AI will become part of our operating layer

Over the next 2-5 years, AI will become part of the operating layer of our work rather than a standalone initiative. In agriculture, volatility is often driven by fragmented information and timing gaps across storage, finance and markets. We expect AI to strengthen how decisions are made within that system. If price signals, storage costs, repayment patterns and local demand movements can be analysed continuously, decisions around selling, holding inventory or extending credit become more informed. The outcome we are working towards is lower uncertainty and greater income stability across the value chain.

In practical terms, AI will sit beneath processes we already run. We conduct structured quality checks and collateral assessments across a distributed storage network. AI can help make those processes more consistent across geographies and generate clearer digital audit trails. In lending, underwriting models can incorporate commodity behaviour, repayment history and storage cycles so that risk is calibrated dynamically rather than assessed through static documentation alone. This is particularly relevant as more first-time borrowers enter formal credit systems.




The tasks we most want AI to handle are those where scale creates operational strain. Monitoring thousands of storage points simultaneously. Detecting deviations in quality of storage conditions before they escalate. Underwriting high volumes of small-ticket, commodity-backed loans without slowing down decision cycles. Analysing district-level price data to indicate whether holding stock is economically rational after accounting for carrying costs. Matching supply with buyer specifications based on grade, quantity and location. These are data-intensive functions where pattern recognition adds measurable value.

A key concern, however, is the cost of accessing and using this AI-driven data. Clarity is needed on who bears the cost: farmers, government, or platform providers. Adoption will ultimately depend on affordability and measurable return on investment for farmers.

Must remain grounded in agricultural realities

Looking ahead, AI systems must remain grounded in agricultural realities. Crop harvest windows vary by region. Mandi volumes move with regional harvest patterns, and rural liquidity tightens or eases over the crop cycle. Models must incorporate these variables and communicate recommendations clearly. When a credit decision is taken or a risk is flagged, the reasoning should be visible and understandable. Adoption depends on trust. If AI can reduce risk at the farmgate, improve capital flow efficiency and strengthen formal participation without adding complexity, it will have made a meaningful contribution to the agricultural ecosystem.