Assessing the Adoption of Gen-AI in the Italian Agri-Food Industry: An Empirical Analysis

Piloca Diletta (), Luca Quaglieri, Francesco Mercuri and Bernardino Quattrociocchi
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Piloca Diletta: Sapienza University of Rome
Luca Quaglieri: Sapienza University of Rome
Francesco Mercuri: Sapienza University of Rome
Bernardino Quattrociocchi: Sapienza University of Rome

Chapter Chapter 3 in Advanced Perspectives and Trends in Digital Transformation of Firms, Networks, and Society, 2025, pp 27-33 from Springer

Abstract: Abstract 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. Simultaneously, it aids logistics operators in refining supply chain efficiency through improved process, inventory, and distribution management. Despite the growing adoption of Gen-AI in agri-food practices, the scholarly examination of its impact remains scarce, highlighting a critical need for further research to understand its transformative potential and implications within the sector’s production paradigms. This study focuses on identifying the various factors influencing the adoption of Gen-AI technology in agri-food contexts, encompassing technological, environmental, and organizational dimensions. It aims to explore the drivers behind agri-food operators’ intent to adopt Gen-AI and assess how organizational structure and leadership influence this adoption. By investigating these elements, the research seeks to provide a nuanced understanding of the integration of Gen-AI in the agri-food sector, offering insights into the challenges and opportunities that lie ahead for stakeholders navigating this technological evolution.

Keywords: Gen-AI; Agri-food; Technologies; IA; Digital transformation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-80692-6_3

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DOI: 10.1007/978-3-031-80692-6_3

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