CHALLENGES AND PROSPECTS OF ARTIFICIAL INTELLIGENCE APPLICATION IN HR PROCESSES OF DEFENSE INDUSTRY ENTERPRISES

The research examines the potential applications of artificial intelligence (AI) across various stages of personnel management processes in defense sector enterprises: workforce planning, recruitment, training and development, and personnel assessment. The primary functions of AI in human resources processes have been identified, encompassing forecasting, pattern recognition and data transformation, evaluation, and communication. During the workforce planning phase, AI can analyze the dynamics of key enterprise processes to determine optimal periods for conducting recruitment campaigns and forecast personnel requirements based on historical data. In the recruitment process, AI can automate the processing of personnel documentation, perform comprehensive analysis of applicant resumes, verify their compliance with job requirements, document interviews and their outcomes, and automate communication with applicants via chatbots. Regarding personnel training and development, AI can provide employee support during training through chatbots and generate educational content. The study addresses security concerns associated with AI implementation and the quality of data produced by artificial intelligence systems. Proposed solutions aim to mitigate data leakage risks and enhance the quality of AI-generated outcomes. Special emphasis is placed on utilizing BPMS (Business Process Management System) as a methodology to address data quality issues generated by AI. BPMS facilitates automated execution of business processes, enables integration with external systems including AI services, and allows assigning user tasks based on predefined conditions. This approach streamlines the storage of standardized prompts and ensures human verification of results. Additionally, the introduction of a variable into the process is proposed, enabling HR specialists to be involved only in specific cases determined by AI assessment of input parameters quality.