The other side of AI- Job enabler and creator?

In a recent television interview, TV Mohandas Pai, Chairman of Aarin Capital Partners shared his insights on the evolving role of artificial intelligence (AI) in business workflows, India's position in the global AI landscape, and the future of the country's IT services sector. Pai, a veteran investor and former CFO of Infosys, emphasized a pragmatic, phased approach to AI integration rather than overhyped transformations. 

Pai highlighted that significant value from AI in the next 24 to 36 months will primarily emerge through agentic AI and workflow automation. These areas are relatively easy to implement and adopt, offering quick wins for enterprises. He noted that major IT services companies are already deploying agentic AI for clients, reporting productivity improvements of 10-15%. 

Over the coming 12-24 months, Pai expects these firms to increasingly apply AI to core work, such as maintenance and new feature development, via AI-assisted coding to accelerate processes and reduce backlogs. He cautioned against viewing AI as a free-for-all technology, stressing that in enterprise settings, careful implementation with expert guidance is essential.

Large IT services providers are leading this charge, drawing on their global client interactions and partnerships with AI innovators like OpenAI (ChatGPT), Anthropic, and Perplexity. Addressing underestimated risks in India's AI transition, Pai pointed to a disconnect between market speculation and real-world expertise. He dismissed claims from stock market operators suggesting dramatic job losses or economic downturns due to AI, arguing that global IT giants—constantly engaging with clients and testing AI tools—are far better positioned to understand its impacts. 

While individual users might experiment freely, large businesses must proceed cautiously with legacy systems. Pai acknowledged AI's disruptive potential, predicting initial productivity gains of 15-20% in the first year, potentially rising to 25-30% in subsequent years. This could clear software backlogs, speed up operations, and give early adopters a competitive edge. He cited a recent example from a major Indian finance company that analyzed 2 million call data points with AI, distilling them into actionable use cases worth significant value, with applications focused on peripheral areas like discovery, research, customer acquisition, and servicing rather than core legacy software.

Turning to the upcoming India AI Impact Summit (scheduled for February 16-20, 2026, in New Delhi), Pai described it as timely and impactful. He countered recent IMF comments suggesting India lags behind the US and China in AI, noting that India ranks as the second-largest hub for AI-trained human capital. Much of the AI work for US and European clients is already performed in India through its service companies. 

Pai sees India emerging as a tier-one player alongside the US and China, with major US firms investing in hyper-scale cloud, R&D, and product subscriptions in the country. The summit, he said, will showcase India's strengths and trajectory as a major AI player over the next 3-5 years. On pricing shifts in IT services—from headcount-based to outcome-based models—Pai expressed confidence in the sector's adaptability. 

He described leading IT companies as sophisticated entities in daily contact with clients, already preparing to price AI-driven work to benefit customers through lower costs while incentivizing adoption. He predicted that AI will enable faster, better, and cheaper delivery, releasing talent to tackle backlogs and new projects. Global enterprises, he believes, will likely reinvest cost savings into technology upgrades amid competition, accelerating innovation cycles.

Pai also addressed concerns about over-reliance on Global Capability Centers (GCCs) in India. He outlined AI's three layers: large foundational LLMs dominated by the US (with limited alternatives elsewhere), small LLMs and agentic AI where Indian IT services firms will lead through client collaborations, and application-layer AI where smaller Indian companies and startups innovate. While some startups relocate to the US for better markets and funding, Pai sees strong growth in agentic AI, small models, and application development.