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Case Studies // AI-Powered Agriculture: Taking GenAI from Pilot to Field to Maximize Productivity and Profitability
- Which AI use cases (yield forecasting, input optimisation, livestock health monitoring, credit and insurance risk scoring) are currently delivering the clearest commercial impact?
- What are the main barriers to scaling AI systems, from connectivity and infrastructure gaps to cost, data readiness, and model accuracy, and what proof do producers and agribusinesses need before investing?
- What minimum standards must agribusinesses establish to ensure easily accessible and trustworthy GenAI outputs, especially in multilingual and culturally diverse farming regions?
- What commercial models (subscription tools, embedded services, outcome-based pricing) are proving most effective across smallholders and large enterprises?
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