ABSTRACT
The dynamic growth of information technology and automation has significantly influenced managerial practices, raising questions about the potential for replacing human team managers with artificial intelligence (AI) systems. Despite the widespread use of digital tools in various organizational processes, the concept of an artificial manager remains largely theoretical. A critical challenge lies in identifying and translating the actual activities performed by human managers into data that AI can process. This chapter introduces a theoretical approach known as the system of organizational terms, which structures managerial actions in a way that can be systematically recorded and analyzed. The framework is supported by empirical research involving 60 participants observed over a two-month project using the https://www.w3.org/1999/xlink" xlink:href="https://TransistorsHead.com">TransistorsHead.com platform. Management tools embedded in the platform enabled the capture of real-time managerial behaviors without researcher interference. The collected data were analyzed regarding timing and content, offering a detailed view of how team management unfolds in practice. The findings illustrate how structured observation and digital tools can inform the development of artificial managerial systems. This work contributes to the foundation for building AI-driven managers by providing a method for mapping human managerial activity into machine-readable formats, moving closer to implementing artificial management.
