India’s artificial intelligence journey is entering a decisive new phase. After spending the past two years building computing infrastructure, supporting indigenous AI models, creating data platforms, and formulating governance frameworks, the government is now moving towards a more challenging objective: integrating AI into the functioning of ministries and public institutions.
In a significant step towards that goal, the Ministry of Electronics and Information Technology (MeitY), through the National e-Governance Division (NeGD), has empanelled six technology firms—Tata Consultancy Services (TCS), NEC Corporation India, Kyndryl Solutions, Innefu Labs, CoRover and Cactus Communications—to develop and deploy AI solutions across government departments.
More than 80 companies participated in the selection process, underscoring the growing importance of AI in the public sector. The empanelled firms will provide specialised expertise in artificial intelligence, machine learning and data science to help ministries implement AI-driven applications and services.
The initiative may appear to be a routine technology procurement exercise, but it signals something much larger. India is moving from building AI capabilities to deploying AI at scale. The success of this transition will play a major role in determining whether the country’s ambitious AI vision can translate into measurable improvements in governance, productivity and citizen services.
The Rise of India’s AI Ambitions
Artificial intelligence has become one of the central pillars of India’s technology strategy. Policymakers increasingly view AI not merely as a digital technology but as a foundational capability that will shape economic growth, national competitiveness, public administration and scientific innovation over the coming decades.
The launch of the IndiaAI Mission marked the government’s most comprehensive effort in this direction. Approved with an outlay exceeding ₹10,000 crore, the mission seeks to build a complete AI ecosystem spanning compute infrastructure, datasets, foundation models, startup support, skills development and responsible AI frameworks.
Unlike earlier technology programmes that focused largely on digitisation, the IndiaAI Mission aims to position India as both a producer and consumer of AI technologies. The emphasis is on developing capabilities across the entire AI stack—from chips and computing infrastructure to applications and citizen services.
Speaking at the World Economic Forum earlier this year, Union Minister for Electronics and Information Technology Ashwini Vaishnaw highlighted this broad approach, noting that India is developing capabilities “across the AI stack, from applications and models to chips, infrastructure and energy.” He also observed that India’s IT industry is increasingly pivoting towards AI-based solutions that drive productivity and value creation.
The minister has repeatedly argued that India should focus not only on developing AI models but also on creating practical applications. At Davos, he described India as having the potential to become the world’s “use case capital,” stressing that the next stage of AI competition will be determined by the ability to build impactful applications rather than merely train large models.
Building the Foundations
One of the most important pillars of the IndiaAI Mission has been the creation of AI computing infrastructure.
Access to high-performance computing resources has emerged as a strategic requirement in the global AI race. Recognising this reality, the government has invested heavily in expanding GPU capacity and making computing resources accessible to startups, researchers and academic institutions.
The IndiaAI Compute initiative has already created a substantial national AI infrastructure base, with plans for further expansion. This effort seeks to reduce dependence on overseas cloud providers while ensuring that Indian researchers and innovators have access to the resources required to build advanced AI systems.
At the same time, the government has encouraged the development of indigenous foundation models capable of operating effectively in India’s diverse linguistic and socio-economic environment. Several startups and research organisations have received support to develop large language models and AI systems designed specifically for Indian use cases.
The broader objective is clear: India does not want to remain merely a market for foreign AI products. It wants to create an ecosystem capable of generating home-grown innovation.
The Shift from Infrastructure to Applications
If the first phase of India’s AI strategy focused on infrastructure, the second phase is increasingly centred on applications. This is where the empanelment of six firms assumes significance.
Government departments possess vast quantities of data and manage complex workflows affecting millions of citizens. AI has the potential to improve administrative efficiency, strengthen policy analysis, automate repetitive tasks, enhance grievance redressal systems and support data-driven decision-making.
The empanelled firms are expected to help ministries design, deploy and manage AI solutions tailored to specific operational requirements. Rather than requiring every ministry to build in-house AI teams, the government is creating a pool of specialised expertise that can be accessed across departments.
This approach reflects a growing recognition that the challenge is no longer whether AI technology exists but how to operationalise it effectively within government systems. The thinking behind this transition was articulated clearly by MeitY Secretary S. Krishnan during the India AI Impact Summit 2026.
