SEM-Based Analysis of AI-Enabled Decision Making and Its Effects on Project Outcomes

Abstract

Background: Artificial Intelligence (AI) is increasingly being integrated into project management to enhance decision-making capabilities and drive project performance. However, the actual impact of AI-enabled decision-making on project outcomes remains insufficiently understood, especially when factoring in team dynamics and decision quality as mediating influences.
Objective: This study aims to examine the structural relationships among AI Integration in Decision Making, Team Acceptance of AI, Decision Quality, Project Efficiency, and Project Success. It specifically investigates how decision quality mediates the effect of AI integration and team acceptance on project performance outcomes.
Methodology: A quantitative research design was employed using a structured questionnaire distributed to 300 professionals involved in AI-enabled projects. Data were analyzed using Structural Equation Modeling (SEM) to test the hypothesized relationships. Model fit was assessed through standard indices including RMSEA, CFI, SRMR, and CMIN/DF.
Findings: The results confirm that AI Integration significantly enhances decision quality, which in turn positively influences both project efficiency and project success. Team Acceptance of AI, however, showed only a marginal effect on decision quality. Decision quality emerged as a central mediating variable, highlighting its pivotal role in translating AI capabilities into tangible project outcomes.