Future Farming in India A Playbook for Scaling Artificial Intelligence in Agriculture 2025

Develop a strategy Governments – federal and provincial – can best begin their AI journey by developing an AI strategy appropriate for the promotion of responsible AI. The strategy should reflect the way AI is used given the needs, resources and capabilities of that geographical area. Examples are the AI strategies of Singapore,22 the UK23 and Canada,24 and the UP Digital Agriculture policy.25 Deploy contextual policies The strategy must be closely followed by a set of clear and achievable policies, including those relating to procurement of AI solutions by public agencies, financial incentives and responsible data- sharing by public agencies. Without these policies there is uncertainty and indecision. 1. Enabling procurement policies and market access: Developing clear guidelines for the procurement of AI solutions by government agencies can streamline the adoption process. AI Procurement in a Box guidelines on government AI procurement (published by the World Economic Forum) provide guidance for public-sector agencies on procuring AI solutions. 2. Financial incentives: Offering financial incentives and support to farmers through cooperatives can encourage the adoption of AI technologies. Implementing the digital payment solution e-RUPI26 can provide farmers with vouchers specifically earmarked for procuring AI services validated by AI sandboxes. Examples include the National Health Authority (NHA) using prepaid e-vouchers for healthcare. The Saagu Baagu 2.0 project in Telangana provides farmer cooperatives with financial support to implement AI solutions. 3. Robust digital public infrastructure and data-sharing policies: This is foundational for the integration and scaling of AI solutions in agriculture. Centralizing agriculture-related data on a secure and accessible platform provides a backbone for all stakeholders in the AI ecosystem. a. Data platforms and exchanges: Aggregating agriculture-related data (landholding crop data, soil health, weather patterns, market prices and more) will enable AI developers to create more precise and reliable models, reducing data silos. The Agri Stack27 initiative from the government of India and the Agricultural Data Exchange (ADeX)28 from the government of Telangana are examples. b. Data-sharing policies: DEPA (Data Empowerment and Protection Architecture)29 and the Telangana ADMF (Agricultural Data Management Framework)30 – drafted in collaboration with the World Economic Forum – are public–private efforts designed for data protection and to ensure data empowerment by facilitating smooth and secure data flow, enabling AI. Promote responsible AI Governments do well to initiate discussion on developing a “progressive” regulation on AI to balance the positive and negative externalities of AI solutions. MeiTY, India has launched an initiative to build tools and frameworks31 to promote the ethical development and deployment of AI across different sectors. EnableAI strategy roadmap Policies Governance (responsible AI)Procurement frameworks Financial incentives Data-exchange platforms Data-sharing policiesThe enable stream forms the fertile soil for AI to grow, flourish and bear fruits. It creates an environment conducive to the innovation of AI solutions and their deployment at scale – whether at national or state scale. While a few other enablers exist, the model identifies the major ones.Enable Pillar 1 Enable pillar of the IMPACT AI framework FIGURE 13 The enable stream forms the fertile soil for AI to grow, flourish and bear fruits. Future Farming in India 32