The integration of Artificial Intelligence (AI) into procurement and supply chain management has significantly transformed traditional operations by enhancing efficiency, resilience, and decision- making. As businesses increasingly adopt AI-driven solutions, procurement processes and supply chain functions have evolved to become more automated, data-driven, and responsive to market dynamics. This study conducts a systematic literature review (SLR) to explore the role of AI in optimizing procurement processes and improving overall supply chain performance. The research examines key AI technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), highlighting their applications in procurement, logistics, and risk management. Findings suggest that AI enhances supplier selection, automates procurement workflows, improves demand forecasting, and strengthens supply chain resilience, particularly in response to disruptions such as the COVID-19 pandemic. By leveraging AI, organizations can minimize operational inefficiencies, enhance real-time decision- making, and improve supply chain sustainability. Despite its numerous benefits, the study identifies key challenges hindering widespread AI adoption in procurement and supply chains. These challenges include high implementation costs, data security concerns, workforce resistance to AI-driven automation, and the complexity of integrating AI with existing systems. Addressing these barriers requires strategic investments in AI infrastructure, enhanced data governance, and workforce training initiatives to ensure smooth AI adoption. The study concludes that while AI presents transformative opportunities for procurement and supply chains, businesses must develop comprehensive AI strategies to maximize its potential. Future research should focus on overcoming adoption barriers, improving AI-driven decision-making frameworks, and exploring emerging innovations to further enhance procurement and supply chain efficiency.
Al-Huzaili et al. (2025) studied this question.