AI dairy farming has found its most ambitious deployment yet \u2013 not in a Silicon Valley lab nor a European agri-tech campus, but in the villages of Gujarat, India, where 36 lakh (3.6 million) women milk producers are now being served by an AI assistant named Sarlaben.
\nAmul, the world\u2019s largest dairy cooperative, has launched what it calls Amul AI: a platform built on five decades of cooperative data, designed to give every farmer in its network round-the-clock, personalised guidance in their own language.
\nAmul was launched just ahead of India\u2019s AI Impact Summit 2026 and backed by the Ministry of Electronics and Information Technology (MeitY) with the EkStep Foundation. It is a test case for whether AI \u2013 the kind being debated in boardrooms and policy forums globally \u2013 can actually reach the last mile.
\nMeet Sarlaben: The AI dairy farming assistant
\nSarlaben draws from one of India\u2019s most comprehensive agricultural data repositories. It\u2019s accessible via the Amul Farmer mobile app \u2013 already downloaded by over 10 lakh (one million) users on Android and iOS \u2013 as well as through voice calls for farmers using feature phones or landlines.
\nThe system is integrated with Amul\u2019s Automatic Milk Collection System (AMCS) and the Pashudhan application, allowing it to offer personalised, cattle-specific guidance.
\nWhat makes Amul AI substantially different from most agricultural chatbots is the scale of its training data. The platform was built on a digital backbone managing over 200 crore (two billion) milk procurement transactions annually, veterinary treatment records from more than 1,200 doctors covering nearly 3 crore (30 million) cattle, approximately 70 lakh (seven million) artificial inseminations conducted each year, ISRO satellite imagery for fodder production mapping, and a cattle census conducted every five years.
\nEvery animal in the system carries a unique ID, with individual records of feed intake, disease history and milking status. \u201cAmul AI is about taking dependable, verified information directly to the farmer \u2013 instantly and in a language they are comfortable with,\u201d said Jayen Mehta, Managing Director of the Gujarat Cooperative Milk Marketing Federation (GCMMF), which markets the Amul brand.
\nHe said how, by using decades of structured data and integrating it with their operational systems, the platform will help farmers make timely decisions that improve animal productivity and income.
\nIndia\u2019s productivity paradox
\nIndia is the world\u2019s largest producer of milk, generating 347.87 million tonnes in 2024-25 according to the Department of Animal Husbandry and Dairying \u2013 more than double the US\u2019s 102.70 million tonnes. And yet despite leading in volume, India\u2019s per-animal milk yield remains among the lowest globally.
\nThe reasons are structural. India\u2019s dairy sector is characterised by small herd sizes, low-quality feed, limited access to veterinary care in rural areas, and widespread lack of awareness about modern breeding and husbandry practices. Amul\u2019s network spans more than 18,600 villages in Gujarat, where farmers supply over 350 lakh litres (35 million litres) of milk daily.
\nBut information asymmetry has long been a bottleneck \u2013 a farmer facing a sick animal at midnight in a remote village has few places to turn; the gap Amul AI is designed to close.
\nAvailable initially in Gujarati \u2013 the primary language of the cooperative\u2019s farmer base \u2013 the platform is built on the government\u2019s Bhashini multilingual framework and could, in principle, be extended to 20 Indian languages, reaching Amul\u2019s presence in 20,000 villages in 20 states.
\nThe cooperative model
\nThe technology story here is inseparable from the institutional one. Amul\u2019s cooperative structure \u2013 built over five decades under the original White Revolution \u2013 created the data infrastructure that makes Amul AI possible.
\nMost private agri-tech startups are working backwards: collecting data first, building products second. Amul already had the data. What was needed was a way to make it actionable at the farmer level.
\nExperts tracking the dairy-tech space see this as significant. Sreeshankar Nair, Founder of Brainwired, a dairy-tech startup, identifies three specific challenges that Amul AI could meaningfully address: farmer awareness, access to quality veterinary guidance, and connectivity to grazing and feed resources.
\n\u201cIf AI can integrate local dialects of Indian languages, India can have White Revolution 2.0,\u201d Nair said, pointing to the transformative potential of vernacular AI in a sector where not every farmer speaks the same dialect.
\nSaswata Narayan Biswas, Director of the Institute of Rural Management, Anand (IRMA) \u2013 the institution closely associated with Amul\u2019s founding ethos \u2013 frames it as an AI embedded in a cooperative framework. It becomes \u201cnot a technology upgrade, but an instrument of inclusive rural transformation.\u201d
\nFor Biswas, the specific abilities Amul AI brings \u2013 predictive disease detection, oestrus tracking, optimised feed formulation, localised weather risk advisories \u2013 are abilities Amul had been building for years. AI accelerates and democratises them.
\nScale and the test ahead
\nThe launch has drawn backing from the highest levels of government. Gujarat Chief Minister Bhupendra Patel launched the platform and confirmed it will be showcased at the AI Impact Summit 2026. The cooperative has acknowledged MeitY and the EkStep Foundation \u2013 an open digital infrastructure nonprofit \u2013 as partners in building the AI layer.
\nFarmers not affiliated with Amul can also access general dairying and animal husbandry information through the app. At its current scale, Amul AI already covers more cattle \u2013 nearly 3 crore (30 million) \u2013 than most national veterinary databases anywhere in the world.
\nThe harder question, as with most AI deployments at a population scale, is whether the tool will serve those who need it most. The farmers most likely to benefit first \u2013 those already comfortable with smartphones, already plugged into Amul\u2019s digital system \u2013 may not be the ones with the greatest information deficit.
\nThe rollout of Bhashini-enabled dialect support, the adoption rate among feature-phone users relying on voice calls, and whether AI-driven advisories translate into measurable yield improvements will be the metrics that determine whether this is genuinely White Revolution 2.0.
\nAmul has built an AI system grounded in half a century of real cooperative transactions, real animals, and real farmers. Such an infrastructure is, arguably, the most credible foundation for AI dairy farming at scale. Whether it fulfils its promise will depend on execution \u2013 and on whether Sarlaben\u2019s voice can reach in the last few miles; those that have always been the hardest to cross.
\nSee also: Hitachi bets on industrial expertise to win the physical AI race<\/p>\n
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\nThe post How Amul is using AI dairy farming to put 36 million farmers first appeared first on AI News.<\/p>\n","protected":false},"excerpt":{"rendered":"
AI dairy farming has found its most ambitious deployment yet \u2013 not in a Silicon Valley lab nor a European agri-tech campus, but in the villages of Gujarat, India, where 36 lakh (3.6 million) women milk producers are now being served by an AI assistant named Sarlaben. Amul, the world\u2019s largest dairy cooperative, has launched [\u2026]<\/p>\n
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