Mark Philip Jayan | OCTOBER 8, 2026, 4:30 PM
https://analyticsindiamag.com/ai-features/the-case-for-personalising-ai-in-agriculture
Amazon founder Jeff Bezos once famously said, “You can build a business strategy around the things that are stable in time,” implying that an industry cannot base business decisions on things that are constantly changing.
No other industry is more impacted by the ‘change’—or the flow of private markets—than agriculture. At the receiving end of these fluctuations has always been the farmer.
“The price is decided by the market,” said Ravishankar Cherukuri, CEO of UPL Sustainable Agri Solutions, at Cypher 2026—AIM’s flagship AI conference. However, when the Iran war pushed raw material costs up by close to 2x, the farmer’s selling price didn’t move the same way.
“The farmer faces prices which can suddenly shoot up to 2x or suddenly drop down to 20% of what it was last year. The wherewithal required to conduct basic farming is compromised as well,” he noted, explaining that raw material prices, urea, cost of labour, and cost of machinery are highly volatile. “Amalgamated with the volatility of weather changes with abrupt pests and diseases, makes fertile ground for destroying business.”
That’s where artificial intelligence comes in. It can help farmers by forecasting prices, optimising crop selection, and connecting producers with profitable regions of global demand. Moreover, it can help with research and development to make crops resistant. Also ReadCypher 2026 Day 2 Highlights: GCCs Face AI Reckoning, Compute Hits Physical Limits
Cherukuri recalled that agriculture’s relationship with AI started well before the current AI moment. “Ten years ago we were already using AI in R&D,” he said. This was in the form of genomics data, bioinformatics, etc, to design products and genetically modified crops.
In 2010, he helped launch Dr. DEKALB Farm Care that offered custom, tech-enabled, and mobile-based guidance for farmers cultivating the popular DEKALB brand of corn. “We succeeded enormously,” he said, because farmers “lapped it up”.
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The platform delivered relevant information, and adherence to recommendations was “humongously high,” he said, high enough that farmers were ready to pay for it. But the project couldn’t scale up because the cost of personalising advice for each farmer was too high to extend to the roughly 100 million farmers across India.
Farmers often find themselves at the short end of misallocation. What actually improves profitability and reduces risk is a comparable full-system reboot, but what the industry typically hands them is the “agricultural equivalent of that pill”—a pesticide in a bottle.
This is where Cherukuri argued AI changes the math that broke his 2010 platform. The personalisation that was financially impossible to scale to 100 million farmers over a decade ago—tailored “based on our knowledge of what the farmer is doing”—is now within reach. Also ReadAI is Changing Filmmaking, But Vatsal Sheth Believes Human Judgement Will Prevail
“Without AI, it would be prohibitively expensive,” he said. “With AI, doable.”
Similarly, it is playing out in the company’s operations.
When diagnosing a problem, field visits to a single village or a handful of villages may not give the entire picture of the issue, which may be multivariate. They are now digitised down to which village was visited, which farmers attended, and what was discussed.
Combined with product-flow data, his team is building AI-powered algorithms to optimise exactly what happens in which village and when, while using conversational intelligence to “cut the friction” between farmers, dealers, and the company.
“I think we are only at the 10th percentile of this journey, but we are making the investments to take it forward,” he said with pride.