Zhaxat Kenzhin

This study explores the integration of artificial intelligence (AI) into project management through a bibliometric analysis of scholarly publications and patents from 2001 to 2024. The paper aims to identify key trends, emerging applications, and global contributions to this evolving field. The study analyzes 51,752 patents from Lens, 5.5 million scholarly records from Google Scholar, and 436,380 records from Web of Science, providing a comprehensive overview of AI’s impact on project management. The analysis maps the evolution of AI-driven project management practices, focusing on resource allocation, risk management, and scheduling optimization. The findings reveal a substantial increase in AI-related project management research, with China and the United States leading in research output. A notable surge in publications post-2019 suggests acceleration due to the COVID-19 pandemic and the growing demand for digital transformation in project execution. Despite the rising adoption of AI in project management, research gaps persist, particularly in interdisciplinary methodologies, practical AI applications, and ethical concerns related to algorithmic decision-making. This study contributes to understanding AI’s transformative role in project management and highlights future research directions to enhance AI adoption for improved efficiency, decision-making, and project performance. The findings underscore the need for continued exploration of interdisciplinary approaches, practical implementations, and ethical considerations to foster innovation and efficiency in AI-driven project management.

The industrial era is gradually giving way to a creative economy driven by human resources. This study aimed to assess the impact of human resources within the creative economy on countries’ economic growth. The focus was on human capital involved in the creative economy. Panel data from 21 countries (2016–2023) were analyzed. The ILO classification methodology for employment types was applied. A fixed-effects regression model was employed to assess the impact of human capital on economic growth, while controlling for relevant factors. The model’s coefficient of determination increased from 0.494 to 0.652 with the addition of new variables, indicating improved accuracy. These variables were used to assess the effect of creative economy indicators on GDP per capita across countries. A direct correlation has been established between the share of employed people involved in the creative economy and the country’s level of economic development, specifically in terms of GDP per capita (Gini coefficient: r = –0.431, P = 0.01). The quantitative importance of human resources of the creative economy was calculated for both developed and developing countries of the world. The practical value of the obtained results lies in the possibility of their use for the development of public management decisions to stimulate the economic growth of the country.