π-Economy

RAR RUS 9-22 Shinkevich. Aleksey Lubnina Alsu Research on trends in the use of artificial intelligence at the level of industrial development project management In the context of the transition to a new technological paradigm, the digital transformation of industry is becoming especially relevant, requiring the implementation of large-scale projects through the involvement of state support institutions. In Russia, such an institution is the Industrial Development Fund (IDF), which is an effective tool for supporting projects. At the same time, artificial intelligence (AI) technologies are of particular interest as promising forms of digitalization, which are equally effective for optimizing the operations of industrial enterprises and for managing large-scale projects. In this regard, the goal of the article is to study the trends in the use of AI at the level of industrial development project management, achieved by addressing the following tasks: the activities of the IDF were analyzed, organization’s priority areas were identified; the dynamics of digitalization indicators for enterprises that received IDF support for industrial development projects from 2015 to 2023 were examined, with a forecast for 2025; the impact of digitalization on the project development of high-tech industries was determined through correlation-regression analysis; a set of recommendations for the development of AI at the level of industrial development project management was proposed. The article uses statistical data processing methods, a third-degree polynomial trend line and correlation-regression analysis. Based on the results, the following conclusions and recommendations were made: it is recommended to implement AI technologies in the IDF’s activities for analyzing projects, as well as processing complex, large, unstructured data, assessing the probabilities and consequences of risks, diagnosing the financial model of a project and automating document workflow; the IDF is recommended to collaborate with IT project support funds to coordinate requests from industrial enterprises and proposals from AI technology developers; industrial enterprises are recommended to actively implement AI to create smart factories (fully automated and robotic, controlled by AI), maintain equipment, monitor, control product quality, optimize supply chains etc. In the context of further research, it is of interest to develop targeted recommendations for the development and implementation of AI technologies in the activities of funds supporting large-scale projects, as well as in the operations of industrial enterprises. 10.18721/JE.18501 338.1 project management industrial development artificial intelligence industrial development fund digitalization https://economy.spbstu.ru/article/2025.115.1/ 01_shinkevich_lubnina.pdf

RAR RUS 23-33 Farahov Rustam Burnashev Rustam Matrenina Olga Application of artificial intelligence technologies for analysis of polymer composite materials in production conditions This article addresses the development and research of a modern information system aimed at effective recognition and comprehensive analysis of composite material particles. The proposed solution is based on advanced artificial intelligence (AI) technologies, including the application of deep convolutional neural networks (CNNs) for high-accuracy image classification of particles. System integration is achieved through Internet of Things (IoT) technologies that ensure interaction with modern measurement equipment used in industrial processes. A key component of the developed system is an expert evaluation module based on fuzzy logic inference mechanisms. This component is designed to enhance analysis accuracy in situations characterized by uncertainty or incomplete initial data. A created knowledge base containing production rules and specialized membership functions, also plays a crucial role. It allows for adequate processing of material property descriptions using linguistic variables. The implementation of the proposed approach has been carried out on the Python platform, widely used in software development due to its rich capabilities provided by libraries for machine learning and web programming. The user interface is presented as a convenient web portal, allowing users to upload images of samples under investigation, configure analysis process parameters and obtain final results in a user-friendly format, including graphs, tables and intuitive visualizations. The practical application of this information system significantly reduces time spent on analyzing composite materials, improves microstructural feature recognition quality and increases overall productivity typical of Industry 4.0 processes. It particularly contributes to the development of additive manufacturing technologies by enabling substantial improvement in product quality control, cost reduction and increased efficiency of production operations. Therefore, this development becomes an essential element of intelligent infrastructure for modern industrial enterprises, contributing to improved economic performance and product competitiveness. The study demonstrates the prospects of approaches combining AI methods and new information technologies, opening new horizons for automation and optimization of technological processes in industry. 10.18721/JE.18502 004.891 decision support system composite materials fuzzy logic knowledge base membership functions expert systems Python additive manufacturing artificial intelligence Industry 4.0 https://economy.spbstu.ru/article/2025.115.2/ 02_farahov_burnashev_matrenina.pdf

