KissanAI is a generative AI platform redefining how agricultural intelligence reaches farmers and agribusinesses worldwide. It deploys advanced, multilingual, voice-enabled AI to deliver real-time, personalized guidance on crop management, pest control, irrigation, and sustainable practices. By eliminating literacy and language barriers, the platform ensures inclusive access to expert knowledge. Its flagship AgriCopilot, along with specialized models such as Dhenu, provides context-aware, locally relevant recommendations. By integrating large language models with deep domain expertise, KissanAI democratizes agricultural intelligence and accelerates productivity, resilience, and innovation across the agri-value chain.
In an exclusive conversation with The Interview World at the World Futures Forum, organized by India SME Forum in partnership with WeDO, Dr. Pratik Desai, Founder and CEO of KissanAI, articulates the structural inefficiencies within the agricultural value chain and explains how his platform addresses them. He evaluates the readiness of Indian farmers to adopt advanced technologies and outlines the tangible benefits of such adoption. Furthermore, he examines the systemic barriers to modernizing Indian agriculture and critically assesses India’s AI trajectory, identifying the forces that will shape its near-term evolution. The following are the principal insights from this substantive discussion.
Q: What specific problem in the agriculture value chain is KissanAI’s product designed to solve?
A: We are actively deploying our solution with large agricultural enterprises and are now launching a scalable platform tailored for MSMEs. The opportunity is clear: most agribusinesses lack advanced IT infrastructure, and even when they possess it, technology is not their core strength. AI adoption requires capital, expertise, and operational readiness, resources that many enterprises cannot easily mobilize. Our platform removes these barriers.
Agricultural companies aiming to expand their reach face a structural limitation to scale. In farmer interactions, nearly 80% of queries are repetitive: product usage, dosage, pricing, availability, or basic troubleshooting. Yet organizations continue to allocate trained personnel to answer each inquiry manually. This model constrains growth, increases costs, and limits responsiveness.
Our AI platform changes that dynamic. By automating up to 80% of routine queries, enterprises can redeploy experts to focus on complex, high-value issues. AI handles standard interactions instantly and consistently, while human specialists address nuanced agronomic or product-specific concerns. This hybrid model improves service quality, accelerates response times, and significantly enhances operational efficiency.
Beyond conversational automation, the platform functions as a modular AI ecosystem. It enables agribusinesses to activate integrated e-commerce, provide real-time support, capture customer intent, and manage grievances through intelligent agents. Businesses can selectively enable features aligned with their strategy, eliminating the need for fragmented systems or heavy upfront investment.
Language scalability further strengthens expansion. Agricultural markets are inherently multilingual, and entering new regions traditionally demands local hiring and content redevelopment. Our platform supports over 22 languages, allowing enterprises to centralize knowledge and deploy it seamlessly across geographies. A single knowledge base can power farmer engagement in multiple languages without duplicating infrastructure.
In essence, we transform AI from a complex investment into a practical growth engine, empowering agribusinesses to scale efficiently, expand regionally, and deliver superior farmer engagement at lower operational cost.
Q: What is the current readiness level of Indian farmers to adopt advanced agricultural technologies, and will such adoption benefit them?
A: When we began, we confronted a structural imbalance. India has roughly 160 agricultural universities, yet it serves nearly 150 million farmers. The disparity is stark. No system with that ratio can deliver timely, individualized, high-quality advisory support at scale. Moreover, universities, extension services, and KVKs simply do not have enough trained personnel to meet the demand. As a result, farmers turn to the most accessible source, the local retailer, a neighbor, or a known intermediary.
However, this dependency introduces bias and information asymmetry. Retailers often lack exposure to the latest agronomic research. In many cases, commercial incentives influence their recommendations. Consequently, farmers make decisions based on incomplete or skewed guidance.
We address this gap by providing farmers with an additional, independent source of intelligence. We do not aim to replace retailers overnight; that would be unrealistic. Instead, we equip farmers with informed alternatives. When a retailer recommends a product, the farmer can now validate that advice against AI-driven insights. This shift creates informed dialogue rather than blind acceptance. Empowerment begins with optionality.
