Sarcouncil Journal of Multidisciplinary

Abstract: The recent trend of automating the procurement sector has opened the door to the adoption of advanced technologies such as machine learning (ML), transforming the traditional approach to spend analysis. This paper presents a review of both current and future trends in procurement automation, with a particular focus on the application of ML algorithms to enhance spend visibility, strategic sourcing, and supplier management in the future. The paper outlines the current uses of ML for classifying real-time data, conducting predictive analytics, and detecting anomalies that facilitate agile and cost-effective procurement decisions, drawing on ten recent authoritative research studies. It also highlights emerging trends such as autonomous agents, natural language processing, blockchain, and the integration of industry-specific ML models. The benefits of ML in the procurement field are considerable in terms of strategic advancement, despite challenges related to data quality, transparency, and change management. As intelligent procurement systems continue to be gradually adopted by organizations, the role of spend analysis is expected to evolve into a strategic, value-creating activity within supply chains.