As artificial intelligence reshapes industries across India, the conversation is moving from abstract possibilities to concrete realities. In a pivotal statement, IIT Madras has cut through the noise, asking a fundamental question for our AI future.
The Core of the Argument
In
a recent white paper, researchers at IIT Madras argued that while AI is more likely to reconfigure job tasks than eliminate entire professions, this transformation presents a critical challenge, especially for entry-level workers. The report, 'How AI Is Reconfiguring Work in India', moves the debate beyond simple job loss statistics. It focuses on how routine tasks in sectors like IT, BPO, and finance—traditionally the first step on the career ladder for many graduates—are being automated. This automation, the paper warns, could disrupt the apprenticeship model where junior employees learn by doing repetitive tasks. If AI handles this work, a crucial question arises: how will the next generation of professionals gain the experience needed for senior roles? This leads directly to the policy debate about upskilling. In this context, Professor Balaraman Ravindran of IIT Madras stated that the cost of retraining should be shared: 'Partly the state, partly the industry and partly the learner.'
Why Funding is the Crucial Question
The debate over who pays for retraining is not just an economic calculation; it's a decision that will shape the social impact of AI in India. If individuals are left to fund their own upskilling, it could widen the gap between those who can afford continuous education and those who cannot, potentially deepening inequality. If corporations are solely responsible, they may only train for their immediate needs, creating highly specialised workers who may struggle if their new roles are also automated later. A state-led approach could ensure broader access but might lack the agility to keep up with the fast-changing demands of the industry. The IIT Madras position highlights that a hybrid model is necessary. This acknowledges that the benefits of AI—increased productivity for companies, economic growth for the nation, and new opportunities for individuals—are shared. Therefore, the responsibility to manage the disruption, including the financial cost of retraining, should also be a shared one.
The Main Contenders: Who Could Pay?
Exploring the shared responsibility model reveals three primary sources of funding. First, the government can act as a key enabler. National initiatives like the 'IndiaAI Mission' and 'Skill India' are already in place, with programmes like Pradhan Mantri Kaushal Vikas Yojana (PMKVY) 4.0 focusing on emerging tech skills. The government has also launched incentive programmes like FutureSkills Prime, which reimburses a portion of course fees for learning AI and other digital skills. Second, the industry has a vested interest. According to a 2026 KPMG report, many Indian CEOs are already planning to reskill their workforce, with 48% prioritising AI education across their companies. Corporations benefit directly from a skilled workforce and can offer targeted, on-the-job training that is immediately relevant. Finally, the individual learner also plays a role, investing their time and, in some cases, money. The key is finding the right balance to ensure training is accessible, affordable, and aligned with real-world job opportunities.
The Indian Context: A Challenge of Scale
The sheer scale of India’s workforce makes this challenge particularly urgent. With millions of young people entering the job market annually and a vast existing workforce in sectors susceptible to automation, a coherent national strategy is not a luxury but a necessity. The IIT Madras paper notes that while large companies can absorb the costs of AI adoption, small and medium-sized enterprises (MSMEs) may struggle, creating an uneven transition. This could lead to a 'digital divide' where a small, highly-skilled elite in major cities pulls away from the rest of the country. Policies must therefore be inclusive, offering grants, subsidies, or low-interest loans to help smaller businesses and individual workers adapt. Furthermore, the nature of work in India, with its large informal sector, adds another layer of complexity that requires India-specific research and tailored solutions.
