How Indian IT Built Its Annuity

To understand the threat, we need to look at the evolution of the Indian IT services model. It began with labor arbitrage, as Indian firms delivered software development and maintenance at a lower cost than onshore teams. Custom application development followed, as clients outsourced the building of their systems. The build then hardened into application maintenance, where one-time projects became long-running support contracts that billed year after year. Cloud migration and modernization programs opened the next revenue pool, and managed services extended it. AI agents now go after the repetitive work inside maintenance and modernization, where the annuity, not the build, was always the profitable half.

Inside AWS Transform

At its core, AWS Transform is an agentic modernization platform designed to automate the repetitive stages of application migration. It runs specialized agents across three tracks: .NET, mainframe and VMware. The platform starts by discovering the application estate, then analyzes the source code and dependencies, generates a migration plan, refactors the application into a modern architecture, and produces the tests that validate functional parity with the original system. The output is not only converted code but a modernization workflow that removes much of the manual inspection, rewriting and testing that large engineering teams once performed.

Enterprises can point AWS Transform at a legacy estate and cut the discovery, planning and refactoring effort that large modernization programs usually demand. With AWS Transform, Amazon is enabling enterprises to compress migrations that once ran for years into months, and it reports customers have pushed more than 1.1 billion lines of code through the service and saved over 810,000 hours of manual effort. For .NET workloads, the agents run the conversion up to four times faster while removing the Windows licenses that carried much of the operating cost. CIOs can now ask service providers to price modernization on outcomes rather than headcount, and platform teams can hand the agents the repetitive analysis and conversion once done by offshore delivery teams.

The market leaders are publishing the same discount. Infosys told investors it moved 3 million lines of Hertz COBOL into a microservices environment using AI foundation models at 60% lower cost and on a timeline 60% shorter than a conventional migration. A vendor that advertises an outcome delivered for 60% less is teaching every future buyer what that outcome should now cost.

AWS Transform vs The System Integrators

Indian IT majors are not standing still. TCS, Infosys and Wipro have built platforms such as MasterCraft, Topaz and Wipro Intelligence to automate parts of the modernization lifecycle, and these assets still matter where domain knowledge, regulatory context and client-specific architecture decide the outcome. The challenge is that these tools are attached to a services business model, while AWS Transform is attached to cloud consumption. Amazon can afford to make the core service free because it monetizes the destination, the AWS infrastructure where the modernized workload eventually lands. With this, AWS is turning modernization into a cloud acquisition motion rather than a services-led transformation program. The key difference lies in monetization, where hyperscalers monetize the destination while Indian IT firms monetize the journey of getting there. Microsoft and Google Cloud are building the same kind of agentic modernization tooling, which makes this the direction of the platform layer rather than one company's bet.

Chandrasekaran’s strongest point is that context and trust become the scarce resources, and that governing AI systems will need continuous supervision. He is right that agentic services like AWS Transform produce plans and refactored code, while a team still has to handle the fraction it cannot handle, confirm parity, manage the change, and connect the output to how the business runs. That supervision is unlikely to become an annuity at the old scale and margin because governing AI runs on the hyperscalers, the model labs, and Microsoft within Azure AI, all of whom sit closer to it than any integrator and intend to sell it themselves.

What The Shift Means For Each Side

What does this mean to enterprise customers? How should CIOs look at modernization contracts in the age of agents? For enterprise buyers, the shift moves modernization from effort-based pricing toward outcome-based pricing and gives them a stronger negotiating position. For the Indian IT firms, the old annuity is shrinking, and the defensible position lies in governance, domain-specific agents, industry workflows and regulated AI operations rather than in reselling the agents the hyperscalers built. For the hyperscalers, agentic modernization becomes a customer acquisition strategy that pulls workloads onto their own infrastructure.

Where The Pressure Is Showing

The pressure already shows in hiring, where the five largest Indian IT firms cut a combined 6,981 jobs in FY26, reversing a net addition of 12,718 the year before. Cognizant has signaled plans to remove 12,000 to 15,000 roles in an AI-led restructuring that falls hardest on its India workforce.

Revenue is under pressure as well, with Wipro's IT services revenue flat in constant currency for the year even as large deal bookings rose, and Infosys guiding FY27 growth to between 1.5% and 3.5% while naming AI productivity compression as a force working against it. Analysts model 2% to 3% annual deflation across the application services base as agents change the pricing model for application work.

The visa environment adds another layer, with H-1B approvals for the six biggest firms down about 40% year over year, raising the cost of placing Indian engineers in front of US clients at the same time that automation reduces how many are needed.

Where This Leaves Indian IT

Enterprise customers are likely to gain leverage from this transition. A vendor whose historical profit came from the maintenance now being removed has little reason to modernize at full speed. Enterprises can use that to price these contracts on outcomes rather than hours, and ask what the vendor owns in the governance layer that a hyperscaler cannot replicate. The replacement opportunity is genuine, but it is neither captive nor proven at the same scale as the maintenance annuity it aims to replace.

The road ahead for Indian IT firms depends on whether they can build defensible governance, domain and operating-layer IP around AI agents rather than reselling the agents the hyperscalers ship. If they can, AI will expand their market rather than erode it, and until then the safer reading is that agents move the profit toward the companies that supply the intelligence and away from the ones that have long integrated it.