The AI models are here. The harder part is putting them to work. For enterprises, that means adapting models to their needs, connecting them to existing systems and data, redesigning workflows, securing information, navigating regulations and integrating AI into thousands of everyday business processes. This is where India’s decades-long leadership in IT services could give it a new advantage: becoming the world’s AI deployment hub.
The biggest opportunity may not be in building the world’s largest foundation models, but in helping global enterprises use them at scale. The country already has deep experience in implementing technology for global businesses, managing complex IT systems and solving the problems that arise when technology meets real-world operations. AI could give that expertise a new market and, potentially, a new business model.
India’s technology industry is expected to reach about $315 billion in FY26. Yet enterprises are discovering that buying AI is much easier than deploying it at scale. A recent HCLTech-Raconteur study of 500 enterprise decision-makers found that 90% said generative and agentic AI were transforming workflows, but only 18% reported substantial revenue gains. The gap between experimentation and business impact is where the next opportunity lies.
“Models are improving fast. But enterprises still run on legacy systems, fragmented data, policies, permissions, workflows and thousands of exceptions,” says Abhinandan Jain, president – CX & global head of marketing, AIONOS. The difficult work increasingly begins after a company has selected an AI model – connecting it to the systems and processes that actually run the business.
That plays to one of the technology industry’s traditional strengths. IT services companies have spent years integrating complex legacy systems, managing large technology estates and adapting global platforms to the requirements of industries such as banking, healthcare, manufacturing and retail. AI deployment will require many of the same capabilities, but with a new layer of intelligence built into them.
The opportunity extends well beyond implementation. Companies will need help selecting and adapting models, connecting them to proprietary data, building AI agents, redesigning workflows and putting governance around systems that can increasingly make decisions or take actions. For Indian technology providers, this opens the possibility of moving from being an army of technology workers to becoming the technology layer through which enterprises adopt AI.
Atul Soneja, chief operating officer, Tech Mahindra, describes this next phase as the “industrialisation of AI” – moving from pilots to scaled deployment. That distinction matters because an enterprise AI system has to do much more than generate a good answer. It needs to understand the context in which it is operating, follow company policies, access the right information, work with existing applications and know when a human needs to intervene.
This could also create a new market for industry-specific AI. “Open models alone do not perform optimally in specific customer domains,” Soneja says. Models need to be selected, trained and adapted to particular business environments, often with domain experts involved. That creates an opportunity for IT companies to build reusable intellectual property around sectors and workflows rather than simply configuring technology developed elsewhere.
There is another emerging opportunity: managing AI itself. As enterprises deploy hundreds or even thousands of AI agents across customer service, finance, HR and technology operations, they will need people to monitor those systems, manage permissions, evaluate their performance and deal with exceptions. Jain believes managing AI systems will soon become a job in itself, much like managing IT, cloud and security systems today.
The country’s experience in business process outsourcing could prove useful here as well. BPO companies have spent decades dealing with the exceptions and edge cases that sit behind seemingly straightforward business processes. AI can increasingly handle predictable tasks, leaving people to deal with situations that require judgement, empathy or escalation. The combination could create a new model for enterprise operations rather than simply making existing processes faster.
The bigger shift, however, will be in how IT companies make money. “The real pivot is from labour arbitrage to intelligence arbitrage,” says Jaspreet Bindra, founder, AI&Beyond. If AI enables providers to deliver more outcomes with fewer people, the industry will have to move away from selling manpower and hours towards selling productivity, business outcomes and reusable technology.
That transition will not be easy. Global capability centres are becoming more sophisticated, AI-native companies are moving deeper into enterprise technology, and some large businesses may choose to build critical AI capabilities themselves. Indian IT companies will therefore need to invest in domain expertise, AI engineering, governance and intellectual property rather than rely primarily on the scale of their workforce.
The opportunity, nevertheless, is significant. A large technology-services industry, a deep engineering talent pool and decades of experience helping global enterprises navigate complicated technology environments provide a strong starting point. The country may not build the world’s largest foundation models. Its bigger opportunity could be to become the place global businesses turn to when those models need to be deployed, integrated and made useful inside real businesses – turning AI from technology that can do things into technology that actually gets things done.