a strong consensus among expert reviewers in recent studies suggest that AI-induced job loss among Indian IT professionals leads to a profound disruption of personal identity and “shattered assumptions” regarding job security[5].
4. This underscores the necessity for a macro – economic roadmap that harmonizes technological advancement with India’s specific socio – economic needs, ensuring that AI becomes a tool for broadening opportunity rather than expanding inequality.
III. Task-Risk Framework: Routine vs. Non-Routine Displacement in the Indian Economy
5. To illustrate the mechanisms by which AI selectively displaces labour within the Indian economy, it is pertinent to adopt a bidimensional task – risk framework, contrasting routine versus non – routine on one axis and manual versus cognitive on the other. Routine manual tasks exemplified by assembly – line operations in India’s energy intensive industries such as steel and cement and routine cognitive tasks, such as high volume data entry or standardized document review in the Business Process Outsourcing (BPO) sector, are inherently codifiable into deterministic algorithms. This codifiability renders them most vulnerable to automation, a trend corroborated by the International Monetary Fund’s 2026 findings which indicate that IT skills now account for over half of all new competencies, simultaneously displacing middle-skilled roles that rely on predictable, transactional functions[6].
6. In line with the same, the seminal work of Autor, Levy & Murnane[7] documents a marked decline in routine task labour inputs since the 1970s, a trajectory that has reached an inflection point in India’s current digital landscape. Conversely, non-routine manual tasks such as bespoke craftsmanship or agricultural decision – support in rural India[8] and non – routine cognitive tasks such as strategic software architecture, creative design, or complex litigation, demand discretionary judgment, contextual interpretation, and adaptive problem-solving capabilities. It is pertinent to note that the current AI architectures, while sophisticated, continue to struggle to replicate these characteristics at scale within the nuances of the Indian socio – economic environment.
7. Further building on this framework, contemporary estimates suggest that while upwards of 60 percent of routine cognitive occupations in the services sector face high automation risk, fewer than 10 percent of non – routine cognitive roles fall into that same high risk category. In India, this has engendered an acute “skill polarity”. High-skill AI roles are expanding rapidly, offering wage premiums of 30 – 50 percent, whereas routine BPO and low-end service jobs face unprecedented automation pressure. This task risk taxonomy underpins our subsequent analysis of technological unemployment and informs the calibration of targeted counter – strategies necessitated by the shrinking of the traditional middle class[9].
IV. Critical Assessment of Technological Unemployment in the Indian Context
8. Historical precedents in which mechanization and early robotics eventually engendered net employment growth are long gone, the velocity and breadth of contemporary AI adoption in India threaten to outpace traditional labour market adjustment. Whilst routine occupations comprising roughly 28 percent of jobs across OECD nations remain foremost at risk, the Indian industrial landscape faces a more concentrated threat, particularly within its high-stakes tech – services sector where “agentic” systems are beginning to automate entire cognitive workflows. Although aggregate unemployment metrics in India have thus far remained relatively subdued, this often masks a critical temporal distinction between job displacement and re – employment, such a gap initially gives rise to frictional unemployment and, absent timely policy intervention, risks hardening into entrenched structural unemployment by the end of 2026[10].
9. Therefore, compounding these dynamics is the phenomenon of skill – mismatch. The ILO reports[11] that where displaced individuals’ existing competencies often enhanced through traditional repetition learning systems diverge sharply from the demands of emergent AI – centric roles, the duration of unemployment is essentially protracted. In India, this mismatch is acute, traditional degrees are becoming increasingly redundant as AI systems outperform human labour in routine analysis, leaving a generation of graduates “unemployable” in an AI first economy. Simultaneously, empirical analyses reveal a noticeable wage polarization i.e., compensation for high – skill, non – routine positions is soaring with AI – focused roles commanding a 28 percent wage premium even as remuneration for routine tasks remains stagnant or diminishes.
10. According to the IMF’s 2026[12] assessments, this deepening divergence primarily benefits a small elite of high skilled overseers while potentially contributing to the shrinking of the Indian middle class. This convergence of accelerated displacement, re-skilling shortfalls, and inequitable wage trajectories mandates a rigorous, policy oriented response to ensure that the “Viksit Bharat” vision remains inclusive.
V. Counter – Strategies for Sustainable Workforce Transformation
11. Considering the accelerated displacement documented above, a multipronged counter strategy is pertinent to preserve the socio economic fabric of the Indian state. Firstly, substantial investment in adult learning and continuous reskilling programmes, targeted specifically at non – routine cognitive and manual competencies could markedly enhance displaced workers’ re-employment prospects. The IMF[13] emphasizes that bridging these skill gaps is foundational to creating new jobs in the AI age, particularly in emerging economies where the transition from legacy roles to high-tech functions is most jarring. In the Indian context, this necessitates a shift from traditional degrees to a “skills-first” architecture, leveraging frameworks such as the India AI Mission to democratize access to critical technical training.
12. Secondly, the introduction of systemic fiscal mechanisms, such as an automation tax levied on corporations’ AI deployments, could generate dedicated revenue streams to fund both reskilling initiatives and a strengthened social safety net. Such mechanisms are vital to mitigate the wage polarization and the “shrinking middle class” phenomenon identified in contemporary economic analyses. Thirdly, social-floor policies most notably Universal Basic Income (UBI) warrant serious consideration within the Indian policy discourse. Empirical precedents, such as the improvement in participant well being and reduction of bureaucratic burdens in international trials only suggest that a guaranteed social floor can buffer the psychological shock of displacement and provide the financial stability required for long term vocational transition.
