As artificial intelligence reshapes workplaces, are we measuring its true impact? A new report from IIT Madras argues we are not, calling for an urgent overhaul of labour research to reflect the complex realities of India’s modern job market.
Beyond 'Jobs Lost vs Gained'
For years,
the debate around automation has been a simple numbers game: how many jobs will be created versus how many will be lost? Researchers at IIT Madras's Centre for Responsible AI (CeRAI) argue this is the wrong question to ask. In a recent white paper, they propose a more nuanced, task-based approach. Instead of looking at entire professions, this framework examines which individual tasks within a job are being automated. The paper explains that AI might take over routine activities like data entry or basic customer service queries, while humans remain responsible for decision-making, handling exceptions, and accountability. This distinction is critical because it shifts the focus from outright job replacement to job redesign and augmentation. The impact isn't a binary choice between a human or a machine, but rather how they collaborate in a newly configured workplace.
The Broken Career Ladder
One of the most significant warnings from the IIT Madras paper concerns the future of entry-level jobs, particularly in the IT, BPO, finance, and professional services sectors that have historically employed millions of fresh graduates. These junior roles have traditionally served as a crucial apprenticeship. Performing repetitive, process-oriented tasks—like reviewing basic contracts, writing boilerplate code, or handling password resets—is how young workers build the foundational judgment and experience needed to advance to more senior positions. The researchers caution that as AI increasingly automates these gateway tasks, the traditional career ladder could fracture. Without these initial rungs, companies will need to completely reimagine how they train new talent and create alternative pathways for skill development to prevent a bottleneck that leaves junior employees stuck.
The Elephant in the Room: The Informal Sector
The IIT Madras paper honestly acknowledges its own limitations: its analysis is based largely on evidence from the formal sector. This is a massive gap, as an estimated 90% of India's workforce is employed in the informal economy, a domain that traditional labour surveys have always struggled to accurately measure. The impact of AI on this vast segment is largely a black box. The report suggests informal workers could be affected indirectly through shifts in consumer demand, access to credit via AI-based scoring, or job losses in formal sectors that reduce local spending. This problem is compounded by the rapid rise of the gig economy. Estimates suggest India had around 1.2 crore gig and platform workers in fiscal year 2025, a figure that is notoriously difficult to pin down with conventional survey methods. Experts and even government bodies have noted the urgent need to update data collection to properly account for these workers.
A Call for an India-Specific Approach
Ultimately, the IIT Madras report is a call to action. It urges a move away from applying global automation trends to the Indian context and instead funding more India-specific labour research. The recommendations are not just academic; they are deeply practical. The paper suggests encouraging the development of affordable, narrow AI tools for Micro, Small, and Medium Enterprises (MSMEs) to ensure they are not left behind. Crucially, it calls for creating structured transition pathways for workers whose roles are changing. This means a concerted effort involving companies and policymakers to figure out who pays for reskilling, how long it takes, and whether workers can maintain their earnings during the transition. Simply automating tasks without a plan for the people who performed them is a recipe for social and economic disruption. The goal is to steer technological adoption towards a model that augments human capability, rather than simply displacing it.

















