Examining the Pathways to Project Success Through AI Adoption: An SEM Perspective

Abstract

Background: In the era of digital transformation, Artificial Intelligence (AI) has become a critical enabler of efficiency and innovation in project management. While many organizations have embraced AI tools to streamline operations and decision-making, the empirical understanding of how AI adoption influences project success through organizational mechanisms remains limited.
Objective: This study aims to investigate the pathways through which AI adoption contributes to project success, focusing on the mediating roles of Project Monitoring and Control (PMC), Decision-Making Quality (DMQ), and Team Collaboration and Communication (TCC).
Methodology: A quantitative research design was employed, collecting responses from 400 professionals with experience in AI-supported project environments. A structured questionnaire consisting of 20 Likert-scale items was used to measure five latent constructs. Data were analyzed using Structural Equation Modeling (SEM), supported by Principal Component Analysis (PCA), reliability tests (Cronbach’s alpha = 0.711), and model fit indices (GFI = 0.861, AGFI = 0.90, PGFI = 0.65).
Findings: The results reveal that AI adoption has a significant positive effect on PMC and DMQ, but a negative relationship with TCC, suggesting potential disruptions in communication when AI is not human-aligned. PMC and TCC significantly influence project success, while DMQ shows a weaker but positive contribution. The SEM model confirms a multidimensional pathway to success, wherein AI’s impact is largely mediated through executional and interpersonal mechanisms.
Conclusion: AI adoption, when strategically aligned with monitoring, decision-making, and collaborative structures, can significantly enhance project outcomes. However, the findings caution against over-reliance on AI at the expense of human communication and team dynamics.