Role of Artificial Intelligence in Supplier Relationship Management Decision Making: A Systematic Literature Review

Abstract

This review examines the role of Artificial Intelligence in enhancing decision-making in Supplier Relationship Management and identifies the most frequently discussed AI role. This function is underexamined in the broader AI and supply chain management Literature. The rsearcher applies PRISMA flow models to mine 35 articles published between 2016 and 2026, the review identifies seven discrete AI-enabled roles: real-time supplier performance monitoring, data-driven decision-making, predictive risk assessment, procurement cost optimisation, buyer-supplier collaboration, supplier selection and segmentation and contract management and optimisation. Empirical evidence across manufacturing, construction, banking, and enterprise procurement contexts confirms that AI improves supply chain performance by 49%, amplifies resilience by 66%, achieves 85% accuracy in supply chain risk detection, and reduces procurement processing times by 85%. The Technology Acceptance Model was applied as the analytical framework, revealing a critical asymmetry: while all seven roles generate measurable Perceived Usefulness outcomes, Perceived Ease of Use barriers, including legacy system incompatibility, data quality deficits and workforce digital literacy gaps suppress adoption of the highest-impact roles. The review contributes a cross-sectorally validated typology of AI’s SRM functions, a TAM-grounded adoption framework, and a research agenda addressing algorithmic bias, longitudinal deployment dynamics, developing economy contexts, and AI-ESG compliance integration.