The Role of AI Analytics in Building Employee Motivation and Engagement Systems: A Study of the Impact of Automated Performance Assessment Models
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Abstract
The study is oriented toward a comprehensive conceptualization of how artificial intelligence technologies and predictive analytics tools are restructuring contemporary human resource management systems. The analytical focus is placed on a paradigmatic shift: from predominantly retrospective and regulation-driven performance evaluation procedures to adaptive automated models capable of continuously monitoring engagement levels, promptly identifying markers of organizational strain, and proactively assessing the probability of workforce attrition. On the empirical basis of data from 2023 and statistical datasets pertaining to early 2024, the paper elucidates the impact of algorithmic management on the contours of intrinsic motivation as interpreted through self-determination theory. A separate analytical layer is devoted to the applied and engineering foundations of artificial intelligence implementation in human resource practices. The capabilities of machine learning algorithms, including XGBoost and Random Forest, in the construction of predictive models are examined, along with the potential of natural language processing (NLP) technologies for interpreting semantic and affective-evaluative components of communication. This enables sentiment analysis and the assessment of social climate dynamics within work collectives. A significant place is occupied by the issue of the transparency paradox: the growth of observability and measurability of processes is accompanied by the complication of trust-based relationships and the intensification of normative and moral risks. In this connection, ethical challenges arising from the integration of intelligent systems into core human resource management domains are emphasized. The results obtained confirm the hypothesis that artificial intelligence–driven analytics can substantially strengthen operational performance and support employee loyalty, provided a critically important condition is met, namely the preservation of balance between the automation of managerial decision-making and the maintenance of employee autonomy. As a practical outcome, a strategic framework is formulated for the implementation of human-centered artificial intelligence solutions oriented toward the development of human resilience in an environment of uncertainty that characterizes the contemporary labor market.
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