RAR RUS 34-48 Kiselev Rem Assessing the innovation activity of an organization using artificial intelligence systems This article presents a set of scientific and methodological principles for assessing an organization’s innovation activity, developed using a process-based approach with the integration of artificial intelligence (AI) technologies. The research’s novelty lies in the development of a comprehensive methodological framework combining a process-based approach, multi-level barrier analysis and AI technologies to assess innovation activity in specific institutional settings. For the first time, an integrated scenario-based management mechanism and the architecture of an intelligent analysis system, adapted to the challenges of sanctions pressure and logistical isolation, are proposed. The study aims to address the current problem of adapting innovation assessment tools to the conditions of regions with specific institutional barriers, using the Republic of Crimea as a case study. The object of the study is the innovation activity of organizations in the Republic of Crimea operating under sanction restrictions and logistical isolation. The subject of the study is a process-based approach to assessing an organization’s innovation activity using AI systems. The methodological basis of the work includes a process-based approach, institutional analysis, SWOT analysis, mathematical modeling and scenario planning. The empirical base includes data on regional organizations, regulatory acts and statistical data. The study addressed a set of interrelated tasks. Institutional and digital barriers to innovation activity were identified and systematized at the micro-, meso- and macrolevels, establishing their synergistic nature. An original multi-level model for diagnosing these barriers was developed, enabling the identification of obstacles at all stages of the innovation cycle. A matrix for assessing the impact of various factors was proposed and tested, revealing the dominant role of economic constraints at the microlevel. A scenario-based management mechanism was constructed, including three development options (“Adaptive Growth”, “Focus and Efficiency” and “Antifragility and Localization”), ensuring the adaptability of the strategy. An architecture for an intelligent analysis system based on machine learning methods (Random Forest, XGBoost, NLP) was developed and a detailed scheme for integrating AI into assessment activities was designed. The practical significance of the work was confirmed by the testing of elements of the developed methodological framework. The results are intended for public authorities to formulate regional innovation policies and for company management to improve the effectiveness of innovation management in the face of uncertainty and technological challenges. 10.18721/JE.18503 330 innovation activity process approach Republic of Crimea AI institutional barriers digital transformation scenario planning import substitution https://economy.spbstu.ru/article/2025.115.3/ 03_kiselev.pdf

RAR RUS 49-65 Dorzhieva Valentina Strategic planning for the development of artificial intelligence in the face of new challenges (a pharmaceutical industry case study) This article examines international experience in adapting national artificial intelligence (AI) development strategies to new challenges and identifying key directions for applying AI technologies in the pharmaceutical industry. Transformations in the global economy, driven by geopolitical changes and their impact on global mechanisms of international cooperation, have necessitated a revision of national AI strategies. A comparative analysis of the updated national strategies of the USA, EU, China, and Russia, aimed at assessing their adaptability to the rapidly changing conditions of the global AI market, revealed that each country has chosen its own unique development path. The USA and China, as recognized leaders in the development of AI technologies, are strengthening their positions and tightening their control over resources in this area. The main challenges include restrictions on access to global databases and prohibitions on the use of AI technologies, equipment and infrastructure. The author concludes that the successful implementation and scaling of AI in the pharmaceutical industry depends not only on favorable macroeconomic conditions, such as a significant domestic market and extensive investment opportunities, but also on the development and implementation of national strategies with clearly defined goals, which are also reflected in other strategic documents (programs, plans etc.). An analysis of the approaches of leading countries shows that national strategic initiatives in AI are being developed not only in high-tech countries but also in dynamically developing economies. However, these initiatives are fragmented, and state policy is based on disparate strategic planning documents, each covering only specific aspects of AI application in the pharmaceutical industry. This is also characteristic of Russia’s strategic documents defining national AI priorities. Therefore, the author substantiates the need to improve approaches for ensuring the alignment of national goals for AI development in the pharmaceutical industry in strategic planning documents. 10.18721/JE.18504 338.3 pharmaceutical industry strategic planning artificial intelligence national development strategy new challenges technological sovereignty https://economy.spbstu.ru/article/2025.115.4/ 04_dorzhieva.pdf

