GenAI Leaves Indian Outsourcers Fighting For Value Capture

When productivity improvements of 25% fail to boost revenue growth beyond 5%

Generative AI offers remarkable efficiency gains while presenting a profound challenge for the global IT services industry — a sector concentrated in India and central to its export economy.

For decades, Indian technology firms thrived by deploying their engineering talent to serve primarily Western clients. Now they face a critical question. Will AI’s productivity dividend translate into revenue growth? Or will fierce competition see these gains competed away through price reductions?

Industry soundings suggest the deflationary dynamic may already be taking hold. JPMorgan’s conversations with executives, deal advisors and consultants across India’s technology hubs reveal growing concern — AI-driven efficiencies are fuelling pricing pressures. This threatens to constrain medium-term industry growth to a modest 4-5%, with little prospect of acceleration into fiscal year 2026.

This emerging reality challenges the earlier narrative that AI would primarily unlock new revenue streams. Though leading firms have demonstrated impressive GenAI engagements — Accenture reported $1.4 billion in such bookings in a recent quarter, nearly 7% of its total new business — yet there is growing recognition that such activity often displaces, rather than purely supplements, existing IT expenditure. The efficiency gains are still substantial. Cognizant now generates approximately 20% of its code using AI assistance. TietoEVRY reports productivity improvements of up to 25% in software development.

Clients are demanding — and receiving — a share of these AI-generated savings. Capgemini characterizes it as an expectation for gradual cost improvements from GenAI, similar to historical productivity demands. Accenture says its contracts presuppose continuous technology-driven efficiency gains, a trend that GenAI merely accelerates. LTIMindtree reports instances where securing vendor consolidation deals hinged explicitly on commitments to share AI-derived benefits with clients. Some industry participants observe clients wielding both macroeconomic uncertainty and AI capabilities as negotiating leverage to extract more favorable terms.

This environment poses a conundrum for an industry whose expansion has historically outpaced global economic growth. Its fortunes align closely with corporate revenue trends in the U.S. and Europe. Adding to the challenge, even amid weak operating leverage and potentially slowing growth, continued investment in technologies like GenAI remains imperative, further pressuring margins.

Views on AI’s near-term financial impact remain divided. One school anticipates an initial deflationary phase characterized by cost reductions and productivity pass-backs. This could potentially evolve into a more positive longer-term outlook as AI integration deepens. An alternative perspective foresees initial cost inflation associated with AI readiness. This includes cloud migration and extensive data engineering, followed by a more pronounced deflationary period affecting traditional maintenance outsourcing. Both scenarios point toward significant adjustments for a sector vital to India’s economy.

To counter potential revenue erosion, service providers recognize the need to overhaul their commercial frameworks. Delivering the same output requires fewer inputs and thus risks lower fees under traditional models. The strategic imperative is to shift toward outcome- or value-based pricing structures. This approach would decouple remuneration from labor inputs and link it instead to quantifiable business results.

Cognizant’s chief executive reminds us of the historical precedent of cloud adoption. He argues that anxieties about technological disruption often underestimate long-term technology spending. The immediate challenge involves navigating substantial pricing headwinds alongside mandated technology investments. The critical test for India’s IT services sector lies in its ability to adapt commercial models swiftly enough to capture the value generated by AI, rather than merely enabling efficiency gains further down the value chain.