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.