Lauren Campbell
Purpose: The aim of the study was to analyze the influence of artificial intelligence-driven procurement systems on procurement performance in public institutions Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The findings indicate that Artificial Intelligence-driven procurement systems significantly enhance procurement performance in public institutions by improving efficiency, transparency, supplier selection, decision-making, and cost management through automation and data-driven processes. However, their successful implementation depends on organizational readiness, adequate digital infrastructure, skilled personnel, high-quality data, and supportive regulatory frameworks. The literature also reveals limited empirical evidence on the influence of AI-driven procurement systems in public institutions within developing countries, highlighting the need for further context-specific research. Unique Contribution to Theory, Practice and Policy: The technology–organization–environment (TOE) framework, resource-based view (RBV) & dynamic capabilities theory may be used to anchor future studies on analyze the influence of artificial intelligence-driven procurement systems on procurement performance in public institutions. Procurement professionals should receive continuous training in artificial intelligence, data analytics, and digital procurement to strengthen their ability to utilize AI technologies effectively. Governments should formulate comprehensive national policies and regulatory frameworks that guide the adoption, governance, and ethical application of Artificial Intelligence within public procurement.