The hiring patterns tell a different story than the headlines. While Indian IT majors have been trimming workforces through 2025, global firms are quietly expanding their India operations. Capgemini, Cognizant, and EPAM are adding thousands of roles domestically, betting that AI implementation will increase rather than decrease their need for skilled technical talent.
This isn’t the automation narrative we expected. The conventional wisdom suggested AI would hollow out offshore delivery centers first. Instead, these firms are discovering that AI projects require more human oversight, not less. Client implementations need specialists who understand both the technology stack and the business context. That combination remains expensive to source in developed markets.
The margin math is straightforward. A senior AI consultant in New York costs $200-300 per hour. The same expertise level in Bangalore runs $50-80. When client budgets for AI transformation projects stretch into millions, that arbitrage becomes material to quarterly results. But the talent arbitrage only works if the India-based teams can deliver equivalent quality outcomes.
What’s more interesting is the divergence within the Indian IT sector itself. TCS, Infosys, and Wipro have been managing workforce reductions through natural attrition and selective hiring freezes. Their client mix skews toward maintenance and support work where AI can genuinely reduce labor requirements. The global firms expanding in India are positioning for net-new AI implementation projects where human expertise remains critical.
This creates a talent circulation pattern worth watching. Experienced professionals leaving the Indian majors become available for the global firms’ expansion plans. The skill sets transfer cleanly, but the work cultures differ significantly. Global firms typically offer flatter hierarchies and faster decision-making, which appeals to senior professionals who felt constrained by the traditional pyramid structures of Indian IT giants.
The regulatory environment supports this shift. Recent changes to India’s employment laws have made it easier for global firms to establish substantial operations without the bureaucratic overhead that previously favored domestic companies. The infrastructure investments in tier-2 cities have also reduced the concentration risk of operating solely from Bangalore, Hyderabad, and Chennai.
But the pattern reveals something deeper about AI adoption cycles. Companies aren’t automating away human roles as quickly as anticipated. They’re creating new categories of work that require human judgment applied to AI-generated outputs. Quality assurance for AI systems, bias testing, and integration oversight all require human expertise that can’t be easily automated.
The governance question for boards becomes: are we correctly anticipating the workforce implications of our AI investments? The global IT firms betting on India expansion seem to believe that AI will create more skilled work, not less. That’s a contrarian position worth examining when setting compensation budgets and succession planning timelines.
My Boardroom Takeaway
Directors should question whether their AI implementation strategies account for increased rather than decreased talent needs in the near term. The global IT firms’ India hiring surge suggests that successful AI adoption requires more human expertise, not less. NRCs may want to revisit workforce planning assumptions and ensure compensation strategies can compete for the specialist skills that AI projects actually require. The talent circulation between shrinking and expanding IT operations also creates recruitment opportunities for companies willing to move quickly.