Assessing the adoption of Gen-AI in the Italian Agri-food Industry: an empirical analysis

The integration of Generative Artificial Intelligence (Gen-AI) into the agri-food sector marks a significant shift, enhancing the precision of production and distribution strategies, process optimization, and productivity. Gen-AI's capability to detect patterns enables agricultural stakeholders to make informed decisions, optimizing crop yields and minimizing waste. Simulta-neously, it aids logistics operators in refining supply chain efficiency through improved process, inventory, and distribution management. De-spite the growing adoption of Gen-AI in agri-food practices, the scholarly examination of its impact remains scarce, highlighting a critical need for fur-ther research to understand its transformative potential and implications within the sector’s production paradigms. This study focuses on identify-ing the various factors influencing the adoption of Gen-AI technology in agri-food contexts, encompassing technological, environmental, and organi-zational dimensions. It aims to explore the drivers behind agri-food opera-tors' intent to adopt Gen-AI and assess how organizational structure and leadership influence this adoption. By investigating these elements, the re-search seeks to provide a nuanced understanding of the integration of Gen-AI in the agri-food sector, offering insights into the challenges and oppor-tunities that lie ahead for stakeholders navigating this technological evolution.