Artificial Intelligence-Assisted Management Decision-Making in the Context of Digital Transformation

Abstract

The rapid advancement of artificial intelligence (AI) is reshaping managerial decision-making across industries, fundamentally altering how organizations allocate resources, manage risks, and coordinate complex operations. In business administration contexts such as logistics, tourism, education, and accounting, AI-driven systems increasingly influence strategic planning, operational control, and performance evaluation. However, existing research often treats AI adoption as a technological issue, overlooking its implications for managerial cognition, organizational routines, and governance structures.This study develops a mechanism-based analytical framework to examine how AI supports managerial decision-making under digital transformation. Focusing on three core AI approaches—machine learning models, knowledge-based systems, and predictive analytics—the paper analyzes their roles in decision automation, managerial augmentation, and organizational learning. The framework highlights three key application domains: intelligent demand and resource management, risk control and resilience building, and performance monitoring and accountability enhancement.The study argues that AI contributes to business value not through full automation, but by restructuring decision processes, reducing information asymmetry, and enabling adaptive managerial control. By integrating AI theory with business administration perspectives, this research advances understanding of AI-enabled management and offers insights applicable across multiple service- and information-intensive industries.