Microsoft India and South Asia President Puneet Chandok has challenged the conventional wisdom that artificial intelligence is driving widespread job losses. According to reports from Business Standard, Chandok stated that companies are not using AI as a pretext to cut jobs, emphasizing that "I actually disagree with that. I don't think organizations are looking at this to cut jobs." He explained that organizations are now adapting to the reality that they must work together to achieve returns on investment, as AI requires collaboration beyond traditional capital deployment.
Microsoft has identified significant growth in specialized AI-related positions, with forward design engineers, agent office traders, and enterprise ontology experts representing roles that did not exist 12 months ago. As reported by Business Standard, these frontier professionals in India, defined as people working with agents and setting intent, represent 32% of Microsoft's research base of 20,000 AI users across 10 markets, compared to a global average of 16%. The company's 2026 Work Trend Index shows that 78% of professionals in India are producing work not possible a year ago, versus 58% globally.
Despite Microsoft's optimistic outlook, significant job cuts have occurred across global capability centres in India. According to reports from Business Standard, Uber cut about 250 jobs from its technology centre, while PayPal showed the door to about 500 people. These redundancies follow Visa's decision to kick out 1,300 people, or 40% of its India workforce, demonstrating the mixed impact of AI adoption on employment across the technology sector. Recent developments show Uber is planning to cut about 3,300 jobs globally, representing approximately 10% of its workforce in a companywide restructuring aimed at reducing management layers and reallocating resources to core businesses.
Enterprise AI adoption has faced significant hurdles, with Chandok acknowledging that "Enterprise AI adoption has lagged expectations over the last couple of years due to a host of reasons." As reported by Business Standard, these challenges include change management issues, workflow orchestration difficulties, massive investments, questions over returns, and high token usage costs that have driven AI bills through the roof. The company notes that many proofs of concept show promise in laboratories but fail when implemented in real-world environments, with estimates suggesting less than 10% of PoCs are able to scale up.
Large language model providers are responding to adoption challenges by ensuring their products are embedded across workflows to generate quicker revenue realization and justify their substantial investments. According to Business Standard, Chandok disagreed with suggestions that companies are struggling to generate real value, emphasizing that "companies need to build intelligence, trust in the system and leadership for greater enterprise AI adoption." The transition requires new skills, ways of working, and organizational structures, representing a fundamental shift in how organizations approach technology integration and workforce development.
