AI, Labour, and the IT/ITes Sector:

Understanding impact and determining policy responses for the IT services industry in India

Abstract

The relationship between artificial intelligence and employment has become a central labour market question. This essay examines the labour market implications of AI adoption for India’s IT/ITeS sector, situating recent workforce reductions at major firms within a broader context of uncertainty surrounding AI’s employment effects. Drawing on academic literature, international institutional reports, and sector-specific data, the essay finds that productivity gains from AI are well documented, while evidence of its net employment effects remains inconclusive. It also highlights the need to examine gendered impacts, given women’s concentration in automation-exposed roles. The essay identifies four plausible labour market scenarios, ranging from task augmentation with minimal job loss to large-scale structural displacement. It concludes with policy recommendations across four domains: evidence gathering and transparency mechanisms, demographic-informed reskilling and career pathway planning, gendered impact mitigation, and inter-ministerial coordination.

Introduction 

On 1 April 2026, employees at Oracle India woke up to an email announcing the layoff of more than 12,000 employees in India, part of a global reduction of 30,000 jobs. It has been speculated that these cuts are linked to AI-related investments, while others argue that they signal the direction the job market may take in the future. Specifically, with the emergence of agentic AI in the workplace and the push towards its adoption across enterprises, there is a broad recognition that the nature of work will need to evolve, although how exactly this transition will unfold remains unclear. 

Concerns about technological progress replacing human labour are not new and date back at least to the Industrial Revolution, as discussed by economists including Ricardo (1821), Keynes (1930), and Leontief (1982). Historically, such displacement has largely been mitigated over time, with machines taking over certain kinds of tasks while new types of work and occupations emerged. This compensatory dynamic, described by Daron Acemoglu and Pascual Restrepo as the “reinstatement effect,” has historically helped maintain overall labour market stability. However, how this will play out in the AI era is uncertain. Some research indicates that job displacement has accelerated in recent decades without a proportional increase in new job creation. 

The research on the economic impact of AI is fairly new and is concentrated largely in the United States. For instance, the National Bureau of Economic Research (NBER), has published approximately 80 working papers on AI and its economic impacts between 2023 and April 2026 (including revised versions). For this study, ten papers focusing on job-related impacts were analysed, summarising findings across four parameters: 1) Productivity gains with AI, 2) Overall job losses due to AI, 3) Job displacement but no net job loss, and 4) No discernible impact of AI on jobs. As Figure 1 shows, most papers confirm productivity gains but are largely silent on net employment effects.  

Figure 1: Comparison of Outputs of Papers on AI and Job Impacts 

Legend:  ■  Yes / Supported by paper  ■  No / Not supported by paper  ■  Mixed / Partial evidence  ■  Not directly addressed

Notes: 'Displacement, no net loss' = some jobs displaced but new work offsets them. Entries reflect each paper's primary empirical or theoretical findings. Cells marked 'Not addressed' indicate the paper did not primarily study that dimension.

Global institutions have also weighed in. A World Economic Forum report estimates that by 2030, 22 per cent of global jobs will be disrupted, with 92 million displaced and 170 million created (net +78 million), while 39 per cent of core skills will change, reflecting large-scale structural transformation rather than absolute job loss. An October 2025 World Bank report also highlights that, unlike previous waves of automation, AI can potentially displace a range of non-routine, white-collar service sector jobs. The IMF’s 2024 report likewise suggests that AI can fundamentally restructure global labour markets, with advanced economies likely to experience the most immediate effects because of their high concentration of cognitive-intensive jobs. It also notes that women and entry workers face greater exposure to these technologies, although they may also be better positioned to leverage them. Conversely, older workers may encounter significant challenges in adapting to these shifts. The report suggests two strands of economic consequences. On the one hand, wealth and income gaps could widen if AI primarily benefits capital owners and high-skilled labour. On the other hand, substantial productivity breakthroughs could lead to a broad increase in income levels across the workforce. Further, clerical roles – highly exposed to generative AI – represent a significant source of female unemployment, particularly in high- and upper-middle-income countries. 

This, however, is only one side of the story. The other is that AI is still being used far below its capabilities in the workplace. Research from Anthropic, shown in Figure 2 suggests that AI is only doing a fraction of what it can actually do globally. For instance, AI’s theoretical capability for legal work is significantly higher (blue line in the chart) than its observed use (red line). 