13. Finally, fostering robust Public Private Partnerships (“PPP”) that align industry needs, academic research, and governmental policy could ensure that labour market transitions occur fluidly and responsively. Collaborative efforts between bodies like Ministry of Electronics and Information Technology of India (“MeitY”) and industry associations are essential to embedding “just transition” criteria into national infrastructure and R&D policies. Jointly, these measures form the foundation of a macro-economic planning framework capable of transforming AI induced disruption into sustainable workforce evolution, ensuring the long term viability of the Indian economy.
VI. Policy Roadmap for Phased Implementation in India
14. Moving from strategy to implementation will require Indian policymakers to take a phased and collaborative approach, one that is responsive to the complexities of an increasingly digital economy.
● Phase I would ideally entail the establishment of a cross sectoral steering committee, comprising representatives from the MeitY, industry bodies such as NASSCOM, educational institutions, and civil society organizations. This body would oversee comprehensive diagnostics to identify sectoral vulnerabilities and set quantitative targets for displacement mitigation, aligning with the India AI Mission’s objectives to democratize technology access.
● Phase II would focus on expanding upskilling opportunities through accredited providers, building on existing vocational systems while introducing modular, AI-focused courses to address the “skills paradox” in India’s tech sector. At the same time, the government could pilot measures like a graded tax on AI adoption by firms, using the proceeds to create a national reskilling fund and support localized Universal Basic Income (UBI) trials in key digital hubs.
● Phase III would involve scaling successful pilot programmes across the country, using India’s Digital Public Infrastructure (commonly referred to as “India Stack”) to streamline the delivery of benefits. At the same time, PPP should be expanded to better align academic research with industry needs.
● Phase IV would then focus on ongoing monitoring and evaluation, based on clear indicators such as re-employment rates, wage trends, and participant well-being. Due regard should also be given to the psychological impact identified in recent studies on Indian IT professionals, so that policies can be refined over time and a more resilient labour market can be ensured.
VII. Conclusion & Going Forward
15. The rise of AI driven automation need not displace the Indian workforce or lead to sustained job loss. If managed carefully, it can instead support the creation of a more dynamic and resilient labour market. A useful starting point is to distinguish between the displacement of employment where machines take over routine, rule-based tasks and the creation of new forms of work driven by innovation. This distinction provides a balanced foundation for policymaking. Further, critical analysis shows that routine manual and cognitive roles, particularly in India’s services and manufacturing sectors, remain the most exposed to automation. At the same time, the broader debate on technological unemployment underscores the growing risks of skill mismatches and wage polarization, especially for the middle class. Addressing these concerns will require a coordinated policy response.
16. India must invest in continuous learning and reskilling, consider a fair levy on automation to support a social safety net, and strengthen PPP so that education and training remain aligned with industry needs. If approached in this manner, AI can be harnessed to expand opportunities and advance the vision of a “Viksit Bharat”, rather than deepen existing socio economic divides. In conclusion, the question is no longer whether AI will reshape India’s labour market, but whether policy can ensure that this transformation remains inclusive.
*Rahul Kanna R.N., Advocate, Madras High Court, JGLS (BA-LLB Hons)
[1] Calvão, F., & Thara, K. (2019). Working Futures: The ILO, Automation and Digital Work in India. The ILO @ 100, 223–247. https://doi.org/10.1163/9789004399013_012
[2] Mitra, S. (2025). The Potential Impact of AI on the Economic Growth of India. CeSPI. https://www.cespi.it/sites/default/files/osservatori/allegati/the_impact_of_ai_on_indian_economy_mitra.pdf
[3] International Monetary Fund. (2026). Bridging Skill Gaps for the Future: New Jobs Creation in the AI Age (SDN/2026/001). https://www.imf.org/-/media/files/publications/sdn/2026/english/sdnea2026001.pdf
[4] NITI Aayog. (2026). Scenarios Towards Viksit Bharat and Net Zero – Sectoral Insights: Industry (Vol. 4). https://niti.gov.in/sites/default/files/2026-02/Scenarios-Towards-Viksit-Bharat-and-Net-Zero-Sectoral-Insights-Industry.pdf
[5] Rawashdeh, A. (2025). Psychological impacts of AI-induced job displacement among Indian IT professionals: A Delphi-validated thematic analysis. Psychological Research and Intervention. https://doi.org/10.1080/17482631.2025.2556445
[6]Ibid.
[7]Autor, D.H., Levy, F. and Murnane, R.J., 2003. The Skill Content of Recent Technological Change: An Empirical Exploration. Quarterly Journal of Economics, 118(4), pp.1279–1333.
[8]PIB. (2026). Artificial Intelligence (AI) Transforming Rural India. Government of India. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2231706
[9]Prasad, U.T.R.. (2026). The Future of Employability in the Age of AI. https://www.researchgate.net/publication/400868872_The_Future_of_Employability_in_the_Age_of_AI/link/699477767247bc6473e141f3/download?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19
[10]Ibid.
[11]Ibid.
[12]Ibid.
[13]Ibid.