AI democratisation in India: Amid the resilient global growth outlook projected in the IMF’s January 2026 World Economic Outlook update, India’s growth is expected to slow from 7.3% in 2025 to 6.4% in 2026 and 2027. The Economic Survey 2025-26 was more optimistic, projecting 6.8-7.2% growth in 2026-27 despite trade tensions, policy uncertainty, and regional conflict. The latest Q3 2025-26 GDP data from the Ministry of Statistics and Programme Implementation, released on February 27, 2026, shows why caution is warranted.
Export growth slowed to 5.6% in Q3 2025-26 from 10.5% a year earlier and 10.2% in the previous quarter, with global trade friction, especially involving the United States, weighing on momentum. Import growth rose to 8.6% from 2.9% a year earlier, reflecting external instability and fresh pressure on the trade balance. That has implications for the current account as well.
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AI productivity gains and economic growth
This softer growth trajectory sharpens an old policy question: how should the state support productivity growth when external conditions deteriorate?
The neoclassical growth model, associated with Robert Solow, explains long-term growth through capital accumulation, labour-force expansion, and technological progress. But once diminishing returns set in, adding more capital does less work. Per capita output then depends less on accumulation and more on productivity. That shifts the focus to technological change, research and development, innovation incentives, and knowledge spillovers.
Artificial intelligence sits squarely in that productivity debate. In India, it can raise the efficiency of both capital and labour. But that will depend on democratisation in the real sense of the term: access, affordability, usability, and diffusion beyond a narrow set of firms and consumers.
AI is relevant across agriculture, manufacturing, and services. The Q3 2025-26 GDP data underlines the need for that spread. Growth in the primary sector remained weak, especially in agriculture. That is where the productivity gap remains large. AI can help address water stress, improve crop management, support rural incomes, raise yields, and manage soil degradation. In manufacturing and services, it can improve production processes, quality control, logistics, operations, routine task automation, and customer-facing services. The economic case is straightforward. India needs broader productivity gains, not islands of efficiency.
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India’s labour market and AI inclusion
The labour-market implications are central. India is not constrained by a shortage of workers. It is constrained by low productivity, uneven skills, and weak access to productive tools. AI democratisation matters in that context because it can narrow productivity gaps if widely diffused, or widen them if it remains confined to the formal sector.
That risk is already visible in policy design. AI policy in India is often discussed through the lens of the organised economy, large firms, formal employment, and advanced digital infrastructure. The informal economy gets far less attention. Yet gig workers, daily-wage earners, artisans, and agricultural labourers account for a large share of India’s workforce.
Without coordinated policy on skills, digital access, and social security, AI adoption could deepen inequality rather than reduce it. That makes the informal sector impossible to ignore. Around 490 million people work outside formal labour-market protections. For them, productivity gains will require more than access to apps. They need credit, tools, connectivity, training, and institutional support.
Easier access to microfinance and credit can help workers and small firms buy equipment, improve skills, expand output, and move into higher-value activity. Initiatives such as the National Mission: Digital Shramsetu, backed by industry, academia, and civil society, could support education, healthcare, and skill development. That would do more than raise incomes. It would weaken the cycle of low productivity and poverty.
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India’s low AI adoption despite digital scale
The paradox is that India has the digital base to move faster. Yet diffusion remains weak.
The AI Diffusion Report 2025 published by the Microsoft AI Economy Institute ranked India 64th in the world in AI adoption. This is despite the country having about 958 million active internet users, according to the Internet in India Report 2025, and one of the world’s largest smartphone markets. The gap between digital reach and AI adoption is too wide to miss. India has scale, but not yet depth.
That gap points to the next stage of the policy challenge. The issue is no longer basic digital access alone. It is the ability to convert connectivity into capability. Internet access does not automatically translate into AI use. Smartphones do not by themselves create productivity gains. Diffusion needs institutions, incentives, and public policy that reaches smaller firms and poorer workers.
Risks of AI monopoly and digital inequality
The costs of failure are large. One risk is digital monopoly, with a few firms controlling the technology stack and capturing most of the gains. That would concentrate productivity growth in a small cluster of technology companies while reducing competitive space for smaller firms. It would also weaken incentives for broad-based innovation.
Another risk is a sharper rural-urban divide. Unequal access to connectivity, cloud infrastructure, digital literacy, and AI tools could widen existing skill and income gaps. Low income households would then face exclusion not only from jobs, but also from credit and market access. The result would be the underuse of labour in an economy that can least afford it.
India cannot afford to miss the AI bus. But the larger point is this: catching it will require more than celebrating frontier technology. It will require policy that spreads capability across farms, workshops, small firms, and informal workers. Without that, AI will remain a story of technological promise and limited economic payoff.
Chinmay Joshi is a Research Associate at the Economics and Policy Area at Bhavans’ SP Jain Institute of Management and Research (SPJIMR), Mumbai and a Research Scholar at Gokhale Institute of Politics and Economics (GIPE), Pune.