India’s push to establish a sovereign artificial intelligence ecosystem is gathering momentum through initiatives such as the IndiaAI Mission and investments aimed at strengthening domestic computing infrastructure.
While efforts to expand access to more data centers remain critical, industry leaders believe the larger opportunity lies in creating AI systems tailored to India’s unique needs.
As enterprises seek greater control over data, AI models, and decision-making processes, attention is increasingly turning toward the development of localized, multilingual, and industry-specific AI ecosystems. At the same time, questions remain about whether the benefits of AI adoption will reach India’s vast network of micro, small and medium enterprises (MSMEs) and manufacturers, rather than being concentrated among large corporations.
In an interaction with ET Digital, Xebia Global CEO Anand Sahay discussed the country’s sovereign AI aspirations, the growing demand for enterprise ownership of AI infrastructure, and the role of contextual AI systems designed for local industries and languages.
According to Sahay, India’s sovereign AI initiative carries significant strategic importance because it addresses one of the biggest obstacles to AI adoption—access to computing resources.
He noted that AI experimentation, model development, and large-scale enterprise deployments require substantial computational capacity, and sovereign GPU programs could accelerate adoption across sectors by making such infrastructure more accessible.
He added that the potential extends well beyond infrastructure. India, he said, is uniquely positioned to build domain-specific and multilingual AI solutions aligned with local business environments, regional languages, and sector-specific requirements. Such capabilities could help the country emerge not only as a major consumer of AI technologies but also as one of the world’s largest applied AI economies.
Sahay said this shift presents enterprises with an opportunity to accelerate AI adoption while creating systems that are more contextual, localized, and aligned with evolving governance and regulatory requirements.
“Absolutely. Sovereignty today is no longer limited to the question of where data resides. Enterprises are increasingly evaluating whether they control the broader AI stack, including infrastructure, models, governance frameworks, and operational outcomes.
“Regional cloud infrastructure addressed part of the challenge around data residency, but organizations are now asking much deeper questions around ownership and control. They want to understand who controls the models, where inference happens, how enterprise knowledge is protected, and how much operational autonomy they truly possess within AI environments.
“This becomes especially critical for regulated sectors such as banking, financial services, healthcare, telecom, and public sector environments, where governance, risk management, and data sensitivity are central to enterprise operations.
“The broader industry conversation is now shifting from “Where is the data stored?” to “Who controls the intelligence layer?””
