Open the backend of almost any large global enterprise system built over the past thirty years, and you will find India somewhere inside it. Indian engineers maintain core banking platforms. Indian teams are running airline reservation systems. Indian IT firms are managing enterprise infrastructure for Fortune 500 companies. That invisible layer of reliability became one of India’s greatest economic stories, building a $300 billion per year industry and making the country the default technology support partner to the world.
That model was powered by scale, process discipline, and cost efficiency. Large teams, predictable delivery, structured execution. For a long time, that formula worked remarkably well. But AI is beginning to change the economics underneath it. When code can be generated by models, when testing can be automated, when documentation writes itself, and when infrastructure can self-monitor, the traditional advantage of deploying large pools of engineers starts to narrow. Clients are no longer measuring value only by how many people are assigned to a project. They are measuring how much intelligence is embedded in the system.
India’s cost advantage is being blunted. The challenge now is not whether the IT services industry survives, but whether it evolves fast enough to stay central to global technology value creation. This has significant downstream implications for India, because Indian IT exports till now have been used to offset India’s US$ 180-200 energy imports, and hence prevented a further slippage of the rupee to the US dollar.
India therefore needs to urgently rethink its role in the global IT/software delivery model, else this entre business model is at risk.
The first priority is to modernize the core services engine with AI at its heart. AI cannot remain confined to innovation labs or side experiments. It must be embedded into every delivery workflow. Every engineer should be AI-assisted. Every large engagement should have automation designed in from day one. This requires reskilling at a scale the industry has never attempted before. Routine roles in testing and maintenance must transition into AI orchestration, cybersecurity architecture, domain-driven design, and platform operations. The productivity dividend from AI will either protect margins or compress them. There is no neutral outcome.
The second shift must move the revenue model away from pure effort billing toward IP and platform-led offerings. For decades, revenue scaled with headcount. That linkage will face pressure. Indian IT firms need to identify repeatable solutions across sectors and convert them into subscription platforms. AI tools tailored for banking risk management, healthcare workflows, supply chain optimization, or manufacturing analytics can be packaged as products rather than one-off projects. Owning intellectual property changes margins, valuations, and global perception. It signals that India is not only executing someone else’s roadmap but building its own.
The third step is to build strong adjacencies around AI data and sovereign digital infrastructure. India has already demonstrated that it can build digital public systems at a population scale. Identity rails, payment networks, logistics APIs, and interoperable data frameworks are real assets. AI systems layered on top of these rails can become exportable models for other emerging markets. India has an opportunity to become a trusted technology partner for countries that need scalable, affordable, and secure digital infrastructure. That is a differentiated path that draws from India’s own experience rather than copying another ecosystem.
At the same time, India cannot ignore deeper engineering domains. Selective bets in semiconductors, AI infrastructure, advanced computing, and embedded systems are essential. India does not need to dominate every layer of the hardware stack immediately, but it cannot remain absent from it. Chip design, advanced packaging, and AI-enabled edge systems represent strategic capabilities. These areas demand patient capital, strong academia-industry collaboration, and long-term policy continuity. Without deeper engineering muscle, India risks being confined to the lower layers of the value chain.
Talent redesign is equally critical. The industry absorbed millions of engineers into structured delivery roles. The next phase demands AI researchers, product architects, systems engineers, and deep technologists. Universities must integrate more tightly with industry. Frontier labs, fellowships, and joint research programs need to become standard. Retaining top talent and attracting global Indian researchers back home will shape the next decade.
Capital and procurement behavior must also evolve. Deep tech innovation requires longer commercialization cycles. Investors need to become comfortable with extended timelines. Government and large enterprises can act as first buyers of Indian AI and deep tech products, reducing risk for innovators. Incentives for IP creation and simpler regulatory pathways can accelerate experimentation.
Finally, there is a mindset shift required. India’s technology industry grew on reliability and process excellence. Those strengths should remain. But innovation ownership demands experimentation and tolerance for failure. Leadership teams must reward long-term IP creation alongside short-term efficiency.
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