Methodology for Assessing the Effectiveness of AI Implementation in HR Processes

Abstract

Purpose. The study explores the implementation of generative artificial intelligence in human resource management processes within enterprises and examines methodologies for assessing its effectiveness. It substantiates the necessity of utilizing innovative tools to automate routine HR tasks in the context of modernization and digital transformation of business operations.

Methods. The research framework is based on the conceptual principles of artificial intelligence applications by business entities, the laws of dialectical logic, the integral interconnection of personnel management components, and the formation of a contemporary methodology for evaluating the efficiency of AI technologies through the automation of HR processes. To achieve the stated objective, the study employs the dialectics of scientific inquiry and a set of general and specialized research methods, including theoretical analysis, synthesis, system analysis, classification methods, and systemic-structural analysis.

Results. The findings of the study systematize methodological approaches to assessing the effectiveness of artificial intelligence in enterprise management. Key factors influencing its implementation and formalization have been identified, forming the basis for defining the necessity of developing a unified methodology for evaluating AI effectiveness in HR processes under modern conditions.

Conclusions. Implementation of Artificial Intelligence in HR Processes: Strategic Importance and Key Aspects. The integration of artificial intelligence (AI) into HR processes represents a strategically significant step toward enhancing personnel management efficiency. The key findings of effectiveness assessment highlight several crucial aspects: automation of routine tasks reduces time expenditures associated with resume processing, initial candidate screening, and administrative HR operations; improved decision-making quality through the analysis of large datasets, enabling HR professionals to better forecast workforce needs, identify talent, and optimize team management; personalization of employee interactions – AI-driven algorithms facilitate the development of individualized training and professional development programs, enhancing employee motivation; minimization of bias risks – AI models, built upon objective data, help reduce subjectivity in HR decisions, particularly in recruitment processes; financial benefits – the effective implementation of AI in HR leads to cost reductions in talent acquisition and workforce management while simultaneously increasing overall productivity. However, the success of AI-driven HR processes depends on the proper configuration of algorithms, adherence to ethical principles, and the establishment of adequate trust levels among employees. A crucial stage in this transformation is the adaptation of corporate culture and the upskilling of HR specialists in utilizing emerging technologies. Thus, to maintain competitive advantage, companies must consider integrating generative AI technologies as a means of ensuring sustainable growth in an increasingly dynamic and unpredictable business environment.