Within the framework of the study, the specific features of the influence of machine learning algorithms (ML) on personnel professional adaptation and the operational performance of service organizations are examined within the logic of the technological transition of 2024–2025. The relevance of the research is determined by the phenomenon of a critical experience gap: 66% of employees at entry demonstrate insufficient readiness to perform job-related tasks, which translates into a decline in the stability and quality of services provided. The presented analysis is aimed at eliminating deficits in understanding how the synergy of agent-based artificial intelligence and predictive analytics can be used for deep personalization of onboarding and for accelerating the achievement of sustainable performance. The methodological foundation includes a systematic review of scholarly publications and applied studies, as well as a comparative analysis of prognostic approaches represented by XGBoost models, Random Forest models, and neural network architectures. The results obtained demonstrate that the implementation of adaptive digital employee support loops ensures a reduction in the time required to reach target performance within a range of 40–80%, while simultaneously generating an increase in labor efficiency in service functions with an average value of 22.6%. Additionally, it is substantiated that the application of ensemble models in conjunction with semantic search mechanisms based on cosine similarity enhances the accuracy of relevant content selection and managerial interventions, as a result of which return on investment is achieved within the first year for 74% of organizations. The practical significance is associated with the applicability of the conclusions for human resources managers, leaders of digital transformation, and the research community in the field of management, oriented toward the implementation of artificial intelligence in strategic human capital management frameworks, taking into account the requirements of the European Union Artificial Intelligence Regulation of 2024.