Furthermore, AI delivers continuous support. Unlike traditional systems, it operates 24/7. If a farmer encounters a problem with a crop or product, immediate guidance becomes available. Previously, delayed advice could result in crop loss. Now farmers can ask what to use, when to use it, and how to apply it—at any time. This immediacy reduces risk and improves decision precision. Farmers no longer operate in isolation; they operate with accessible expertise.
Adoption has evolved. Early results were modest because models and datasets lacked maturity. Today, both have significantly improved. More importantly, trust has strengthened—not directly through us, but through the enterprises we partner with. These companies validate the system through their agronomists and sales teams before introducing it to farmers under their own brand. Because farmers already trust those companies, they engage openly and provide candid feedback.
Accordingly, we are prioritizing a B2B model. Rather than offering standalone advisory, we integrate AI into enterprise ecosystems where recommendations translate into actionable outcomes—product purchases, service requests, and measurable field impact.
Q: What are the core challenges to modernizing Indian agriculture?
A: Indian agriculture faces a fundamental structural constraint: land fragmentation. Farm holdings are small, and they continue to shrink. This trend directly limits productivity and technology adoption.
The pattern is systemic. As population increases, land parcels divide across generations. When a landowner passes away, holdings are split among heirs. Consequently, plots become progressively smaller. Over time, this fragmentation creates operational inefficiencies and restricts scale.
Small plots cannot economically support large-scale mechanization. Advanced harvesters, precision spraying systems, and modern tractors require scale to generate viable returns. On fragmented land, utilization rates fall, and capital efficiency collapses. As a result, farmers cannot justify the investment.
This dynamic creates a negative cycle. Smaller farms produce lower aggregate output. Lower output constrains income. Constrained income limits reinvestment capacity. Without reinvestment, farmers cannot adopt modern equipment, improved inputs, or advanced practices. Consequently, yields stagnate or decline relative to global benchmarks.
Moreover, capital-intensive technologies assume economies of scale. When average landholding size decreases, per-acre mechanization costs increase disproportionately. For smallholders, the payback period becomes too long and financially risky. Therefore, adoption remains low, not because of resistance to innovation, but because of structural infeasibility.
This fragmentation-driven constraint represents one of the most significant bottlenecks in Indian agriculture today. Unless technology models adapt to smallholder economics, through shared infrastructure, service-based models, or scalable digital solutions, productivity gains will remain limited.
In short, shrinking landholdings suppress income growth, restrict capital formation, and inhibit modernization. Addressing this structural imbalance is essential to unlocking higher yields and sustainable agricultural advancement.
Q: How do you assess India’s AI journey to date, and what will shape its evolution in the near term?
A: I hold two competing views on India’s position in AI.
First, we lag significantly behind global leaders. When we compare ourselves to innovation hubs such as OpenAI, Google, Anthropic, DeepSeek, or Meta, the disparity in capital and compute becomes undeniable. India may highlight access to roughly 40,000 GPUs; however, Meta alone operates at a scale approaching 700,000 GPUs. Similarly, Google’s annual capital expenditure reaches approximately $180 billion, comparable to the fiscal scale of entire national budgets. These investments compound. Larger capital pools produce more powerful base models. Stronger base models accelerate downstream innovation. The gap, therefore, widens structurally.
This imbalance has direct labour-market implications. AI increasingly automates software development, testing, support, and routine application engineering. Consequently, white-collar roles tied to IT services and outsourcing face structural risk. For decades, global firms offloaded non-core software functions to India. Now, those same functions can be automated domestically through AI. The traditional outsourcing advantage may erode rapidly.
Yet this is only one side of the equation.
India retains a significant opportunity in sectors that remain under-modernized, manufacturing, agriculture, food processing, and export-oriented production. These industries still rely heavily on manual compliance, fragmented documentation, and inefficient processes. Here, AI can serve as an equalizer rather than a disruptor.
For example, AI can guide farmers through export licensing, ensure adherence to quality protocols, and standardize agronomic practices. It can monitor food-processing plants through image-based inspection systems, verify sanitation compliance, and streamline regulatory approvals. In doing so, AI elevates operational standards to global benchmarks.
Therefore, while high-paying software service exports may contract, AI-enabled modernization of physical industries can expand. If executed strategically, this shift can increase productivity, improve quality, enhance export competitiveness, and strengthen the broader economy. The challenge is clear: prepare for disruption in services while aggressively leveraging AI to modernize the real economy.
