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Liburkina L. M.

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Balanced AI Management of Remote IT Teams: A Conceptual Model For Integrating Algorithmic Monitoring and Team Cohesion
Ilchuk P. H., Horeiko D. Y.

Ilchuk, Pavlo H., and Horeiko, Danylo Ya. (2026) “Balanced AI Management of Remote IT Teams: A Conceptual Model For Integrating Algorithmic Monitoring and Team Cohesion.” Business Inform 3:570–577.
https://doi.org/10.32983/2222-4459-2026-3-570-577

Section: Management and Marketing

Article is written in Ukrainian
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UDC 005:658.3

Abstract:
The article examines the transformation of IT project management models in the context of the spread of remote work formats and the active integration of artificial intelligence systems into management processes. The relevance of the study is due to the increasing complexity of coordinating the activities of distributed IT teams in a digital environment characterized by high uncertainty, process nonlinearity, and elevated levels of organizational turbulence, which are described by the BANI (Brittle, Anxious, Nonlinear, Incomprehensible) conception. Under such conditions, traditional project management approaches based on hierarchical control and manual information analysis are gradually losing efficiency, which highlights the necessity of using algorithmic tools to support managerial decision-making. At the same time, excessive algorithmization of management can create risks of reduced trust, employee autonomy, and psychological safety within teams, which necessitates the development of balanced models for integrating artificial intelligence into management practices. The aim of the article is the theoretical substantiation and development of a conceptual model of balanced AI management of IT projects, which integrates algorithmic performance monitoring with mechanisms that support team cohesion in remote IT teams. The methodological basis of the research consists of methods of system-structural analysis of scientific sources, comparative analysis of traditional and algorithmic approaches to project management, as well as methods of generalization and conceptual modeling. The study analyzes modern scientific approaches to algorithmic management, the sociotechnical integration of digital management systems, and the use of data analytics in remote collaboration environments. As a result of the study, the tools of algorithmic monitoring were systematized into three interrelated levels: operational, predictive, and socio-communicative. The operational level covers the analysis of software development process performance indicators, the predictive level involves algorithms for forecasting project execution risks and resource management, and the socio-communicative level includes tools for analyzing team interaction, communication networks, and the psychological state of team members. Based on this systematization, a conceptual model of balanced AI management of IT projects is proposed, combining the Performance Core with the Cohesion Layer for analyzing team cohesion. The model is complemented by a system of ethical and legal safeguards for the use of algorithmic systems and the Human Override principle, which ensures the primacy of human interpretation of algorithmic analysis results. The practical significance of the study lies in the development of a phased algorithm for implementing the proposed model into the IT project management system, which involves auditing the organization’s digital maturity, setting up algorithmic monitoring metrics, integrating tools for analyzing team interaction, and developing managerial competencies in the field of interpreting algorithmic data. The proposed approach contributes to increasing the operational resilience of IT projects, improving the quality of managerial decisions in remote teams, and creating a balance between the technological efficiency of digital management systems and the social resilience of the organizational environment.

Keywords: remote IT teams; IT project management; artificial intelligence; algorithmic management; algorithmic monitoring; team cohesion; BANI environment.

Fig.: 1. Tabl.: 1. Bibl.: 14.

Ilchuk Pavlo H. – Doctor of Sciences (Economics), Associate Professor, Professor, Lviv Polytechnic National University (12 Stepana Bandery Str., Lvіv, 79013, Ukraine)
Email: [email protected]
Horeiko Danylo Ya. – Postgraduate Student, Lviv Polytechnic National University (12 Stepana Bandery Str., Lvіv, 79013, Ukraine)
Email: [email protected]

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