Figure 2: Anthropic’s Representation of AI’s Theoretical Capability vs Actual Usage

Source: https://www.anthropic.com/research/labor-market-impacts 

This disconnect persists even when tech companies are cutting workforces while increasing AI investments. Globally, tech companies have been announcing layoffs alongside expanded investments in AI. Microsoft laid off 15,000 workers in 2025, while Amazon cut nearly 30,000 positions. Financial-services company Block reduced its workforce by 40 per cent in February 2026, and Meta laid off more than 1,000 employees in the last six months. According to a Reuters report, Meta may reduce its workforce by a further 20 per cent in the near future. 

India’s IT services sector is widely regarded as being particularly exposed to these shifts. Dario Amodei has argued that India may stand to see some effects in the IT/ITes sector given the advancements in AI coding. He also noted that many IT companies also function as consulting companies, suggesting that these relationships may become increasingly important as AI advances.

Over the past three decades, India’s IT services sector has emerged as a major driver of economic growth and has become one of the country’s major success stories. In fiscal year 2023, the IT industry contributed roughly 7.5 per cent of India’s GDP, with over 5 million people directly employed in the sector. Since 2024, however, there has been growing evidence of layoffs in the IT/ITes sector. While not all of these reductions may be directly attributed to AI, there is considerable overlap between workforce restructuring and increased AI investment. Tata Consultancy Services, India’s largest private sector employer, with more than 613,000 employees globally and a workforce drawn overwhelmingly from India, announced in July 2025 that it would eliminate more than 12,000 positions – representing 2 per cent of its global workforce – concentrated in middle and senior management layers. Although the decision was attributed to “skill mismatch” rather than AI adoption, its revenue grew by only 1.3 per cent in Q1 FY2026, deal wins fell to USD 9.4 billion from USD 12 billion, and the company simultaneously announced a USD 6.5 billion investment in AI infrastructure, suggesting a strategic reallocation of investment from labour to technology. Infosys and HCL also slowed down hiring at entry levels, while Wipro also appeared to be cutting certain roles strategically. A 2025 NITI Aayog report on AI’s impact on India tech services notes: in “a business-as-usual case, there will be a downside. In this worst-case scenario, the headcount in the tech services sector could go down from 7.5 to 8 million in 2023 to 6 million by 2031. Similarly, the headcount in the CX sector could go down from 2 to 2.5 million in 2023 to 1.8 million.” At the same time, the report suggests that demand may increase for occupations such as data centre controllers, AI marketing specialists, ethical AI specialists, and sentiment intelligence specialists. 

A recent report argues that India’s IT sector is undergoing a phase of “silent layoffs,” in which workforce reductions occur without formal announcements, often through internal processes like non-renewal of contracts or the elimination of roles. The report estimates that India could lose up to 50,000 jobs in this sector. The evidence, however, remains contradictory. A recent survey-based study on the employment implications of generative AI in India’s IT sector finds “no evidence to support apocalyptic predictions of large-scale job losses following AI adoption.” It points instead to a moderation in hiring, primarily at the entry level. Contrary to earlier predictions, the report suggests that technical and analytically intensive roles are experiencing the strongest growth in demand. As the authors conclude, “these findings indicate that rising global demand for AI-enabled goods and services should support significant net job creation in India’s IT sector.” 

Further, new data from the International Labour Organization reveals a significant gender imbalance in the impact of Generative AI, noting that women are likely to be disproportionately affected for three primary reasons: they make up a large part of the workforce that may be impacted by automation; they are underrepresented in AI-related and science, technology, engineering and mathematics (STEM) occupations; and AI systems frequently reproduce gender biases and harms already embedded in society. Consequently, AI-mediated hiring and performance evaluation systems may compound the structural disadvantages women already face in IT career progression. During the major layoffs of 2022–23 in the US, women accounted for around 45 per cent of those laid off despite comprising only about 26–28 per cent of the tech workforce. Further disaggregation at the country level suggests women are more exposed to GenAI than men in 88 per cent of countries analysed. These patterns could have significant implications for women's employment in India's IT sector. 

The current moment is marked by significant uncertainty. AI investments – and accompanying layoffs – appear to be outpacing the available evidence on the productivity gains they are meant to deliver. At the same time, the organisational contexts within which AI is deployed deserve closer attention. Digital transformation in incumbent organisations has historically taken far longer than anticipated, and whether AI adoption will follow a similar trajectory remains an open question. What is clear, however, is that a policy framework to support workforce transition is long overdue. This paper therefore focuses on outlining meaningful pathways towards an inclusive workforce transition in India.

The paper highlights policy responses for India organised around four plausible labour market scenarios ranging from task augmentation with minimal job loss to large-scale displacement. It develops targeted policy recommendations across four areas: evidence gathering and transparency, reskilling and career pathway planning, gendered impact mitigation, and inter-ministerial coordination. 

