The Indian agriculture sector has for long depended on local knowledge, experience and business acumen when making decisions regarding procurement, pricing, and supply-chain planning. However, the rapid adoption of artificial intelligence is beginning to transform how these decisions are made. Rather than reacting to changing market conditions, enterprises across the agri-food value chain are increasingly using predictive intelligence to anticipate demand, optimise sourcing and improve operational resilience.
As AI moves beyond automation towards enterprise decision intelligence, technologies such as satellite imagery, GIS mapping, voice interfaces and agentic AI are creating new opportunities to connect farmers, farmer producer organisations (FPOs), processors, financial institutions and buyers through a common intelligence layer.
In an exclusive interaction with Express Computer, Milind Borgikar, Co-Founder & CTO, Ayekart, discusses how AI is reshaping India’s agri-commerce ecosystem, why predictive intelligence is becoming central to enterprise decision-making, and what organisations need to build AI that works across Bharat.
AI is changing how agricultural decisions are made
According to Borgikar, experience will continue to play an important role in agriculture, but AI significantly expands the information available for decision-making.
Traditionally, procurement and pricing decisions have depended on local market visibility. AI now enables enterprises to combine historical and real-time pricing, weather conditions, crop intelligence, arrival volumes and policy inputs to generate predictive insights across the supply chain.
He says this fundamentally changes procurement planning, commodity pricing and inventory management by allowing organisations to anticipate market movements rather than reacting after they occur. “Experience and intuition will always matter in agriculture. The difference is that AI expands the field of vision by combining multiple signals to generate forward-looking insights that help organisations plan sourcing, pricing and supply availability much earlier.”
This allows procurement teams to identify optimal sourcing windows, enables farmers and FPOs to improve price discovery, and helps logistics and warehousing providers position inventory ahead of demand instead of responding to shortages.
AI is evolving into infrastructure rather than another application
Borgikar believes enterprise AI should not be viewed as another standalone digital tool.
Most agri-tech platforms have historically addressed individual functions such as marketplaces, logistics or pricing. While these improve specific processes, they often create disconnected systems that require users to manage multiple platforms.
Instead, he argues that AI should function as the intelligence layer connecting sourcing, warehousing, processing, logistics, payments and financing into a continuous operating ecosystem.
“When the same intelligence layer powers every decision across the value chain, AI stops being a feature and becomes infrastructure that continuously improves as the network grows,” he adds.
Rather than existing as another application, AI increasingly becomes the operating system coordinating information across every participant in rural commerce.
Agentic AI is supporting distributed field operations
Borgikar also sees agentic AI transforming enterprise operations across geographically distributed workforces.
Traditional enterprise software often assumes users have time to analyse dashboards and reports. However, field teams working across villages and remote locations require concise, contextual information that helps them act immediately.
According to him, AI should proactively deliver recommendations instead of expecting employees to search for information themselves.
Voice briefings, AI-powered managers and intelligent decision-support systems can guide field teams on procurement priorities, inventory risks and operational exceptions through natural language delivered via familiar communication channels.
He believes this approach enables organisations to improve workforce productivity in three ways: faster decision-making, broader managerial oversight and earlier detection of operational anomalies before they become business losses.
“The objective is not to replace field judgement but to give every field worker access to the equivalent of an analyst, planner and supervisor wherever they operate,” Borgikar points out.
Geospatial intelligence is strengthening supply chain resilience
Satellite imagery and GIS-based crop intelligence are generating unprecedented volumes of agricultural data. However, Borgikar argues that data alone creates little value unless it translates into operational decisions.
The combination of geospatial intelligence and the transactions data of farmers and processors and FPOs will help in forecasting supply in regions and identifying sources of disruption.
Rather than identifying supply shortages after harvesting has been done, organisations will be able to foresee climate risks weeks ahead of time and prepare themselves with sourcing diversification plans.
The same intelligence will also be able to benefit farmers through climate advice and crop suggestions, helping the entire agricultural ecosystem rather than just the enterprise supply chains.
Voice-first AI will decide technology adoption in Bharat.
According to Borgikar, the need for a multilingual and voice-first interface will remain critical as enterprises scale AI to farmers, retailers, and rural business players.
Conventional digital solutions which operate on text and forms and other menu-based interfaces tend to create friction for many of the users.
Integrating voice-first interfaces to popular messaging platforms like WhatsApp will enable the users to communicate in their preferred language without having to learn an entirely different digital workflow.
Voice is expected to be the most important interface for rural business transactions going forward, similar to how UPI did.
“The technology must adapt to the user rather than expecting the user to adapt to the technology,” he asserts.
Trust will drive adoption of AI in agribusiness
Borgikar believes that the greatest contribution of AI would be in fostering trust within India’s agricultural value chain.
Transparency would aid the assessment of the creditworthiness of financial institutions, and it would assist buyers in authenticating product quality and reducing wastage.
However, he points out technology alone will not determine success.
Instead, organisations will differentiate themselves through three capabilities: building AI models trained on real-world agricultural transactions, designing multilingual and low-bandwidth user experiences, and ensuring AI systems remain transparent and accountable to every participant across the ecosystem.
“The organisations that succeed will not necessarily have the most advanced AI models. They will be the ones that build the strongest connection between intelligence and the realities of India’s agricultural ecosystem.”
As enterprise AI expands into agriculture, Borgikar believes long-term value will come from embedding intelligence into day-to-day operations rather than treating AI as another standalone digital capability. For India’s agri-commerce sector, that means building systems capable of connecting every participant from farmers and FPOs to enterprises and financial institutions through trusted, data-driven decision-making.