Abstract
The article explores the development and implementation of an automated system for constructing professionograms of artificial intelligence (AI) specialists using modern information technologies. Against the backdrop of an ever-evolving digital economy and rising demand for qualified AI professionals, the study highlights the need for accurate and scalable tools to identify and assess relevant competencies. The main objective of the study is to design an innovative digital platform that enables automated generation of professionograms by analyzing unstructured textual data—such as job descriptions, expert publications, and scientific articles—through machine learning algorithms. Special emphasis is placed on the use of TF-IDF (Term Frequency-Inverse Document Frequency) and LDA (Latent Dirichlet Alloca-tion), which allow for effective identification and classification of key terms, skills, and knowledge areas relevant to the AI domain. The methodology involves a comprehensive review of the labor market, an analysis of expert opin-ions, and the processing of large-scale textual data to develop a structured model of professional compe-tencies. The system is designed to function as a decision-support tool in HR analytics, enabling data-driven recruitment, optimization of corporate training programs, and personalized career planning. One of the core advantages of the proposed solution is the reduction of subjectivity in the recruit-ment and evaluation process, as well as its adaptability to various specializations within the AI field. The system architecture includes a web-based interface, an interactive testing module, and visualization tools such as profession graphs and thematic distribution diagrams. This allows stakeholders—from HR man-agers to educators and researchers—to intuitively explore competency models and apply them in real-world scenarios. Furthermore, the article discusses the broader implications of using data-driven approaches in strategic human capital management. It argues that automated professionogram systems can significantly improve workforce planning, reduce turnover rates, and align employee skill sets with evolving business needs. The research also opens pathways for extending the system to other professional domains, incor-porating soft skills and psychological profiling, and developing mobile applications for continuous self-assessment and skill tracking. In conclusion, the study presents a scalable and versatile framework that not only enhances the efficiency of personnel selection and development but also supports long-term organizational growth in the age of artificial intelligence.