Artificial intelligence (AI) could reshape India’s employment landscape in ways that extend beyond job displacement, with entry-level workers, smaller businesses and employees facing heightened workplace monitoring among those potentially bearing the greatest costs, according to a new white paper by researchers at the Indian Institute of Technology Madras (IIT Madras).
Titled How AI Is Reconfiguring Work in India: A Task-Based Framework for Understanding Augmentation, Displacement, New Roles and the Changing Structure of Jobs, the study was prepared by the Centre for Responsible AI (CeRAI) at IIT Madras’s Wadhwani School of Data Science and Artificial Intelligence. Released during the Industry Conclave on AI Governance 2026, the paper calls for a more nuanced assessment of how AI changes work, arguing that its impact depends on the tasks being automated, the cost of deploying the technology and the way organisations redesign jobs.
Entry-level jobs face a potential squeeze
One of the study’s central concerns is the future of entry-level employment. Routine tasks such as password resets, basic contract reviews and boilerplate coding have traditionally given new employees opportunities to develop practical skills, understand business processes and build the experience required for more senior positions.
As AI systems take over some of these responsibilities, companies could find themselves with fewer reasons to recruit for conventional junior roles. The resulting challenge extends beyond immediate job losses: if entry-level employees no longer gain experience through routine work, organisations may need to rethink how they develop future managers and specialists.
The implications could be significant for India’s information technology services, business process outsourcing (BPO), financial services and professional services sectors, which have historically relied on large entry-level talent pipelines.
The researchers argue that AI’s impact should be assessed at the level of individual tasks, jobs, occupations and the broader labour market. Automating selected tasks does not necessarily eliminate an entire profession. Instead, it can change the responsibilities attached to a role, shift decision-making between humans and machines, or create demand for new capabilities.
AI adoption could widen the gap between large firms and MSMEs
The paper also examines what it describes as the geography of AI costs: how the expense of adopting and operating AI systems influences which businesses can use them and for what purposes.
More affordable tools could expand the use of AI in activities such as bookkeeping, quality checks and BPO operations. However, applications requiring advanced systems, specialised expertise and substantial infrastructure may remain concentrated among larger organisations and businesses operating in major urban centres.
Large enterprises are generally better positioned to absorb the costs of integrating, customising and governing advanced AI systems. Micro, small and medium enterprises (MSMEs), meanwhile, may depend on narrower or subsidised tools, potentially limiting the range of applications they can adopt.
This disparity raises questions about how widely AI-driven productivity gains will be distributed across the economy. While technology could help smaller businesses improve efficiency, unequal access to infrastructure, skills and affordable tools could also reinforce existing differences in competitiveness.
Workplace surveillance and stress emerge as concerns
Beyond employment numbers, the study highlights gaps in the available evidence on job quality, including wages, workplace monitoring, work intensity, stress, burnout and opportunities for career progression.
As organisations automate routine activities, employees may increasingly be responsible for handling complex exceptions, difficult customer interactions and decisions requiring human judgement. Although this could increase the value of human expertise, it may also leave workers dealing with more demanding tasks under tighter performance monitoring.
The paper raises concerns about workplace surveillance and the use of productivity metrics to track employees. It also identifies accountability for errors and the quality of human interaction as areas requiring further examination.
The researchers caution that evidence on these dimensions remains limited in the Indian context. As a result, the consequences of AI adoption cannot be fully understood by measuring productivity gains or counting the jobs created and displaced alone.
Informal workers remain an important blind spot
The study also points to gaps in understanding AI’s indirect effects on India’s informal workforce. While much of the discussion around workplace automation focuses on formal employment, changes in sectors such as IT and BPO could have wider economic consequences.
For example, reduced hiring or changes in wages in formal-sector employment centres could affect local businesses and service providers that depend on workers’ spending. AI-driven systems could also influence access to credit and other services, potentially benefiting some individuals while disadvantaging those with limited digital transaction histories.
The paper notes that the available evidence does not adequately capture these wider effects, underscoring the need for India-specific research into how AI is changing employment conditions across different sections of the workforce.
Language is another consideration. Uneven AI performance across Indian languages and code-switched conversations could affect the usefulness of these tools for workers and businesses operating in multilingual environments.
Researchers call for task-focused AI and clearer transition pathways
The white paper recommends expanding access to affordable, task-focused AI tools, particularly for smaller businesses, while investing in research that identifies how specific tasks and skill requirements are changing.
It also calls for clearer pathways that help workers acquire new skills and transition into emerging roles. Rather than treating AI adoption as a straightforward choice between automation and job preservation, the researchers emphasise the importance of redesigning work and preparing employees for changing responsibilities.
For enterprise leaders, the findings broaden the discussion around responsible AI. Alongside considerations such as data security, governance and model reliability, organisations must examine how automation affects recruitment, employee development, performance monitoring and long-term career progression.
As AI becomes more integrated into business operations, the challenge for employers will be to translate efficiency gains into sustainable organisational and workforce outcomes. The study suggests that understanding who benefits from AI, who bears its costs and how workers can move into new roles will be as important as determining which tasks the technology can perform.
The wider question, therefore, is not simply whether AI will replace jobs, but how businesses and policymakers can ensure that the transition does not weaken the pathways through which workers gain experience, build expertise and advance their careers.
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