ARTIFICIAL INTELLIGENCE AND EFFECTIVENESS OF ORGANISATIONAL DECISION MAKING (A CASE STUDY OF A STUDY OF UBA, ILORIN, KWARA STATE

ABSTRACT As organizations adapt to rapid technological advancements, Artificial Intelligence (AI) has emerged as a crucial tool for enhancing decision-making effectiveness. In the dynamic banking sector, institutions like United Bank for Africa (UBA), Ilorin, are increasingly integrating AI technologies such as machine learning, predictive analytics, and natural language processing to optimize decision-making processes. However, the extent of AI adoption and its impact on employee participation in decision-making remains underexplored, particularly in the Nigerian context. This study, therefore, investigates the influence of AI on organizational decision-making effectiveness in UBA, Ilorin. The specific objectives of this study were to: (i) determine the extent to which automated decision technologies are integrated into UBA’s decision-making processes; (ii) assess the impact of AI on employee participation in decision-making; and (iii) examine organizational factors (e.g., leadership style, communication flow, and organizational culture) influencing the relationship between AI and employee involvement. A descriptive survey design was employed, using structured questionnaires administered to 86 staff across five UBA branches in Ilorin. Data were analyzed using regression analysis and statistical hypothesis testing. The findings of the study were that: i. Automated decision technologies are significantly used in UBA’s organizational decision-making processes (β = 0.712, t = 8.965, p < 0.05); ii. AI has a significant negative effect on employee participation in decision-making when systems lack transparency (β = –0.315, t = –4.671, p < 0.05); iii. Organizational factors significantly moderate the relationship between AI use and employee involvement (β = 0.658, t = 9.123, p < 0.05). The study concludes that while AI significantly enhances decision speed and accuracy, its impact on employee participation depends largely on organizational support systems. It recommends that UBA and similar institutions promote inclusive decision-making by ensuring AI systems are transparent, ethical, and complemented by effective leadership and communication structures. Regular training, ethical oversight, and fostering a collaborative AI-human work environment are essential to maximize AI's benefits while minimizing employee resistance.