RAR RUS 66-80 Krasyuk Tatyana actualbil@gmail.com Developing a typology of competition and growth strategies based on the use of artificial intelligence The goal of this study is to develop a typology of competition and growth strategies based on the use of artificial intelligence (AI), using examples of the strategic development of the largest retail grocery chains as representatives of a technologically advanced and highly competitive cluster of the Russian economy, which has a significant share in the structure of the country’s GDP. The objectives of the research include the identification of various types and methods of AI applications in large-scale retail, their systematization and structuring within strategic planning frameworks, the empirical construction of an integrated strategy matrix and the development of a new modified competition and growth strategy. The relevance of the study is necessitated by the need to integrate advanced digital technologies into strategic analysis, planning and competitive positioning under contemporary conditions. The scientific novelty lies in the systematization of established industry practices of AI application and their transformation into a tool for the targeted development and competitive strategy design, as well as in the empirical development of a typology of integrated competition and growth strategies. The object of the research is competition and growth strategies based on the use of advanced intelligent technologies, including AI. The subject of the research is the strategic actions of Russia’s largest grocery retail chains. The study comprises a bibliographic review of recent research on the strategic development of large-scale public retail chains in modern Russia, an analysis of their key financial and economic indicators and an empirical assessment of strategic actions undertaken over the past five years. For the first time in the academic field, a concept of the competitive positioning of Russian retail chains relative to each other was developed based on actual data. Classical competition and growth strategy matrices were integrated into a unified framework, presented as a typology of competition and growth strategies. Furthermore, the study systematically identifies and classifies the forms of actual use of AI technologies by large-scale retail chains in the context of competition and growth strategies for the first time in the academic field. The scientific novelty of the research lies in the development of a new modified type of competition strategy based on a smart platform ecosystem, alongside a typology of integrated competition and growth strategies. By combining the systematization of AI technologies’ application trends and experience, coupled with financial and economic indicators, a conclusion was drawn regarding its importance as a factor in enhancing the competitiveness of Russia’s largest retail grocery chains. The research findings are presented in the form of strategic competitive action matrices 10.18721/JE.18505 330.322, 330.341, 334.02, 336.6 competitive strategy growth strategy positioning artificial intelligence matrix model of integration of growth and competition strategies transformation of strategies capitalization retail grocery chains typology of strategies https://economy.spbstu.ru/article/2025.115.5/ 05_krasyuk.pdf

RAR RUS 81-99 56502340400 0000-0002-1685-2625 Russian State University for the Humanities Vertakova Yulia vertakova@rambler.ru

Miusskaya sq. 6, Moscow, 125047, Russia

Shulgina Yulia Sobirov Bezhan Features of the life cycle structure of digital innovations based on the use of artificial intelligence This article examines the factors influencing the duration and life cycle structure of digital innovations based on artificial intelligence (AI). The relevance of this research is driven by the fact that AI technologies are an area of international competition that has a significant impact on socio-economic development, economic growth (in particular, GDP growth) and the technological sovereignty of the state. The goal of this article is to examine the features of the structure and duration of the life cycle of innovations based on AI models. The object of this study is digital innovations obtained using AI. The subject of this study is the life cycle structure of digital innovations associated with the use of AI, including various stages of the innovation process from initiation, development, implementation, commercialization and diffusion across various sectors of the economy. The authors set a number of tasks for conducting this study, including: analyzing the development trends of digital innovations based on AI; identifying the factors influencing the duration of various stages of the life cycle of digital innovations based on AI; characterizing the processes occurring at the stages of the lifecycle of digital innovations based on AI, depending on the level of development of the MLOps management methodology. The study identified trends toward increased research activity in the field of AI in recent years (an increase in the number of publications and patents), as well as the increasing complexity of models (an increase in the number of their parameters), which led to an increase in the duration of training and increased requirements for computing resources. A list of external and internal factors determining the duration of the processes forming the lifecycle of a digital innovation was compiled. A diagram of the lifecycle processes of an AI-based digital innovation is constructed in accordance with the MLOps maturity level of the implementing organization. The article demonstrates that assessing the maturity level is necessary for improving the development, testing and implementation processes, which helps to reduce the duration of the preparatory (preceding the use of the innovation) stages of the lifecycle. To ensure the flexibility of the MLOps process and its components, it is advisable to use a combination of open-source tools and corporate solutions. To automate and standardize processes throughout the entire lifecycle of a machine learning model (from development and testing to deployment, monitoring and management), the MLOps methodology is used, the maturity level of which significantly determines the duration of the stages of the digital innovation life cycle. 10.18721/JE.18506 330.34 digital innovation artificial intelligence life cycle machine learning MLOps digitalization https://economy.spbstu.ru/article/2025.115.6/ 06_vertakova_shulgina_sobirov.pdf

