Improving Food Market Infrastructure Through the Application of Artificial General Intelligence Technologies

Abstract

The article examines the improvement of food market infrastructure through the strategic application of Artificial General Intelligence (AGI) technologies. The study deeply analyses the operational limitations of conventional retrospective market analytics and proposes a novel, data-driven methodology for integrating advanced AGI systems into food production cycles. The scientific novelty of this research consists in developing a closed-loop AGI platform architecture specifically designed to forecast dynamic consumer preferences over a five-year horizon and to generate evidence-based production recommendations for food enterprises in the Kyrgyz Republic. Furthermore, the paper investigates how this intelligent platform optimizes supply chain logistics, reduces food waste, and mitigates market supply fluctuations under volatile economic conditions. By leveraging predictive modeling and machine learning algorithms, the proposed system enhances decision-making transparency and boosts competitiveness within the regional agro-industrial sector. Ultimately, the comprehensive findings and structured implementation frameworks provide valuable strategic guidelines for policymakers and industry stakeholders to ensure national food security and sustainable economic growth.