DESIGN OF A SYSTEM FOR ASSESSING PROJECT TEAM DEVELOPMENT IN AN IT ENTERPRISE
Abstract
The article develops a system for assessing project team development in IT enterprises as a tool for evidence-based management under conditions of digital transformation, hybrid interaction, and the growing role of artificial intelligence. A methodological architecture of the assessment system is proposed, integrating digital metrics with socio-psychological indicators. The system generalizes the SPACE, DORA, and Google re:Work approaches and includes six interconnected blocks: competency development, process and technological maturity, wellbeing and psychological safety, inclusiveness and social adaptation, distributed interaction, and AI-analytical maturity. For each block, key indicators, data sources, risks of misuse, and managerial benefits are identified. Particular attention is paid to combining objective digital data with survey results, retrospectives, and HR analytics, helping avoid a purely technocratic interpretation of team development. A mathematical framework for the integrated assessment of project team development is formed. An integrated team development index based on weighting coefficients and normalized evaluations is proposed. To ensure comparability of heterogeneous indicators, the application of min-max normalization for beneficial and non-beneficial indicators is substantiated. The study also proposes the use of an evidence coefficient for open data analysis, allowing consideration of the completeness of publicly available information on companies and teams. The methodological approach enables the integration of quantitative operational indicators with qualitative characteristics of team interaction and organizational culture. An algorithm for implementing the assessment system is developed, covering the definition of assessment boundaries, indicator selection, weight determination, data collection, index calculation, imbalance identification, and planning of managerial interventions. A matrix of managerial actions according to problematic development blocks is proposed. The system can be applied to Scrum teams, project portfolios, or entire delivery organizations. Its practical significance lies in creating an evidence-based foundation for monitoring and supporting project team development in IT enterprises.
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