Purpose This study explores the major drivers of Artificial Intelligence (AI) adoption by AgriTech firms in the Indian agriculture sector. The study aims to identify and assess operational, technological and policy-related factors that make adoption in agriculture possible, particularly in AgriTech startups. Design/methodology/approach The process included expert consultation, qualitative appraisal and the formation of a structural framework based on interviews with analytics, AI and agricultural specialists. This study validated the top seven AI enablers in Indian agriculture through expert consultations. Five specialists in analytics and five in agriculture academia were consulted. Further, the Multicriteriathe Multicriteria Decision Making Technique, called the Interpretive Structural Modelling technique, is used to identify the interrelationships among the enablers, thereby reaching the major drivers. Findings Market connections, IoT and remote sensing, crop health and pest control, financial and insurance support and governmental regulations are the five significant enablers identified. Research limitations/implications The results indicate the importance of enablers and supportive government actions for adopting AI in AgriTech firms. Originality/value This research identifies and analyzes AI-powered enablers for the development of AgriTech startups in India, providing a strategic model to increase agricultural transformation, thus adding to the scarce studies in this domain.
Sangode et al. (Tue,) studied this question.