THE IMPACT OF AI-ENABLED PROCESS AUTOMATION ON SMARTER OPERATIONAL MANAGEMENT: A CROSS-SECTOR ANALYSIS

Abstract

The rapid adoption of artificial intelligence (AI) has reshaped organizational practices by enabling process automation across diverse sectors. Yet, empirical evidence on how AI-enabled process automation influences operational management efficiency remains fragmented. The problem addressed in this study is the limited cross-sectoral understanding of whether AI-driven automation translates into measurable improvements in smarter decision-making, cost efficiency, and resource allocation within organizations. The purpose of this research is to examine the effect of AI-enabled process automation on operational management outcomes across multiple industries, including manufacturing, finance, healthcare, and logistics. The study seeks to quantify the extent to which automation enhances operational performance and to identify contextual factors that moderate these relationships. A quantitative research design is employed, utilizing survey-based primary data collected from 420 mid- to senior-level managers across four sectors. Stratified random sampling ensures representativeness of the industrial categories. Data are analysed using structural equation modelling (SEM) to test hypothesized relationships, supported by descriptive statistics, correlation analysis, and multiple regression. The key independent variable is AI-enabled process automation, operationalized through indicators such as workflow digitization, predictive analytics integration, and robotic process automation. The dependent variable is smarter operational management, measured in terms of efficiency, agility, and decision quality. Potential moderators, including organizational size and digital maturity, are tested to explore differential impacts across contexts.