An Artificial Intelligence Framework for Food Supply Chain Management: Policy Implications for UAE Public-Sector Agricultural Authorities

Abstract

Artificial intelligence offers significant potential to strengthen food supply chain management in countries facing resource constraints and food security pressures. This study develops and empirically evaluates an AI framework within the Abu Dhabi Agriculture and Food Safety Authority and derives policy implications for UAE public-sector agricultural authorities. A quantitative cross-sectional survey was administered to operational employees. Of 500 questionnaires distributed from a population of 1,506 employees, 402 valid responses were retained after data screening. The data were analysed using SPSS and Partial Least Squares Structural Equation Modelling. The model examined four AI capabilities, namely Expert System, Machine Learning, Agent-Based System, and Artificial Neural Network, in relation to three food supply chain outcomes: Production and Sourcing, Logistics and Distribution Efficiency, and Safety and Quality Control. Six of the twelve hypothesized relationships were supported. Artificial Neural Network was significantly associated with all three outcomes, Expert System with Production and Sourcing and Safety and Quality Control, and Agent-Based System with Logistics and Distribution Efficiency. Machine Learning showed no significant direct effects. Although the structural model demonstrated predictive relevance, several constructs recorded average variance extracted values below 0.50; therefore, the findings should be interpreted with caution. The study proposes a capability-based policy framework emphasizing AI governance, data infrastructure, workforce readiness, institutional coordination, and phased implementation. Sustainability is treated as a broader policy objective supported through improved operational resilience, efficiency, and safety rather than as a directly measured outcome.