Impact of Artificial Intelligence on Procurement Management Performance

Abstract

This study examined the impact of Artificial Intelligence (AI) on procurement management performance at the College of Business Education (CBE), Mbeya Campus, with a focus on transparency, accountability, and value for money. Employing a descriptive research design with a quantitative approach, data were collected from 40 purposively selected participants—including procurement officers, auditors, and accountants—through structured questionnaires. The study applied descriptive statistics, Pearson correlation, and multiple regression analyses to evaluate the influence of AI technologies such as Robotics, Process Automation, and Machine Learning on procurement outcomes. Findings revealed that all three AI technologies significantly and positively affect procurement performance. Machine Learning demonstrated the strongest impact, particularly enhancing transparency and value for money, followed by Process Automation and Robotics. High Cronbach’s Alpha scores (≥0.8) confirmed the internal consistency of the instruments used, while data normality tests validated the use of parametric statistical techniques. The Pearson correlation coefficients showed strong and statistically significant relationships between AI tools and performance indicators, especially between Process Automation and Transparency (r = 0.642) and Machine Learning and Value for Money (r = 0.612). Regression results further supported Machine Learning as the most influential predictor of procurement performance (β = 0.581, p = 0.001). Despite positive perceptions of AI integration, performance indicators like accountability and transparency showed room for improvement, suggesting a gap between technological adoption and its effective utilization. The study recommends capacity building for procurement practitioners to optimize AI applications and improve procurement outcomes in public institutions.