Policy Responses for India 

The potential vulnerability of India’s IT/ITeS sector to the current wave of AI must be understood in the context of its historical position within the global value chain (GVC) of offshore services. As global value chain frameworks suggest, the offshore service ecosystem can largely be classified into Information Technology Outsourcing (ITO), Business Process Outsourcing (BPO), and high-value Knowledge Process Outsourcing (KPO). Over the past three decades, India successfully plugged into these GVCs by capturing highly codifiable, labour-intensive, and low- to mid-value tasks, such as routine software application maintenance, coding, and voice-based customer management among others, leveraging its talent and cost advantage. However, Indian political economy researchers argue that this dynamic creates an unequal structural division of labour, trapping large segments of the Indian workforce at the lower end of global technical production. Because agentic and generative AI are increasingly getting better at automating exactly these highly codifiable, process-oriented tasks, the sector faces emerging threats from the technology. 

AI presents both opportunities and challenges for workers and employers, and its effects on jobs and workplaces will depend on how it is deployed and governed. Its overall impact will also be shaped by regulatory frameworks and the extent to which different stakeholders are involved in the process. Importantly, despite the confidence with which the impact AI on jobs is discussed, considerable uncertainty remains. Policy attempts aim to ascertain different scenarios that may arise in the next five to seven years and develop policy pathways as a step towards proofing the future for minimal overall job losses, to the extent possible. The objective of this paper is not to forecast outcomes but to use analytical frameworks to understand emerging trends and develop policy scenarios that are likely to play a much larger role in the workplace. To this end, this paper sets out four scenarios, the evidence supporting each, and the targeted policy interventions the scenarios demand. A cross-cutting section examines gender, recognising that women constitute 36 per cent of the IT/ITeS workforce and are disproportionately concentrated in roles most exposed to automation. The paper identifies four plausible scenarios.

Figure 3: Different Labour Impact Scenarios of AI in the IT/ITes Sector in India 

All four scenarios outlined above require developing policy responses that support and build resilience across different parameters. The World Economic Forum’s Job Transitions Framework provides a useful structure to think about job transitions, with an emphasis on ensuring that job transitions are both viable (technically possible through retraining) and desirable (maintaining wages and stability). It focuses on standardising skill credentials for easier movement between roles, leveraging technology for better job matching, and providing portable social safety nets that follow the worker. Finally, it emphasises that large-scale shifts, such as what one may expect from generative AI, require coordinated collaboration between governments, employers, and educators. This provides a useful basis for developing policy responses for India’s IT/ITes sector. The following sub-sections aim to provide recommendations for what reasonable policy pathways may look like. 

Invest in Evidence Gathering, Research and Transparency Mechanisms 

Much of the existing evidence on AI and job losses is drawn from the U.S. labour market, while relatively little empirical work exists for India, specifically in the IT/ITes sector, even though the estimated impacts on this sector are high. Furthermore, as previously discussed, isolating the short-term effects of AI remains methodologically difficult. Addressing this gap will require investment in novel research methodologies, which leverage both academic and industry research collaborations, to distinguish observable labour market trends from longer-term projections. This, in turn, would require sustained public and private investment and the allocation of longer-term research funding to this end. 

Research must also be accompanied by greater transparency regarding how organisations make workforce decisions. This includes monitoring emerging labour market trends and, where feasible, developing early-warning systems to track AI-related employment changes on an ongoing basis.

There is significant information asymmetry between companies, which are already making labour-related decisions, and employees and the State, who are more passive actors. Even when lay-off decisions are driven by AI, they are often couched in opaque terms like strategic repositioning. It will be important for private corporations to disclose clearly the job losses they anticipate due to AI, including their future predictions, to the extent possible, without impeding on issues of trade secrets and so on. 

Furthermore, it will also be important to disclose where AI systems influence hiring, performance evaluation, or termination decisions. Workers, in these instances, should have the right to know that algorithmic tools were used and the right to request human review. The OECD argues that because algorithmic management, and the use of AI and software to automate managerial functions like scheduling, performance tracking, and task allocation, are often “opaque,” policymakers cannot effectively protect workers’ rights without better transparency. It will be critical to evaluate if there may be specific disclosures that companies beyond a particular size are expected to make to the Labour and Employment Ministry. Further, public institutions should then share public datasets on AI and labour impacts to allow for improved research, which in turn can inform policy. 

