AI-Augmented Decision-Making Agility in Supplier Evaluation: Insights from a Qualitative Procurement Case Study

Abstract

Background: Artificial intelligence (AI) is increasingly discussed as a means of improving procurement efficiency and supply chain agility, yet its role in supplier evaluation remains insufficiently understood, particularly when decisions depend on fragmented information, cross-functional coordination, explainability, and managerial accountability. This study examines how AI may augment decision-making agility in supplier evaluation. Methods: An exploratory qualitative single-case study was conducted in a large multinational manufacturing company. Data were collected through 18 semi-structured interviews with procurement, logistics, quality, operations, and ERP/process actors, and analyzed through a Gioia-inspired thematic analysis, complemented by a descriptive assessment of theme recurrence. Results: The findings show that supplier evaluation is constrained by informational fragmentation, weak organizational memory, limited explainability, and the need to preserve contextual human judgement. AI was not perceived as a substitute for procurement professionals but as a decision-support infrastructure that may reconnect dispersed supplier knowledge, detect recurring problems earlier, and support transparent recommendations. Conclusions: The study develops a preliminary conceptualization of AI-augmented procurement agility as a bounded, process-level capability composed of AI-enabled supplier sensing, AI-supported interpretive integration, explainable decision support, and human-supervised responsiveness. The findings remain context-dependent and require further validation through comparative and longitudinal research.