Keywords

COVID-19, Artificial Intelligence, GDP, Economic Crises

Abstract

The COVID-19 epidemic exposed weaknesses affecting world trade and company continuity, therefore highlighting the vital need for supply chain resilience. This research investigates how artificial intelligence (AI) technologies improve supply chain resilience, especially in times of economic recession-like disturbance. It starts by pointing out important weaknesses in conventional supply chains and then looks at how AI-driven solutions—such as predictive analytics, machine learning, and autonomous systems—may reduce these hazards by allowing real-time visibility, demand forecasting, and operational optimization. By means of a thematic study of current literature, one gains understanding of the strategic importance of artificial intelligence in equipping companies to withstand shocks, react fast, and effectively recover. Case studies in manufacturing, retail, and logistics, among other fields, show useful results and applications of artificial intelligence integration. According to the report, artificial intelligence not only increases operational efficiency but also helps agility and flexibility—qualities absolutely vital in recessionary times. It also underlines the need for data-driven decision-making in guaranteeing continuity and competitiveness. We also cover issues including ethical questions, data protection, implementation complexity, and the requirement of labor reskilling. The article ends with stressing the need for proactive adoption of AI technology to build supply chain resilience and implying a road map for companies trying to include artificial intelligence in their resilience strategy. This study advances knowledge of how artificial intelligence might be used to create flexible, strong, future-ready supply networks in a world going more and more uncertain.

Cite this Article

Anitha Radhakrishnan, Darani Kannan, 2025. "Organizational Resilience Through Recessions", International Journal of Economics, Business, Management Research Intelligence (IJEBMRI) 1(1): 39-47.