Demographic-Wise Planning for Reskilling and Future Career Pathways 

NITI Aayog’s Roadmap to Job Creation in the AI Economy report from October 2025 identifies three main AI-related risks: high levels of job displacement, gaps in India’s computer science education, and a shortage of AI talent in the country. The report recommends setting up an India AI Talent Mission with three key objectives: 1) to embed AI fluency within schools and universities, 2) to position India as a premier destination globally for AI skills, and 3) to launch an AI skilling engine to reskill and upskill the current workforce at scale. Achieving these objectives will require significant shifts to both the education systems, and the way skilling and reskilling are currently undertaken in the workplace. 

Higher education reforms must go beyond the technical “how-to” of AI toward a deeply interdisciplinary model. Future curricula should integrate the humanities and social sciences – including ethics, sociology, and law – into technical degrees to ensure graduates can identify and mitigate the harms emerging from AI use. This includes addressing algorithmic bias, privacy violations, and the socio-economic impacts of automated decision-making. 

The education system must also be clear about the outcomes it seeks to achieve. Preparing a skilled AI workforce that is able to meaningfully work in the IT services industry may require different skills from those needed within an academic research environment to foster breakthroughs in AI research and development, which in turn may necessitate “a deep-rooted fabric of scientific, social, mathematical, philosophical, and engineering expertise spanning academia, industry, and civil society.” While both objectives are important to minimise the labour impacts of AI, the longer-term goal should be a holistic education and research strategy supported by sustained funding to foster “high quality research capacity across disciplines, aiming explicitly for a fundamental shift in conditions that naturally disincentivise skilled, analytical, critical-thinking, passionate brains from draining out of the country.”

Various reports have noted the exacerbated impacts of AI on entry-level jobs, with some indicating that entry-level IT roles have declined by 20–25 per cent due to AI’s push towards specialised hiring. India currently relies on entry-level IT employment to provide on-the-job learning through which junior workers develop into experienced practitioners. If AI performs the codifiable tasks that constitute that apprenticeship phase, alternative paths will have to be charted for entry-level workers. Policymakers could therefore require large IT firms above a defined employment threshold to submit workforce development plans that articulate how they intend to develop entry-level workers in an AI-augmented environment. 

The OECD Skills Strategy provides a useful framework for helping countries build resilient, future-ready skills systems in the face of major shifts while promoting meaningful economic and labour force participation. Reviewing these frameworks (including WEF’s job transition framework) could help in crafting and customising India’s policy responses to support a resilient workforce, especially in the IT services industry. 

Another pathway worth considering is expanding the range of organisations involved in AI skills development beyond traditional educational and skilling systems. Online platforms and AI companies are increasingly offering programs for AI skills globally. Policymakers should therefore consider whether similar programmes could be developed in India or adapted for the Indian context. There will also be a need for innovative financing systems that provide stable funding for AI-related skilling in the short to medium term. 

Workforce development policies should also account for Micro, Small, and Medium Enterprises (MSMEs) in the IT/ITeS value chain, including smaller BPO and KPO firms, staffing agencies, and ancillary service providers, which face automation-driven cost pressure with far less financial cushioning than large, diversified firms. A 2026 PwC India report on AI adoption among manufacturing MSMEs notes that structural informality, small operating scales, and severe capability gaps create a persistent “low-productivity trap” across the wider industrial ecosystem. The report argues that because small enterprises operate on razor-thin margins and prioritise survival over technological experimentation, high upfront costs, paired with constrained access to capital and skilled talent, prevent many MSMEs from participating effectively in the AI transition. 

Addressing and Mitigating Gendered Impacts

The IMF’s 2024 AI analysis identifies consistent patterns of higher female exposure to both displacement and complementarity, while noting that transitions into complementary roles require access to skills and institutional support that is not automatically available. It is clear that neither the technology nor the labour market into which it is being introduced is neutral. 

Across global labour markets, evidence indicates that women earn lower wages, remain underrepresented in senior roles, and have lower participation in STEM disciplines. A 2019 UNESCO report estimates that women account for roughly 29 per cent of research and development positions in science and are about 25 per cent less likely than men to possess basic digital technology skills. It is thus imperative that policy responses account for these differential impacts and do not further exacerbate gender disparity in the workplace. The World Economic Forum’s (WEF) 2025 Global Gender Gap Report estimated that, across 148 economies, 68.8 per cent of the gender gap had been closed and that at the projected rate, it would take approximately 123 years to achieve full gender parity.

Given these realities, it is critical that policy responses address AI-related gender inequalities across the employment lifecycle, right from hiring and job matching to working conditions, career progression and access to reskilling. 

