India’s AI Revolution: Overcoming Barriers To Become a Global Leader

Following the sensational arrival of the Chinese DeepSeek a couple of months ago, India is reportedly at a crucial juncture in its technological evolution. The nation has long been recognized as a global player in information technology, with cities like Bengaluru serving as hubs for tech innovation and IT outsourcing. However, as the world plunges deeper into the artificial intelligence (AI) revolution, India’s ability to not only keep up but lead in the AI race has come under scrutiny. With global tech powers such as the U.S. and China accelerating their AI developments, India is attempting to bridge the critical gaps that separate it from the global AI frontrunners.

India’s announcement of its ambitious AI plans, including the development of its indigenous AI model and the ₹20,000 crore ($2.4 billion) funding for research initiatives, highlights the country’s determination to establish itself as an AI powerhouse. But as exciting as these developments are, India’s journey to AI leadership faces considerable challenges that cannot be overlooked. AI development in India faces persistent challenges, including limited access to high-quality data, a shortage of skilled professionals, privacy and security concerns, and a lack of clear regulatory frameworks.

The Talent Deficit: A Skills Gap in AI Education

India’s engineering colleges churn out over 1.5 million graduates annually, giving the nation a vast reservoir of potential talent. Yet when it comes to AI, the gap between demand and supply of skilled professionals is alarmingly wide. At present, India has only about 416,000 AI professionals, a far cry from the estimated 1.3 to 1.5 million experts required by 2026 to keep pace with the demands of the industry. 

This shortage of talent is one of the major bottlenecks hindering India’s AI ambitions.

The talent deficit goes beyond just numbers. While Indian engineering students are highly capable, they often graduate with limited exposure to hands-on AI development. The curriculum at many of India’s thousands of engineering colleges is outdated and not aligned with the rapid advancements in AI technology. 

The shortage of experienced faculty further exacerbates this issue, leaving students with theoretical knowledge but little practical experience in deep learning, natural language processing, and computer vision.

This talent gap isn’t just a challenge for students—it is an existential threat to India’s AI progress. Young graduates who wish to pursue careers in AI often find themselves having to spend years catching up, acquiring additional certifications or self-learning in order to meet the industry’s practical needs. The situation has made it difficult for the domestic industry to keep up with the fast-paced global AI race, as companies battle for the limited pool of AI professionals, pushing up salaries and making it harder for businesses to remain competitive.

India’s education system needs a drastic overhaul to equip students with the skills needed for the future. 

The emphasis should be on practical AI applications, hands-on projects, and a curriculum that evolves alongside the rapid pace of AI advancements. The country must also focus on lifelong learning and upskilling for its existing workforce, allowing professionals to remain competitive and contribute to the AI revolution.

Data and Infrastructure: The Hidden Hurdles

India’s digital transformation has been nothing short of massive. The country produces an enormous amount of data—estimated at 2.5 quintillion bytes daily—fueling the growth of a thriving digital ecosystem. However, the vast majority of this data remains untapped, due to the lack of standardized, structured, and accessible data across sectors. Without sufficient data, AI development becomes stunted, and India’s AI aspirations could remain an unattainable dream.

One of the biggest issues lies in India’s linguistic diversity. The country is home to over 22 official languages, with hundreds of dialects spoken across the country. AI systems, which are predominantly trained on English-language datasets, struggle to cater to regional languages such as Malayalam, Marathi, Bengali, or Tamil. Despite the wealth of data generated by millions of citizens in regional languages, only 15% of existing datasets capture this linguistic diversity. This leaves AI systems ill-equipped to serve the majority of the population, who are often left out of the AI-driven technological leap.

Moreover, key sectors such as healthcare, education, and agriculture operate in siloed data environments, often using incompatible formats and systems. In healthcare, for instance, patient records are stored in varying formats, with many still kept in paper form, making it difficult for AI systems to process and analyze the data. In agriculture, crop data is often collected manually and lacks standardization, limiting the potential for AI-based solutions to improve productivity.

Similarly, educational data is fragmented across multiple platforms, with no central repository or unified system for tracking student progress.

This lack of standardized and accessible data is a huge barrier to developing AI systems that can benefit Indian citizens. To bridge this gap, the government must focus on creating a unified data infrastructure that aggregates data from various sectors and ensures that it is accessible, interoperable, and standardized. Apart from all this, India’s vast linguistic diversity must be embraced by creating AI datasets that accurately represent the linguistic and cultural variations of the population.

Research and Development: The Funding Shortfall

While India has made impressive strides in applied AI research, there is a glaring gap in fundamental research that drives technological innovation. At present, India invests just 0.7% of its GDP in research and development (R&D)—a far cry from China’s 2.4% and the United States’ 3.5%. This funding disparity has profound implications for the country’s ability to develop its own AI technologies and compete with the global tech giants. India’s research output, while valuable, is limited. The country files around 5,000 AI-related patents annually, but this pales in comparison to China’s 110,000+ and the United States’ 60,000+ patents. 

The gap in R&D investment directly impacts the country’s ability to innovate and contribute to foundational AI research, such as developing new algorithms or designing cutting-edge AI architectures. Many young researchers, faced with limited resources and opportunities in India, often look abroad for better prospects, further contributing to the brain drain.

To become a leader in AI, India must significantly increase its investment in R&D. This involves not only government funding but also incentives for private-sector innovation and a more robust partnership between academia, research institutions, and industry. Without such investments, India risks falling behind in the global AI race.

Computing Power: The Unseen Barrier

AI, particularly machine learning and deep learning, demands enormous computational power. 

However, India’s access to high-performance computing (HPC) resources is woefully inadequate. At present, the country has approximately 15 petaflops of AI-dedicated computing power—far below China’s 500 petaflops and the U.S.’s 1,500 petaflops. The lack of computational resources severely hampers India’s ability to train large-scale AI models that require immense processing power.

While the government has announced plans to build the National AI Computing Platform with 100 petaflops of capacity, this is still far from sufficient to match the computing power of global leaders. Smaller startups and research institutions are particularly disadvantaged, as they often cannot afford the high costs of cloud-based AI services, which can be up to 40% more expensive than in other countries.

To support the growth of AI, India must invest in high-performance computing infrastructure that is accessible to all researchers and companies, regardless of size. This would level the playing field and allow India’s AI ecosystem to thrive.

A Call to Action

India’s journey to AI leadership is not a sprint but a marathon. The country has the demographic advantage, the tech talent, and the ambition to rise as a global AI leader, but it must first address the critical gaps that stand in its way.

The government’s initiatives, such as the “AI Skill India” mission to train 500,000 specialists by 2027 and the proposed National AI Computing Platform, are a step in the right direction. But these efforts must be complemented by deeper institutional reforms, investment in research and infrastructure, and the creation of a robust data ecosystem that captures India’s linguistic and cultural diversity.

India’s AI revolution will only succeed if the country can cultivate an AI-ready workforce, provide accessible data for AI systems, and invest in the computing power needed to innovate at scale. With strategic focus, collaboration between sectors, and a clear commitment to innovation, India can bridge these gaps and lead the way in the global AI race. The clock is ticking, and the time for India to act is now.