Reframing Artificial Intelligence in Project Management: A Systematic Review and Socio-Technical Analysis
TL;DR
The Socio-Technical AI Governance in Project Management (STAG-PM) framework is proposed to explain how AI capabilities interact with managerial judgment, organizational processes, and institutional governance in project environments and provides a conceptual basis for future empirical research and context-sensitive AI implementation rather than a validated model.
Abstract
Artificial intelligence (AI) is increasingly used in project forecasting, monitoring, and decision support, yet research remains fragmented and predominantly technology-centered. This study synthesizes the literature and proposes the Socio-Technical AI Governance in Project Management (STAG-PM) framework to explain how AI capabilities interact with managerial judgment, organizational processes, and institutional governance in project environments. A narrative and integrative review of 26 peer-reviewed studies published between 2015 and 2025 was conducted using a structured search of Scopus, Web of Science, and Science Direct, followed by relevance screening, citation tracking, thematic synthesis, and abductive theory building. The literature is concentrated in construction and engineering and emphasizes prediction, monitoring, and technical performance, indicating that task-level improvements do not necessarily translate into project-level value without reliable and integrated data, alignment with project workflows, effective human interpretation, and appropriate governance. Based on these findings, the STAG-PM framework conceptualizes AI-enabled project management through five interdependent subsystems: data infrastructure, AI intelligence, project execution and control, human cognition and behavior, and governance and institutions. These subsystems interact through AI learning, decision augmentation (DA), and trust-adoption feedback mechanisms to explain how learning, human-AI interaction, and governance-related trust may shape relationships among these subsystems over time. STAG-PM provides a conceptual basis for future empirical research and context-sensitive AI implementation rather than a validated model. The framework positions AI as a means of augmenting managerial judgment while emphasizing socio-technical alignment, organizational integration, and accountable governance as conditions for responsible and sustainable AI-enabled project management.