With the recent technological advancements, empowered by the self-learning capabilities of algorithms, and the increasing power of machines computation, Artificial Intelligence (AI)-driven technologies have become more salient for addressing and solving specific types of business problems. This saliency is no less important for firms operating in the AgriTech sector where the impacts of AI-driven technologies and systems are bringing new opportunities and challenges. We argue that with the unique characteristics of AI technologies and emerging challenges and aspirations of the AgriTech sector, there is a need for rethinking traditional theorizations of technology adoption and readiness within AgriTech firms. In this study, we develop an understanding of AI readiness and adoption through a fuller appreciation of micro and meso empirical data that delineates the determinants of AI readiness and uncovers a set of strategic components that can help AgriTech firms better manage the readiness process for AI adoption. To do this, we employ a mixed- methods approach and elicited data from 236 e-surveys and 25 interviews from an important conference in the AgriTech field. Our findings outline implications for research and practice for understanding AI-technology readiness.