Artificial intelligence and the reconfiguration of competency management systems in organizations

The Effects of Artificial Intelligence on Middle Management Skills: A Systematic Literature Review

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

Mapping the Intellectual Structure of Artificial Intelligence in HRM

Artificial intelligence (AI) is reshaping how organisations create, validate, share and use workforce knowledge. Despite rapid growth, research on AI in human resource management (HRM) remains fragmented across HRM, information systems and knowledge management. This study maps the intellectual structure of the Scopus-indexed literature and interprets its major research streams through a knowledge management lens. Drawing on 693 peer-reviewed journal articles published between 1991 and 2025, it combines performance analysis, bibliographic coupling, keyword co-occurrence mapping and thematic evolution with a focused qualitative reading of influential works in each cluster. The findings show rapid growth since 2019 and three dominant but weakly integrated streams: AI adoption and HR analytics; algorithmic decision-making, fairness and governance; and human-AI collaboration. The qualitative interpretation shows that these streams differ not only in topic but also in their assumptions about workforce knowledge: whether it is treated as data to be codified and optimised, as a source of bias and ethical risk, or as a human capability that intelligent systems can augment. The study contributes to knowledge management by showing that AI-HRM research is organised around competing processes of knowledge codification, validation, transfer and use, while giving less attention to knowledge creation, tacit expertise and employee-side knowledge agency. It concludes with a research agenda on knowledge governance, employee experience and cross-database validation. The Scopus-only corpus is acknowledged as a limitation.

Artificial Intelligence’s Transformative Impact on Management, Strategy, and Workplace Dynamics: A Systematic Literature Review

The integration of artificial intelligence (AI) into organizations is reshaping management practice, competitive strategy, and the experience of work, yet scholarship remains fragmented across disciplines and is predominantly Western-centric. This systematic literature review synthesizes interdisciplinary evidence on how AI transforms leadership and management functions, drives business model innovation, and alters workplace dynamics, with particular attention to Southeast Asian and Philippine contexts. Peer-reviewed, English-language journal articles published between 2016 and 2025 addressing the organizational, strategic, or workforce implications of AI were included; purely technical studies and non-peer-reviewed materials were excluded from the corpus, with institutional reports used only for contextualization. Scopus, Web of Science, ABI/INFORM, Business Source Complete, and the ACM Digital Library were searched. Screening followed the PRISMA 2020 guidelines: 1,245 records were identified, and 128 studies met all inclusion criteria and were synthesized thematically. First, AI integration facilitates an augmented leadership model in which effective managers combine human judgment with algorithmic capability. Second, AI operates as a strategic capability that enables adaptive, data-centric business models. Third, workplace dynamics exhibit a bifurcation effect whereby AI augments some roles while displacing or deskilling others. The review contributes an integrative tripartite framework connecting leadership, strategy, and workforce transformation, and identifies priorities for research and policy in emerging economies. No protocol was registered.

Artificial Intelligence for Strategic Workforce Planning: A Systematic Review of Organizational Adaptation Mechanisms

Artificial Intelligence Capabilities and Project Success – Analysis of Research Trends

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.

Human-artificial intelligence for organizational hyper-performance: a systematic literature review

Human-artificial intelligence (AI) collaboration has become an important topic in organizational management, as AI technologies are increasingly used in decision-making and everyday work activities. Although interest in this topic has grown in recent years, the literature remains fragmented and does not clearly explain how human-AI integration supports performance beyond basic efficiency improvements. This paper explores how this interaction can support organizational hyper-performance. The study is based on a systematic literature review of peer-reviewed business and management research. The analysis incorporates 80 studies, indexed in Scopus and Web of Science from 2019 to 2026. The searches were updated on 16 February 2026. Conceptual, qualitative, and quantitative studies are examined using thematic analysis. The findings show that hyper-performance does not result automatically from AI adoption. Instead, it depends on how organizations design decision-making processes and assign roles and tasks between humans and AI systems. Clear human-AI complementarity and well-defined decision-making structures are key enabling factors. The study concludes that organizational hyper-performance through human-AI integration is possible only under certain conditions and that clearer concepts and practical guidelines are needed for both research and management practice.