Assessed the agricultural ecosystem and policy landscape
- Mapped existing digital infrastructure across multiple farmer-facing platforms collectively serving 2+ crore registered farmers.
- Identified key structural challenges: fragmented landholdings, post-harvest losses, digital divide, and platform interoperability gaps.
- Benchmarked against proven national precedents from other Indian states with demonstrated AI-in-agriculture success.
Designed the AI adoption strategy and use case framework
- Mapped AI and digital opportunities across all 8 stages of the agricultural value chain, from pre-season planning and precision irrigation to post-harvest storage and market linkage.
- Prioritised high-impact use cases: AI crop advisory engines, IoT-based pest surveillance, precision irrigation scheduling, and blockchain-enabled supply chain traceability.
- Defined a Shared Digital Public Infrastructure (DPI) model including an Agriculture Data Exchange and AgriTech Sandbox to break public-private data silos.
Built the phased implementation roadmap
- Structured a 3-phase roadmap: foundational data convergence and command-and-control centre setup; scaled IoT and AI deployment across thousands of villages; and predictive AI and innovation hub establishment.
- Defined governance framework, stakeholder roles, and 30+ measurable success KPIs aligned to national public sector agricultural policy priorities.
- Developed the complete concept note, the client's primary tender submission, covering current state assessment, transformation framework, value chain analysis, and risk mitigation strategies.