India’s AI Future: Opportunities and Challenges in Developing Indigenous AI Foundation Models for Indian Languages
By: Prof. Chiranjib Sur
As artificial intelligence (AI) reshapes the global technological landscape, India stands at a critical juncture supported by its young population, in its AI journey- not only to shape its future but also to change the living standard of its people. Large Language Models emerged as generalized AI frameworks for solving problems that are repetitive and are well-documented, and backed by the vast amounts of data. Major advancements in large language models (LLMs) are driven by global tech giants, western countries, which have fewer language barriers and simpler cultural challenges. . The concern is that LLMs can automate a large part of the manual and repetitive jobs that created the growth story of modern tech-savvy India. With this in mind, it is time for India to develop indigenous AI models tailored to its linguistic diversity, cultural context, and economic priorities. However, building AI foundation models, particularly LLMs in Indian languages, presents both opportunities and challenges. What India needs is a secure, ethical, and affordable AI model that align with Responsible AI principles to realise its AI potential. Addressing challenges such as lack of data, shortage of skilled workforce, affordable training resources, biases in training datasets, and linguistic complexity of language (which changes every 50 km) will be crucial. This article explores India’s AI aspirations, the challenges of building LLMs for Indian languages, and a roadmap for a self-reliant AI ecosystem.
Why India Needs Indigenous AI Models
The development of AI models within India is not merely a technological ambition but a strategic necessity. Regarding digital sovereignty and data security, India’s reliance on foreign AI models raises concerns about data privacy and national security. Many global LLMs operate under regulatory frameworks that may not align with India’s policies. Indigenous AI models ensure data sovereignty and compliance with national regulations. Most LLMs are trained primarily in English, making them inadequate for India’s 22 official languages and numerous dialects. India needs AI models that accurately understand regional languages makeing technology more inclusive and preserving India’s linguistic and cultural heritage. India also needs AI to be affordable and inclusive . Imported AI models are often expensive, limiting accessibility for Indian businesses, startups, and public institutions. A locally developed AI ecosystem will enable cost-effective solutions, ensuring that AI benefits reach all socio-economic groups and bridge the digital divide. India needs an ethical and responsible AI development approach , because AI trained on foreign data often fails to reflect Indian realities. Indigenous AI models allow India to establish ethical frameworks aligned with local socio-economic conditions, ensuring fairness, transparency, and accountability.
Challenges in Developing AI Foundation Models for Indian Languages
Despite its advantages, building AI foundation models in India presents several challenges that must be addressed systematically. We have a scarcity of high-quality data.AI models require large amounts of high-quality, labeled training data.. While widely spoken languages have extensive datasets, Indian languages suffer from a severe data shortage due to limited digital resources and annotated corpora Indian languages are structurally complex and very different from each other.Training AI on limited or biased datasets can reinforce stereotypes and misinformation. We need to ensure unbiased, representative data collection is crucial. AI models should represent all linguistic and socio-economic groups fairly to prevent deepening inequalities. Also, developing large AI models demands substantial computational power, requiring investments in high-performance GPUs, AI supercomputing facilities, and cloud infrastructure. While India has a growing AI talent pool, expertise in AI model training, NLP, and Responsible AI remains limited.
India’s Approach: Building a Responsible AI Ecosystem
The first step to overcoming these challenges and harnessing AI’s potential is for India to adopt a strategic and multi-pronged approach with experienced leadership paying more attention to timely deliverables. We need government-led AI initiatives like India AI Mission to promote AI research, innovation, and applications. Developing AI models for Indian languages is important, but solving real-world problems is even more crucial. As mentioned, public-private partnerships can accelerate AI model development and provide resources to startups and researchers, but we must create large-scale, publicly available language datasets to tackle data scarcity. Initiatives like Bhashini, to digitise Indian languages, are commendable but should be expanded. Many western countries are encouraging open-source AI to democratise AI research. India must establish a responsible AI framework to ensure fairness, accountability, and transparency. This includes bias-free training datasets, explainability and fairness metrics, and ethical AI governance standards. A regulatory framework similar to the EU’s AI Act (or USA’s National Artificial Intelligence Initiative Act of 2020 (NAII)) can ensure AI development aligns with national interests and public welfare. It’s important to note that strong AI infrastructure—like AI supercomputers, data centres, and HPC clusters—is critical."We must invest in AI education and research to expand its AI talent pool. Educational institutes should integrate specialized courses in natural language processing, deep learning, and AI ethics. Another aspect is collaboration with global AI research institutions will also accelerate India’s AI progress.
The Road Ahead: Shaping India’s AI Leadership
India can potentially become a global leader in AI: however, achieving this ambitious vision requires a self-reliant, ethical, and inclusive AI ecosystem. With indigenous AI models that handle diverse accents and follow responsible practices, India can create AI solutions for domestic and global needs. In the coming years, many AI-driven applications in healthcare, agriculture, education will significantly impact India's digital transformation. Homegrown AI models will not only empower startups, researchers, and businesses but also drive innovation across sectors. However, achieving AI self-sufficiency will require long-term investments, strong policy support, and collaborative innovation. If India can effectively address challenges like data scarcity and bias, it will not only build world-class AI models but also ensure AI serves all Indians, regardless of language or economic status. The time is ripe for India to take bold steps toward AI leadership—an opportunity that, if seized, will define its technological future.
Author: Prof. Chiranjib Sur, Assistant Professor, Mehta Family School of Data Science and Artificial Intelligence, and Associate Faculty, School of Business, IIT Guwahati.
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