Artificial Intelligence–Driven Enterprise Decision Support Systems: A Framework for Intelligent Business Process Automation in Digital Economies

Abstract

This study investigates the growing problem that many organizations deploy artificial intelligence, analytics, and workflow automation in fragmented ways, limiting their ability to convert decision intelligence into coordinated business process automation in digital economies. The purpose of the research was to examine how AI-driven enterprise decision support systems improve intelligent business process automation and related operational outcomes. Using a quantitative, cross-sectional, case-based design, the study collected primary data through structured questionnaires from 312 valid respondents drawn from enterprise cases operating in digitally enabled organizational environments with direct exposure to AI-supported decision and automation systems. The major variables included AI-driven decision support capability, AI-enabled analytics, automation intelligence, enterprise automation readiness, and intelligent business process automation, with outcome dimensions covering business process efficiency, decision quality, organizational responsiveness, and productivity. Data were analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression in SPSS. The findings showed strong positive perceptions across the core constructs, with mean scores of 4.08 for AI-driven decision support capability, 4.02 for AI-enabled analytics, 3.96 for automation intelligence, 3.89 for enterprise automation readiness, and 4.11 for intelligent business process automation. Decision quality recorded the highest outcome mean at 4.19, followed by business process efficiency at 4.14, productivity at 4.06, and organizational responsiveness at 4.05. Reliability was strong, with Cronbach’s alpha ranging from 0.81 to 0.90. Correlation results indicated significant positive relationships with intelligent business process automation, including AI-driven decision support capability (r = 0.71), AI-enabled analytics (r = 0.67), automation intelligence (r = 0.69), and enterprise automation readiness (r = 0.63). The regression model explained 64% of the variance in intelligent business process automation (R² = 0.64, F = 108.42, p < .001), while AI-driven decision support capability (β = 0.29), automation intelligence (β = 0.26), AI-enabled analytics (β = 0.21), and readiness (β = 0.18) significantly predicted the outcome; the moderation effect was also significant (β = 0.12, p = .004). The study implies that enterprises achieve stronger automation performance when AI capability is aligned with readiness, data quality, and system integration.