Artificial Intelligence and Managerial Decision-Making in Manufacturing Firms in Delta State

Abstract

This study examined the impact of artificial intelligence (AI) on managerial decision-making in manufacturing firms in Delta State, Nigeria, focusing on machine learning and robotic process automation (RPA). A quantitative cross-sectional survey design was adopted, and data were collected from 234 managers and supervisors using structured questionnaires. The data were analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis. Findings revealed that machine learning significantly enhances managerial decision-making by improving data analysis, forecasting accuracy, and decision quality. Similarly, RPA positively influences decision-making by automating routine tasks, ensuring data accuracy, and providing real-time operational insights. The regression results indicated that both variables significantly predict managerial decision-making effectiveness. The study concludes that AI technologies are critical for improving decision speed, accuracy, and strategic responsiveness in manufacturing firms. It recommends that firms invest in AI infrastructure and integrate AI tools into managerial processes to enhance performance and competitiveness. This study contributes to knowledge by providing empirical evidence on AI-driven decision-making in the Nigerian manufacturing sector.