In India, women constitute 36 per cent of the IT sector workforce. Over time, corporate hiring and retention initiatives, skilling programmes, and diversity and inclusion policies have contributed to increasing women's participation in the sector. However, women remain underrepresented in leadership positions, and AI risks exacerbating these existing inequalities. Policy should therefore encourage targeted recruitment of gender diverse candidates while expanding AI-related training opportunities for groups likely to be disproportionately affected by technological change. It will be important to continue to collect and disclose gender-disaggregated numbers around hiring, layoffs, performance appraisals, and attrition to facilitate continuous monitoring of and mitigating for acute gendered impacts in the workplace. Further, companies above a certain size should be required to transparently report data on these parameters over time. They should also be required to continue to focus on diversity initiatives in the workplace to create favourable opportunities for diverse groups. Crucially, women continue to be underrepresented in the design, development and governance of AI systems. For the AI systems being developed in India, an active push should be made to hire more women in these roles. The India AI mission already includes initiatives to increase women’s participation in the development of AI systems, which can be further expanded upon.

Strengthen Inter-Ministerial Coordination 

The OECD, in its report Governing with AI, recommends establishing inter-ministerial task forces or committees to facilitate decision-making, communication, and collaboration across different institutions on AI-related matters. In April 2025, the Government of India established the AI Governance and Economic Group (AIGEG) under the Ministry of Electronics and Information Technology (MeitY) as a high-level inter-ministerial body to guide and coordinate the nation’s approach to AI governance. One of the key objectives of this body is to “assess the labour market impacts of AI adoption in advance and develop mitigation strategies and transition plans that account for informality, skill diversity and regional variations.” This is a timely step that should be strengthened over time. 

The body currently includes representatives from MeitY, NITI Aayog, the Department of Telecommunications, the Department of Economic Affairs, the Department of Science & Technology and the National Security Council. In addition, it will be important to have representation from the Ministry of Labour and Employment, as well as the Ministry of Education, to ensure improved coordination, especially on the questions of jobs and skills. The announcement also mentions that the body will bring together “expertise from technology, economics, public policy and national security domains, enabling a coordinated and strategic approach to AI governance in India.” It will also be important to create greater opportunities for public participation on AI governance matters. Although AI, especially generative AI, is increasingly affecting people across different walks of life, public participation in AI-related decision-making continues to remain limited. The body should therefore establish regular public consultations and incorporate worker voices into AI governance discussions. IT/ITes employees must have opportunities to meaningfully engage in such discussions through this body. 

Workforce Singapore has worked with government agencies in Singapore to examine how key trends like artificial intelligence, automation, digitalisation and sustainability impact sector-specific jobs and skills, while recommending pathways for employers to reskill their workforce in growth job roles and redesign jobs to align with rapidly evolving industry developments. Through this proposed inter-ministerial body, India could adopt a similar approach to support more informed policymaking around AI and jobs. The body may also consider short social protection measures, where necessary, for workers in the IT/ITes sector displaced by new AI technologies.

This body could also establish an AI and Jobs Observatory to routinely monitor employment-related shifts associated with AI use in the workplace. This may help bridge the gap between the rapid pace of technological advancement and the comparatively slower evolution of policy. It will also be important for the body to publish regular updates on its findings and strategic decisions to keep the public and other stakeholders informed. 

Finally, a growing body of work points to the psychological impact of AI-led job losses and the fear of displacement. One such study among Indian IT professionals identifies various psychological consequences, including emotional shock, erosion of professional identity, social withdrawal, and perceived organisational betrayal. These findings highlight the need for organisations, mental health practitioners, and policymakers to develop anticipatory and compassionate interventions that can mitigate the mental health consequences of technological transformation.

Concluding Remarks

The question of how AI will reshape India's IT/ITes workforce does not yet have a definitive answer. What is clear, however, is that the window for proactive policymaking is open now, before patterns of displacement harden into structural unemployment. The four scenarios outlined in this paper are intended to serve as useful planning instruments in thinking through policy responses. Each scenario may demand a different calibration of policy response, but all share common foundations: investment in evidence and transparency, deliberate reskilling infrastructure, gender-responsive planning, and coordinated governance. India has navigated large-scale labour market transitions before, and the IT/ITes sector itself is a product of deliberate policy choices made over several decades. The current moment calls for a similar sense of purpose. AI investment and workforce reduction are already underway; what lags behind is the institutional architecture needed to manage this transition equitably. Building that architecture – with workers rather than around them – will be one of the most consequential policy challenges for the coming years.