Unlocking Value in Agricultural Value Chains: How AI-Driven Decision Intelligence Improves Value Realisation Across Stakeholders, with a Tur Dal Pilot in Kalaburagi

India’s agricultural value chains remain fragmented and inefficient, characterised by information asymmetry, weak price discovery, excessive post-harvest losses, and poor coordination among farmers, traders, processors, and retailers. These structural inefficiencies result in a large price spread between farm gate and consumer, with farmers capturing a disproportionately small share of final value while consumers face high and volatile prices. This paper hypothesizes on how artificial intelligence (AI), deployed as a value-chain-wide decision intelligence platform, can significantly improve value realisation for all stakeholders. By integrating soil, weather, crop, price, demand, quality, and logistics data, AI can enable demand-aligned production, transparent quality-based price discovery, reduced processing losses, and optimised distribution. The paper elaborates four dimensions of AI intervention—production, procurement and price discovery, processing and value addition, and distribution and consumption—and illustrates the hypothesis through a tur dal (pigeon pea) pilot in Kalaburagi district, Karnataka. The analysis demonstrates that AI can compress the price spread by 20–25%, reduce processing losses from 10–15% to 9%-11%, increase farmer price realisation, and improve consumer affordability. The paper concludes with policy and institutional implications for scaling AI-enabled value chains in India.