Transforming India with AI

India’s ₹10,371.92 Crore Gamble on Artificial Intelligence

On March 2024, the government launched the India AI Mission, committing ₹10,371.92 crore over five years with a vision of "Making AI in India and Making AI Work for India." Two years later, the country finds itself ranked third globally in Artificial Intelligence competitiveness, as per Stanford University’s 2025 Global AI Vibrancy Tool. This position, while impressive, raises a pertinent question: is India’s AI ecosystem creating transformative change or merely flexing policy ambition?

Why India’s AI Vision Marks a Break from the Pattern

The India AI Mission is not just another "digital India" narrative. It signifies institutional thinking far more ambitious than previous flagship tech programs like Aadhaar or Digital India. Unlike these initiatives, which focused primarily on connectivity and identification, the Mission emphasizes sovereignty in AI innovation. Platforms such as BharatGen AI, India’s first government-funded multimodal large language model, are designed to support 22 Indian languages, explicitly preventing dependency on foreign AI tools.

Moreover, the expansion of AI computing infrastructure from the planned availability of 10,000 GPUs to 38,000 GPUs demonstrates flexibility and scale—a rare feature in Indian policymaking. Similarly, Bhashini, the language AI platform, goes beyond access; it serves multilingual governance by delivering state services directly in citizens’ languages, bypassing traditional linguistic barriers prevalent in India’s bureaucracy.

What makes this vision different is its approach to inclusivity: programs like YUVAi are integrating AI education for students from Class VI onward, weaving AI literacy into the foundation of India’s education system. In many ways, this is not just technological evolution; it’s attempting to redefine societal access to it.

The Machinery Steering India’s AI Ambitions

If one scrutinizes the institutional backbone, the Mission relies heavily on the India AI Competency Framework, which trains government officials to apply AI in governance. Concurrently, partnerships such as Sarvam AI with UIDAI ensure AI integration in essential public services, like facial recognition for Aadhaar updates. However, this reliance on UIDAI raises privacy concerns given previous data breach controversies.

At the forefront are specialised Centres of Excellence focusing on healthcare, agriculture, sustainable cities, and education. Alongside these are National Centres of Excellence for Skilling, developed to prepare India’s workforce for AI-dominated global industries. These institutions function under targeted funding provided by the India AI Mission.

The legality and ethics of AI deployment remain underregulated, particularly in domains involving facial recognition and predictive policing. Despite repeated calls for an overarching law governing Artificial Intelligence systems, legislation remains fragmented—scattered across cybersecurity frameworks and IT Acts but devoid of comprehensive AI-specific safeguards.

The Data Tells a Mixed Story

One of the Mission’s achievements lies in employment figures: India already employs over 6 million directly in the tech ecosystem, with the AI-specific workforce projected to grow to 12.5 lakh professionals by 2027. This points toward a CAGR of 15%, indicative of momentum. That said, such statistics can be misleading. According to IndiaAI itself, affordability issues and uneven data quality remain severe bottlenecks for rural AI adoption.

Sectoral progress varies dramatically. While industrial AI maturity has reached global benchmarks, evidenced by a Boston Consulting Group (BCG) survey finding 26% of Indian firms scaling AI adeptly, areas such as governance-driven AI lag. For instance, the e-Courts project’s AI-infused case management system has struggled with implementation in district courts where digitisation itself is incomplete.

In agriculture, initiatives like Kisan e-Mitra use AI tools for pest detection and crop health monitoring, yet reports from states like Bihar and Odisha reveal insufficient reach caused by infrastructural gaps. Ironically, programs explicitly designed for farming communities often fail to penetrate beyond agrarian elite networks.

The Uncomfortable Questions No One’s Asking

The optimism around India's AI leadership conveniently bypasses civil liberty concerns. Take facial recognition policing: while touted as "efficient governance," it risks perpetuating unchecked surveillance without robust accountability frameworks. Additionally, unregulated AI systems have been accused of replicating biases, particularly in credit scoring and recruitment algorithms—issues flagged even within NITI Aayog’s reports.

Another fundamental gap lies in uneven state-level implementation. The India AI Mission assumes uniform infrastructural readiness across states. But consider regions like the Northeast, which often face delays due to geographical challenges. Such disparities dilute the impact of initiatives meant to be nationwide.

Global partnerships also remain double-edged swords. While India benefits from collaborations like HealthAI for ethical usage, its sovereignty ambitions—through platforms like BharatGen AI—struggle against dependencies on imported chipsets dominated by entities like Nvidia. It’s unclear whether national self-reliance will survive these geopolitical entanglements.

A Pointed International Comparison: South Korea’s AI Focus

South Korea presents a sharp contrast. In 2018, its government designated AI as a national strategic technology with structured budgets and merged public-private efforts under a single umbrella plan. This coordination supported breakthroughs in AI-powered healthcare, specifically in predictive diagnostics for heart conditions—a niche India’s fragmented rural health AI systems have barely touched. The state-led approach avoided regulatory ambiguity by introducing clear guidelines as early as the strategy launch. India appears reactive by comparison, addressing issues like bias, ethics, and privacy only after implementation challenges arise.

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