Modern Artificial Intelligence (AI) systems:
especially large language models, can engage in natural conversations with users, making complex knowledge accessible to citizens.
These systems analyze large volumes of data, learn patterns from that data, and generate responses, summaries, translations, or predictions. They learn from experience and improve their responses over time. This enables them to provide predictions and recommendations.
Training and Inference
Large Language models operate through two main processes – training and inference.
The model processes large amounts of text, image, and other data during training. It is at this stage that the model's knowledge and capabilities are created. Very large data sets, specialized AI chips (GPUs), and massive computing power are required during this phase.
During inference, the trained model generates responses or predictions. It is this capability that the systems deliver practical services to users. Countries and companies that can train large models control the core technological capability of the AI ecosystem, while inference determines the scale, cost, and social impact of deployment.
The traditional websites function mainly as digital libraries of information that you can search and navigate. They generally present prewritten information and require users to search and navigate menus rather than interpreting questions in a conversational manner. They are often difficult to navigate.
Many large language models have been trained largely on publicly available internet data, much of which originates from developed countries. They reflect language, institutions, and social contexts from these regions. For example, a model trained mainly on Western data may not fully understand concepts such as panchayat governance, mandi markets, self-help groups, or local cropping patterns unless it has been exposed to Indian data.
This gap is gradually narrowing as more Indian data, languages, and public-sector information are incorporated into AI systems. Governments, research institutions, and private companies are now working to train AI systems in Indian languages and to incorporate local information. Training these systems requires clear national data governance frameworks. India has hundreds of languages and dialects. Models trained primarily on Western datasets may not adequately represent this linguistic diversity.
Dependence on foreign AI platforms could create vulnerabilities in critical sectors such as governance, defense, and digital infrastructure.
The IndiaAI Mission seeks to support:
High performance computing (HPC) infrastructure
domestic research
Indian language models
startup collaboration
Companies that are leaders in the LLM market realize that India will be one of the largest global markets for AI, outside of the US. They are building computing infrastructure and global capability centers and have entered into strategic tie-ups with large domestic companies such as TATA, Jio, and others. Companies are investing in Indian language models, speech recognition, and translation tools, and entering into collaborative arrangements with Indian startups.
Initiatives to fund Indian AI startups developing applications and models have been launched by both the Government of India and companies such as Google. Pricing strategies that are suitable for India have been put in place. AI Certification programs, university partnerships, and skill development programs have been launched. These will help future developers and professionals.
AI has the potential to bridge information asymmetry between citizens and deliver solutions in local languages in an easy-to-understand and simple way. These core capabilities can be leveraged in education, healthcare, agriculture, credit, finance, and other areas. These tools can be used to detect fraud and deliver personalized solutions and guidance to individuals. It can reduce dependence on intermediaries and allow citizens to access information and services directly.
India’s widespread mobile connectivity and low-cost data have created the conditions for large-scale adoption of AI services. The most important question for policymakers is how these capabilities can be applied to address development challenges.
Artificial intelligence represents the next major layer of digital capability after connectivity and digital identity. Countries that successfully integrate AI into governance, agriculture, education, and public services will gain significant advantages in productivity and human development. With its digital public infrastructure, large technology workforce, and vast mobile connectivity, India has the opportunity not only to adopt these technologies but to deploy them at a scale unmatched anywhere in the world.
Use cases:
1) Several countries have begun integrating artificial intelligence into public service delivery. Singapore has deployed conversational AI assistants across government portals to help citizens access information about permits, taxes, and social services. Systems such as “Ask Jamie” handle millions of queries each year and significantly reduce routine administrative workload.
While Singapore demonstrates the feasibility of such systems, India’s scale of mobile connectivity and digital public infrastructure suggests that similar AI-enabled citizen services could operate at a far larger scale, potentially serving hundreds of millions of users. India’s Digital Public Infrastructure will form the foundation for deploying AI-enabled public services at a national scale.
2) Estonia, a Baltic nation, is known for having one of the most advanced digital governments in the world. Its digital transformation project—often called “e-Estonia”—has gradually incorporated artificial intelligence into public administration.
Today, almost 99% of government services are available online, ranging from taxation and healthcare to business registration and voting.
Citizens use a secure digital identity (e-ID) to access these services, sign documents, and interact with government agencies electronically. India’s Aadhaar-based digital identity system provides a similar foundation for secure access to government services at scale. Multi-language support models, which have already been showcased in the pilot model at the Global AI Summit, can be further developed and integrated into an India-specific government services AI stack.
3) AI advisory systems used in corn and soybean farming in the U.S. Midwest analyze weather data, soil conditions, and satellite imagery to guide farmers on irrigation, fertilizer use, and pest management. Studies show that such systems increased yields by about 5–10% while reducing fertilizer use.
4) AI tutors such as Khanmigo developed by Khan Academy use large language models to provide personalized learning support to millions of students, demonstrating how conversational AI can deliver tutoring and skill development at scale.
5) South Korea has introduced AI teacher assistants in public schools to support mathematics and English learning. These systems provide personalized exercises and feedback, allowing teachers to focus on mentoring and higher-level instruction. That example is powerful because it shows government-led deployment in a national education system, not just a private platform.
Policy recommendations:
The productivity gains, practical use cases, and potential social impact of large language model–based artificial intelligence are likely to be transformative across sectors such as education, healthcare, agriculture, and public administration.
However, the cost of training frontier large language models—including the computing infrastructure, specialized hardware, data resources, and engineering talent required—is extremely high. Most countries cannot afford to delay adoption while waiting to develop fully sovereign models.
At the same time, many practical applications of AI do not require building large foundational models from scratch. AI agents and domain-specific applications for individual use cases can be developed at relatively low cost using existing models.
In the short term, therefore, a pragmatic strategy would be to leverage existing global LLM capabilities while continuing to invest in developing sovereign AI models and infrastructure.
The selection of underlying LLM platforms should remain a sovereign policy decision, guided by considerations such as data governance, national security, and long-term technological capability. Appropriate terms of engagement and data-use safeguards will need to be negotiated with companies that own these models.
Such a dual strategy would allow countries to benefit immediately from AI-driven productivity gains while gradually building domestic technological capabilities.
Artificial intelligence is emerging as the next major layer of digital capability after connectivity and digital identity. Nations that harness this technology to improve governance, education, agriculture, and healthcare will gain decisive advantages in productivity and human development. With its digital public infrastructure, technology talent, and vast mobile connectivity, India has the opportunity not merely to adopt these technologies but to deploy them at a scale that few countries can match. The central policy question, therefore, is not whether AI will influence national development, but how quickly it can be integrated into systems that expand opportunity and improve the lives of millions of citizens.