“The India AI Mission is modeled to address diverse needs and real-world challenges,” he said while discussing the evolution of India’s AI strategy. More importantly, he emphasised that infrastructure alone is not enough.
“We are providing compute, models and data for one reason only—to build applications with real impact,” Krishnan said, arguing that AI investments must ultimately translate into tangible outcomes for citizens and public institutions.
In another observation that captures the philosophy behind the government’s current approach, the Secretary noted that the India AI Impact Summit was fundamentally about identifying “impactful applications that we can create using AI.”
These remarks reflect a growing consensus within government that the real measure of success will not be the number of GPUs installed or models launched, but the quality of services delivered through AI-enabled systems.
Building on India’s Digital Public Infrastructure
India enters this phase with an important advantage: a robust digital public infrastructure ecosystem.
Over the past decade, the government has developed platforms such as Aadhaar, DigiLocker, UMANG, Fastag, Bhashini, UPI and various e-governance systems that have digitised large segments of public service delivery.
These platforms generate data, create digital workflows and establish trusted digital identities—foundational elements that can support AI deployment.
The Bhashini initiative is particularly relevant in the AI era. By developing language technologies for Indian languages, it addresses one of the most significant barriers to AI adoption in a multilingual society.
Institutions such as the National Informatics Centre (NIC), Digital India Corporation (DIC), National e-Governance Division (NeGD) and IndiaAI have also created an institutional framework capable of supporting large-scale deployment of AI solutions across the public sector.
Few countries possess a digital governance ecosystem of comparable scale and reach.
Opportunities Across Sectors
The potential applications of AI in governance are extensive.
In healthcare, AI can assist in diagnostics, disease surveillance and resource allocation. In agriculture, it can support crop monitoring, weather forecasting and advisory services. Urban administrations can use AI for traffic management, waste management and infrastructure planning. Public grievance systems can become more responsive through intelligent automation.
The judiciary, education sector and social welfare programmes also stand to benefit from AI-assisted processes that improve efficiency and accessibility. The government hopes that AI deployment across ministries will not only improve service delivery but also create a demonstration effect that encourages wider adoption across the economy.
This broader ecosystem is attracting considerable investor interest. According to Ashwini Vaishnaw, more than USD 200 billion in investments could flow into India’s AI ecosystem over the next two years, reflecting growing confidence in the country’s AI trajectory.
The Challenges Ahead
Despite the optimism, substantial challenges remain. The first challenge is data quality. Government datasets are often fragmented across departments, stored in different formats and governed by varying standards. AI systems can only be as effective as the data that powers them.
The second challenge is talent. While India produces a large technology workforce, advanced AI expertise remains limited. The demand for AI professionals is rising rapidly. Vaishnaw recently noted that AI-related jobs in India are growing by 15–20 per cent annually, highlighting both the opportunity and the shortage of specialised talent.
Cybersecurity presents another major concern. As AI systems become integrated into critical public infrastructure, they also become attractive targets for cyberattacks, manipulation and data breaches.
Questions of transparency, accountability and algorithmic bias are equally important. Government decisions can have profound consequences for citizens, making explainability and responsible AI governance essential requirements rather than optional considerations.
There is also the challenge of organisational change. Deploying AI successfully requires new workflows, training programmes, procurement frameworks and leadership commitment. Technology alone cannot transform institutions.
Global discussions at the India AI Impact Summit highlighted additional concerns relating to data sovereignty, workforce disruption and the need for continuous upskilling as AI becomes more pervasive across sectors.
A Defining Test
The empanelment of six firms by MeitY represents more than a procurement decision. It is a test of whether India’s AI strategy can move beyond vision documents and infrastructure announcements into practical implementation.
The government has already laid substantial foundations through the IndiaAI Mission, digital public infrastructure, computing investments and support for indigenous AI innovation. The next challenge is execution.
As S. Krishnan has repeatedly argued, AI will ultimately be judged not by its sophistication but by its impact. The task now is to convert algorithms into outcomes, infrastructure into services, and ambition into measurable public value.
If India succeeds, it could demonstrate a model of AI adoption that combines technological innovation with inclusive development and public service transformation. If it falls short, the limitations are likely to emerge not from technology itself but from the complex realities of implementation, governance and institutional readiness.
The coming years will determine which of these futures prevails.
