IIT Madras Study Shows AI Alters Specific Tasks Instead of Replacing Whole Careers

Artificial intelligence will likely target individual duties within employment rather than wipe out entire vocations in India, according to a recent white paper released by the Indian Institute of Technology Madras. Published on the institute's portal on Monday, the study outlines that judging the impact of technology by looking at broad professions hides how work actually shifts and touches different worker groups. The report, titled 'How AI Is Reconfiguring Work in India,' was initially shared at an industry governance conclave held on campus on October 22.

Workplace Shifts

Balaraman Ravindran, who leads the Centre for Responsible AI at the institute, stated that the paper challenges the belief that new technology always eliminates employment. Researchers noted that the outcomes rely on specific duties, industry rules, technology expenses, and company restructuring choices. The study analyzed sectors such as manufacturing, call centres, banking, law, software engineering, small businesses, sanitation, and creative fields. It also pointed out brand new job titles appearing in the market, including ethical AI specialists, conversation designers, remote robot operators, forward deployed engineers, and AI trainers.

Evidence and Framework

The authors built their findings on government statistics, company reports, vendor claims, and journalistic pieces. Examples range from a ₹246.72-crore computing allocation given to Sarvam to a Gartner projection that eighty percent of customer service issues might be automated by 2029. Despite these references, the writers admitted a shortage of rigorous local labour data tracking skill changes within occupations. To measure these shifts, the team proposed a four-level model separating tasks, jobs, occupations, and broader market outcomes. Research director Gokul S Krishnan explained that taking over a duty does not mean an entire role vanishes, as results depend on cost, safety, regulations, and institutional choices.

Recommendations

The paper introduced the concept of AI-cost geography to explain how tool expenses shape adoption across regions. Affordable software could spread rapidly in micro, small, and medium enterprises for bookkeeping and quality checks, while advanced applications may stay concentrated in large urban firms with better infrastructure. Experts warned that benefits will divide entry-level staff, informal employees, and rural communities unevenly. To counter this, the authors suggested three measures: expanding affordable tools for smaller businesses, conducting local research on automation costs, and building structured training pathways to help people transition into emerging responsibilities.

Context

Technology adoption in developing economies often creates a gap where basic roles shift quickly while high-end infrastructure remains limited to major urban centres. Educational and research institutions frequently study these economic transitions to help policymakers design training programs before widespread automation changes the job market.