Ms. Rubal Chib
India’s conversation on artificial intelligence is maturing. The focus isshifting from abstract capability to applied impact; from what AI can do in theory to what it can solve in practice. Few sectors make this distinction clearer than food and agriculture, where inefficiencies are not marginal but systemic, and where technology must work within deeply local realities of climate, logistics, and markets.
Food systems illustrate the problem starkly. India grows more than enough to feed its population, yet an estimated 68 million tonnes of food are lost every year, with 35–40% of fruits and vegetables perishing after harvest due to breakdowns due to irregular delivery, poor quality assessment, and fragmented supply chains . The crisis is not one of production, but of preventable loss. This loss is not the result of scarcity, but of misalignment—between harvest and consumption, between data and decision-making. For years, the absence of reliable, real-time information about quality and timing have posed a challenge for Indian farmers and regulators. Artificial intelligence, when designed for Indian conditions, offers a way to close this gap.
This gap between biological reality and economic decision-making is where applied AI can deliver disproportionate impact. At qZense Labs, our work began with a simple observation: while India’s digital infrastructure has transformed payments, identity and service delivery, the food economy remains largely analogue in how it assesses quality. QScan, the AI-powered sensing system we developed, uses infrared spectroscopy and artificial olfaction to capture internal quality signals in fruits and vegetables and translate them into usable insights at the point of decision . The intent was not to introduce automation for its own sake, but to reduce uncertainty for consumers, farmers, and retailers operating on thin margins.
What this experience has underscored is that AI’s effectiveness depends fundamentally on context. Models trained on foreign datasets or standardised supply chains struggle in India’s heterogeneous conditions, where crop varieties, climates, storage practices and market structures vary sharply across regions. In food systems especially, accuracy cannot be separated from locality. AI that does not understand Indian produce, Indian logistics and Indian behavioural norms risks being irrelevant at best, and misleading at worst.
This is why the current policy emphasis on sovereign, domestically grounded AI is both timely and necessary. The IndiaAI Mission signals a clear shift in how the state is approaching artificial intelligence- not as a frontier technology divorced from everyday realities, but as developmental infrastructure. Through targeted funding for indigenous AI applications, investments in national compute capacity, support for Indian datasets, and programmes to incubate sector-specific solutions, the Mission is actively building an ecosystem where innovation is anchored in local needs. By backing homegrown models, startups and public-interest deployments, it is creating space for AI systems designed for Indian conditions rather than retrofitted from other geographies.
Food and agriculture illustrate why this matters. Reducing post-harvest losses has a direct bearing on farmer incomes, food prices and environmental outcomes. Every unit of produce saved reduces pressure on land, water and energy resources, aligning naturally with India’s commitments on climate action and responsible consumption. Unlike yield-enhancing interventions, which often require behavioural change or new inputs, better quality assessment improves outcomes by improving coordination—helping the system act at the right time.
Equally important is the question of inclusion. For AI to serve the public good, it must be usable by small actors, not just large enterprises. In India’s agricultural economy, that means tools that work within informal markets, support local languages, and complement human judgement rather than override it. Technologies that impose opaque recommendations will struggle to earn trust. Those that augment decision-making, by making invisible information visible, are more likely to scale organically.
The India AI Impact Summit, which will take place from 16-20th February 2026 in our capital city, arrives at a moment when these distinctions are becoming clearer. Globally, AI discourse is grappling with concentration of power, data asymmetries and environmental costs. India’s opportunity lies in demonstrating a different pathway, one where intelligence is decentralised, context-aware and embedded within real economic systems. Agriculture, food logistics and climate adaptation are not edge cases in this story; they are central to it.
It is worthwhile to consider that AI does not automatically create impact. It demands deep domain understanding, patience with complex policy realities and close alignment with public institutions. When these conditions are met, however, AI can quietly transform sectors that have long resisted reform. Not through disruption, but through better decisions made earlier.
As the Impact Summit approaches, the task before policymakers, technologists and entrepreneurs is not to accelerate AI adoption indiscriminately, but to deepen it thoughtfully. Success should be measured not by model benchmarks, but by whether intelligence helps reduce waste, stabilise incomes and strengthen resilience. In food systems, as in many areas of India’s development journey, AI’s greatest contribution may simply be helping us act in time.
(The author is co-founder of QZense Labs Private Limited)
