Artificial Intelligence in Procurement and Supply Chain Management: A Systematic Literature Review of Performance Outcomes and Operational Efficiency

Abstract

Purpose. Artificial intelligence (AI) is taking procurement and supply chain management (SCM) from transaction automation to a more strategic and decision-driven approach. The research evidence concerning benefits of such a change in efficiency and organizational performance is still inconclusive, both within and beyond industry, across methods and regions. In this review, we’ll compile that evidence and outline the circumstances when AI-powered procurement can add real value.

Design/methodology/approach. The systematic literature review (SLR) was carried out according to the PRISMA 2020 statement. A corpus of 19 studies published during the period 2022-2026 was screened, appraised for quality through an adapted version of Mixed Methods Appraisal Tool (MMAT), and coded descriptively and synthesized thematically. The corpus comprises of econometric panel analysis, structural equation modelling (SEM), fuzzy-set qualitative comparative analysis (fsQCA), Bayesian best-worst multi-criteria decision-making (BBWMCDM), qualitative interviews, case evidence and formal modelling as well as prior reviews.

Findings. The benefits AI delivers are most consistent when applied to process efficiencies: faster invoice and sourcing cycles, improved demand forecasting, reduced stock-out and quicker supplier evaluation. It does actually make a difference to performance, but it depends. When AI is integrated with the strategy, through restructured structures and internal controls, and complemented by human capability, firm-level and resilience gains seem to emerge. Its evidence for better firm performance and operational performance in the supply chain comes from large sample—AI is responsible for better organizational performance and resilience [1] and better firm performance without direct impact on operational performance [2]. In Agentic AI, routines for resource-orchestration are the only means to improve circular procurement performance. The impact of supply chain practices on employee performance and satisfaction is magnified by human-centred AI [4].

Originality/value. The review incorporates a resource-based view, dynamic capability, strategic alignment and socio-technical view and leverages these views to create a capability-conversion framework comprising five propositions. It separates efficiency effects from performance effects, identifies issues in the literature with respect to the quality of the evidence, and establishes research agendas on causal identification, agentic AI and sustainability and developing-economy context.