The Impact of AI-BA Opacity on Operational Inefficiency: Examining the Mediating Roles of AI Utilization Inefficiency and Organizational Resistance to AI

Abstract

Despite the growing adoption of Artificial Intelligence–Integrated Business Analytics (AI-BA) in organizational processes, empirical evidence reveals that such technological integration can inadvertently lead to operational inefficiencies. This study investigates the paradoxical relationship between AI-BA and operational inefficiency by examining the mediating roles of AI utilization inefficiency and organizational resistance to AI. Anchored in the Technology-Organization-Environment (TOE) framework, the study adopts a quantitative, cross-sectional design, surveying 343 senior professionals from manufacturing firms registered with the Lahore Chamber of Commerce and Industry (LCCI), Pakistan. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the hypothesized relationships. The findings demonstrate that AI-BA significantly contributes to operational inefficiency, both directly and indirectly. Notably, AI utilization inefficiency and organizational resistance to AI emerge as significant mediators, revealing that underutilization of AI tools and institutional resistance hinder the realization of expected operational benefits. These results challenge deterministic assumptions of AI-driven performance improvements, emphasizing instead the critical importance of organizational readiness, cultural alignment, and effective change management.