Unlocking Supply Chain Resilience: The Impact of Artificial Intelligence and IoT Integration in Agribusiness Operations
Year: 2026
Type: article
Abstract: Agribusiness supply chains are exposed to climate variability, biological uncertainty, perishability, demand volatility, infrastructure constraints, and fragmented coordination among farmers, processors, traders, logistics providers, retailers, and regulators. These vulnerabilities increasingly intersect with the transition toward lower-carbon production and distribution. Artificial intelligence (AI) and the Internet of Things (IoT) offer complementary capabilities: connected sensors and devices can capture field, storage, processing, and transport conditions, while AI can convert heterogeneous data into forecasts, anomaly alerts, and operational recommendations. This article examines how AI–IoT integration can improve supply chain resilience and operational efficiency while supporting carbon-neutrality transitions in agribusiness. An integrative review of recent academic research and authoritative reports is used to develop a theory-informed framework. The synthesis identifies six pathways: end-to-end visibility, predictive risk sensing, adaptive planning, quality and loss control, low-carbon operational optimization, and collaborative learning. It also identifies important boundary conditions, including data quality, connectivity, interoperability, managerial capabilities, farmer participation, cybersecurity, financing, and governance. The analysis cautions that digital adoption does not automatically create resilience or sustainability: poorly designed systems can intensify dependence on vendors, exclude smallholders, optimize local efficiency at the expense of system-wide performance, or increase energy use without reducing absolute emissions. The article proposes a phased implementation model and research propositions linking AI IoT capability to resilience, efficiency, and environmental outcomes. It argues that the strategic value of smart agribusiness should be assessed through verified reductions in disruption exposure, avoidable losses, resource intensity, and emissions—not through technology deployment alone.
Source: Advanced Business Journal
DOI: 10.65875/fz9jgw42
Authors: Mahedi Hasan, Sahilali Saiyed
Institution: Chaoyang University of Technology
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7223412449
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