DEVELOPMENT OF A MANAGEMENT SYSTEM IN THE CONTEXT OF HUMAN–ALGORITHMIC INTERACTION

Abstract

This study examines the adaptation of management systems in product IT organizations operating under conditions of human–algorithmic interaction. Its purpose is to develop a management system that supports the coordinated integration of artificial intelligence into organizational processes. The empirical basis comprises three product IT organizations with different levels of AI integration. The study applies a comparative multiple-case design, social network analysis, comparative analysis of management practices, correlation analysis, LASSO regression, and linear trend forecasting. Correlation and LASSO results are interpreted as exploratory because the analysis covers only three organizations.The findings show that AI integration changes communication processes, employee roles, responsibilities, and the coordination of work. AI became an important source of professional information, while the studied communication networks demonstrated weaker interpersonal ties and varying degrees of fragmentation. The comparison identified three levels of management system adaptation: high, partial, and initial. Correlation analysis indicated positive relationships between the frequency of AI use and selected non-financial development indicators. However, LASSO did not retain AI use frequency after network and organizational characteristics were considered together. This suggests that organizational outcomes depend not only on the intensity of AI use but also on the management conditions in which it is embedded.Based on these findings, the study proposes a management system in the context of AI integration (MSAI). The system establishes the organizational basis for coordinating human and algorithmic participation, maintaining communication, allocating responsibility, and supporting managerial decisions. It also defines its operating principles and a staged implementation sequence. Forecasts for 2026–2030 indicate the possible continuation of positive trends in selected non-financial indicators, although they represent baseline projections rather than evidence of a causal effect.