STRATEGIC APPLICATION OF ARTIFICIAL INTELLIGENCE IN AGRIBUSINESS SYSTEMS FOR MARKET EFFICIENCY AND ZOONOTIC RISK MITIGATION

Abstract

 This study investigates the strategic application of artificial intelligence (AI) in agribusiness systems with the dual aim of enhancing market efficiency and mitigating zoonotic risks, addressing two of the most critical challenges confronting global food systems. AI has emerged as a transformative technological paradigm capable of integrating vast, heterogeneous datasets from agricultural production, supply chain logistics, and veterinary health networks to generate real-time, predictive insights. These capabilities hold significant potential to stabilize volatile markets and strengthen biosecurity within highly interconnected agri-food systems. To examine this potential systematically, the study employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which ensured methodological transparency, reproducibility, and rigor. An initial search retrieved 1,247 publications across major scholarly databases and institutional repositories. Following a structured screening and eligibility assessment process, 122 studies were selected for in-depth qualitative synthesis. These studies were thematically categorized into seven domains: conceptual and theoretical foundations; production-level optimization; market systems and supply chain efficiency; zoonotic risk detection and mitigation; global case studies and institutional experiences; data infrastructure, ethics, and governance; and synthesis of conceptual gaps. Simultaneously, AI enhances bio surveillance through anomaly detection, natural language processing of veterinary data, genomic epidemiology, and spatial risk modeling, enabling earlier detection and targeted containment of zoonotic threats. Evidence from global case studies highlights measurable improvements in yield stability, compliance reliability, and disease risk management, alongside reductions in losses and border clearance delays. The review also identifies critical enabling conditions—such as data interoperability, governance frameworks, and institutional capacity—that determine the long-term success of AI integration. Collectively, this synthesis reveals that AI can function as a unifying infrastructural layer that links efficiency and biosecurity goals, reframing them as mutually reinforcing rather than competing objectives. The study concludes that strategic AI deployment, underpinned by robust data systems and cross-sectoral governance, offers a viable pathway to building resilient, transparent, and risk-aware global agribusiness networks.