2026· Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu· Vol 70, pp. 59-73· 0 citations· 15 references
TL;DR
The gap between the growing role of AI and current project management competency models is identified, which should be expanded to include AI-related competencies, such as AI literacy, data work, algorithmic decision-making, and technology ethics.
Abstract
Aim: The objective of this article was to systematically analyse the literature on AI-related competencies of project managers and assess how these competencies were addressed in the context of project management. Methodology: The study was conducted in accordance with the PRISMA 2020 guidelines, using bibliometric analysis (VOSviewer) and qualitative content analysis. The bibliometric analysis included 38 publications, and the qualitative analysis covered 26 works, coded into four competency categories: technological, cognitive, decision-making, and ethical-organisational. Results: The literature focuses primarily on the technological dimension of AI, while project managers' competencies are only marginally and inconsistently described. Technological and decision-making competencies dominate, cognitive and ethical-organisational competencies remain underdeveloped, and there is no consistent model of AI competencies for project managers. Implications and recommendations: Project management competency models should be expanded to include AI-related competencies, such as AI literacy, data work, algorithmic decision-making, and technology ethics. The DigComp 2.2 framework can support their systematisation. Originality/value: The article identifies the gap between the growing role of AI and current project management competency models and outlines directions for further research.
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.
Afsana Munni, Mustafa Alokoud· International Journal of App...· 0 citations
The rapid integration of artificial intelligence (AI) technologies into business processes is fundamentally transforming the traditional role of middle managers, who provide the critical link between strategy and operations in organizational hierarchies. AI-driven automation and decision systems are assuming routine managerial tasks, compelling middle managers into a new role structure that demands higher-order cognitive, analytical, and social competencies. This study aims to analyze the changing responsibilities of middle managers in the age of AI, identify the new skills and behaviors expected from them through a systematic literature review, and explain the resulting competency transformation within a holistic framework. Within the scope of the research, 39 articles published between 2018 and 2025 in the Web of Science database were examined in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. The findings indicate a shift in middle managers’ roles from operational supervision toward data-driven decision-making, human–AI collaboration design, and psychological empowerment.
Cite this article as: Sancar S, Camcı A. Yapay zekanın orta düzey yönetim becerileri üzerindeki etkileri: Sistematik literatür taraması. Journal of Business Administration and Social Studies. 2026;10:0005. doi:10.5152/JBASS.2026.26005
Semih Sancar, Alper Camcı· Journal of Business Administ...· 0 citations
The findings show that AI-related competency change extends beyond technical skills toward hybrid and portfolio-based configurations that integrate technical understanding, managerial judgment, learning agility, governance capabilities, and psychological readiness, and demonstrates that no single framework sufficiently explains these shifts.
The emergence of artificial intelligence (AI) has transformed project management processes and continues to provide immense opportunities to improve efficiency, decision-making, and results in project execution. In this paper, a systematic literature review was carried out with a view to assessing the extent of AI integration into project management practices through identifying its key functions, possible spheres of application, and issues related to its implementation in different industries.
The process of systematic literature review was performed in compliance with the PRISMA guidelines, and 62 scholarly articles, along with industry reports covering the period from 2015 to 2025, were investigated. The review was conducted through the search of four data-bases: Scopus, IEEE Xplore, Web of Science, and Google Scholar, with the help of inclusion and exclusion criteria. After that, the results of the review were collected through manual thematic content analysis.
It was found that AI possesses four main capabilities that determine the process of project management innovation: predictive analytics (68%), machine learning (60%), natural language processing (45%), and automation (50%). The analyzed literature revealed risk management to be an essential area of application of AI (70%), while cost estimation and resource allocation accounted for 60% and 55%, respectively. It was also noted that the industries used AI with different degrees of success: IT (80%) and construction (50%).
The use of AI in project management is expected to grant the opportunity to make advances in predictive capabilities, manage better utilization of resources, and simplify adminis-trative processes in practice. AI helps project managers concentrate on important aspects of the activities of the organizations they work for. However, the successful use of AI implies that all the necessary measures are taken to address issues related to data quality, ethics, and costs of project implementation.
This paper summarizes evidence from 62 studies that indicate the significant impact of AI on project management. Further research has to focus on designing ethical frameworks for the use of AI in practice.
Belay Gaga, Amit Kohli· Current Artificial Intellige...· 0 citations
The findings reveal that human - AI collaboration has a significant positive effect on project management performance, particularly in improving cost efficiency, schedule adherence, and quality delivery, indicating that organisations with strong digital infrastructure, skilled personnel, and supportive leadership derive greater benefits from AI integration.
Mehifu Egogwe Jaiyeola, Sunday Ade Bello· International Journal of Adv...· 0 citations
Artificial Intelligence (AI) is increasingly transforming project management by enhancing productivity improving forecasting accuracy, supporting more informed managerial decision making. Despite the growing body of research on AI applications in project environments the literature remains fragmented across technological, managerial and organisational dimensions. This study addresses this gap through a concept-centric systematic literature review of peer-reviewed studies published between 2022 and 2026. Drawing on a final sample of 72 studies selected from an initial dataset of 205 publications, the review synthesises the literature into key thematic areas, namely AI-enabled productivity, predictive analytics, prescriptive analytics, AI-driven risk prediction, human–AI collaboration, ethical AI governance, digital ecosystems and data infrastructure, governance and organisational integration, sustainability, and managerial decision support. The findings indicate that AI has evolved from a supporting analytical tool into a strategic capability that enables proactive project management through early risk detection, real-time monitoring, performance optimisation, and intelligent decision support. The review further highlights that the effective adoption of AI depends not only on technological sophistication but also on organisational readiness, governance structures, data quality, and the integration of human judgement with intelligent systems. The study contributes to the literature by providing a structured conceptual synthesis of AI in project management and identifying important directions for future research particularly the need for integrated frameworks, empirical validation across the project lifecycle, and stronger attention to ethical and governance considerations in AI-enabled project environments.
Zainab Aziz, T. Nenzhelele· International Journal of App...· 0 citations
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