India stands at a remarkable inflection point in its AI journey, no longer dabbling in experimentation, but powering ahead into full-scale adoption. According to an EY–CII report on India’s AI landscape, nearly half (47%) of Indian enterprises now run multiple AI use cases in production, spanning sectors as diverse as banking, insurance, manufacturing, and e-commerce. For a country often cast as a follower in global tech waves, the numbers now tell a different story.
Beyond pilots
Today, it is entirely routine to find an HR team deploying AI to screen résumés or a hospital digitising prescriptions with computer vision. EY’s survey notes that Indian enterprises are embedding AI into core workflows, from fraud analytics in banks to demand forecasting in retail, signalling the shift from novelty to necessity. AI has quietly left the lab and entered the back office, the shop floor, and the call centre.
Chasing speed, not perfection
According to EY’s “Is India ready for Agentic AI? The AIdea of India: Outlook 2026”, India’s executives overwhelmingly value speed: 91% rank rapid deployment as the top factor in buy-versus-build decisions for AI solutions. In an economy where margins are thin and customer volumes immense, the cautious “pilot phase” is a luxury few firms can afford. The same report finds that about 76% of leaders expect GenAI to have a significant impact on their business models, underscoring that AI is seen less as a feature and more as a new operating system for the enterprise.
Agentic AI: the rise of digital interns
EY’s research highlights a particularly rapid shift towards “agentic AI”—AI agents capable of planning and executing multi-step, rules-based tasks with limited human supervision. These systems already answer customer queries, process documents, and summarise meetings, nibbling away at work that once formed the backbone of India’s outsourcing and shared-services economy.
Yet, as EY points out, 64.5% of organisations cite data governance and security as “very severe” constraints when scaling such agents, even as adoption continues because the cost advantages are too compelling to ignore.
Small language models: India’s best bet
Rather than chase gargantuan, trillion-parameter models, Indian firms are increasingly betting on small language models (SLMs)—compact, domain-specific systems tailored to local needs. According to industry analyses and consulting studies, these SLMs offer lower compute costs, better support for Indian languages, and higher accuracy on narrow operational tasks, often running on-device or at the edge. For a country whose problems are logistical, linguistic, and cost-sensitive, this “Bharat-first” AI looks less glamorous than Silicon Valley’s efforts, but far more usable.
Statecraft and silicon: the IndiaAI push
The policy backdrop is equally ambitious. According to government announcements and IndiaAI mission documents, New Delhi is planning a national AI compute grid of roughly 40,000 GPUs, backed by a multi-thousand-crore outlay and complemented by initiatives such as AIKosh (a national datasets platform) and public Indic language models. As with Aadhaar and UPI, the state is once again building shared digital infrastructure that private firms can ride on—creating the conditions for AI to become plumbing rather than ornament.
Value over experiments
EY’s survey suggests that Indian CEOs are far less enamoured of flashy AI demos than of hard-nosed returns. Time saved, productivity gains, cost efficiency, revenue creation, and competitive differentiation form the five pillars on which AI investments are being judged, with many firms funnelling GenAI budgets into operations, customer service, and marketing—functions closest to cash flows. Integration headaches and legacy systems remain real, but they slow rather than stop the march.
Human capital: the reassigned, not the replaced
The EY findings paint a subtler picture of labour markets than the usual tale of mass displacement. Standardised and outsourced roles are being reshaped first, yet many employees are being shifted into higher-value tasks: finance staff spend more time on analysis than reconciliation, and support agents supervise AI-generated responses instead of typing every line. In this emerging division of labour, AI drafts and humans decide—a hybrid that suits India’s mix of ambition and caution.
From pyramids to diamonds
If EY is right, the organisational chart itself may be in for a redesign. The traditional pyramid, with a vast base of junior process workers, could gradually bulge into a diamond, featuring a smaller base, a broad middle of AI-augmented specialists, and the same narrow apex of leadership. That, in turn, would demand new investments in reskilling, domain expertise, and AI governance.
India’s distinctive edge: an adoption culture
The final advantage is cultural. A country that normalised UPI, FASTag, Aadhaar-based eKYC, and GST-linked compliance in short order is no stranger to abrupt shifts in systems. EY’s numbers suggest AI is following a familiar trajectory: while Western boardrooms debate regulation and ethics, many Indian firms ask a more ordinary question: how fast can this be put to work on real problems?
According to EY, India is no longer merely preparing for an AI future; it is already living in an AI-present that is messy, decentralised, and relentlessly pragmatic. The AI revolution may well be conceived in global research labs, but if these trends hold, it could be operationalised at scale in India first.