RAR RUS 100-112 Barsegyan Naira Shinkevich. Aleksey Artificial intelligence as a key tool for the development of naturelike technologies The relevance of this study is driven by the acute need to ensure Russia’s technological sovereignty and economic resilience in the context of global environmental challenges and unprecedented sanctions pressure. The response to these systemic risks is a transition to a new paradigm of economic development based on the deep integration of artificial intelligence (AI) and nature-like technologies (NLT). This combination is considered a key driver for overcoming resource dependency, enhancing competitiveness and transitioning to a circular economy model. The aim of the research is to systematize and analyze the key roles and functions of AI as a catalyst and integration platform for implementing the principles of NLT across various economic sectors. The methodological framework of the study involves a comprehensive approach, combining methods of systemic and comparative analysis, as well as economic and statistical processing of data from official sources (Rosstat, the Russian Ministry of Digital Development) and industry analytics from RAEC. As a result of the study, a structural model is proposed, demonstrating the synergistic effects of integrating AI and NLT. At the micro-level, a 15–30% reduction in corporate operating costs through the optimization of resource consumption is confirmed. At the meso-level, the potential for the formation of new secondary resource markets with a volume of up to 3–5% of the GRP is identified. At the macro-level, a 15–20% reduction in the GDP energy intensity is forecasted, which will directly enhance the country’s economic security. The significant market potential of the AI–NLT sector is assessed, with projections indicating growth from 158 billion rubles in 2024 to 450–600 billion rubles by 2030. Key barriers hindering development have been identified: a structural personnel shortage (25–30 thousand specialists annually), critical wear and tear of fixed assets (45–60%) and a chronic lack of venture financing. It is concluded that the integration of AI and NLT is forming a new technological and economic paradigm, and their accelerated development is an unconditional strategic imperative for diversifying the economy and ensuring the long-term technological sovereignty of the Russian Federation. 10.18721/JE.18507 004.8 artificial intelligence nature-like technologies sustainable development technological sovereignty digital ecosystem industry 5.0 https://economy.spbstu.ru/article/2025.115.7/ 07_barsegyan_shinkevich.pdf

RAR RUS 113-129 57222509570 0000-0002-1801-4326 Tashkent state university of economics Makhmudova Guljahon guljaxon0038@gmail.com Azizov Abbos Digitalization of state asset management in ensuring economic security (the case of the republic of Uzbekistan) In the context of globalization and the accelerated development of digital technologies, ensuring the economic security of states is acquiring strategic importance. State assets, including natural resources, enterprises, infrastructure and other assets, play a vital role in maintaining the stability of the national economy. However, traditional management mechanisms are often accompanied by low transparency and risks of corruption, limiting their effectiveness. This article examines the prospects for digitalization of state asset management processes as a tool for improving the economic security of the Republic of Uzbekistan. The goal of the study is to identify opportunities for strengthening economic security through the introduction of digital technologies into the asset management system. The key objectives are: establishing the relationship between digitalization and economic security; analyzing technologies that facilitate management optimization; studying international and national experience in digital transformation; and developing practical recommendations. The object of the study includes state asset management processes in the national economy of the Republic of Uzbekistan. The subject of the study includes digital tools, technologies and mechanisms used in state asset management, as well as their impact on the country’s economic security. The study utilizes literature analysis, comparison, expert assessments, and statistical data. The analysis revealed that blockchain, artificial intelligence, big data, digital twins and cloud computing are key technologies for improving management efficiency, reducing risks and ensuring operational transparency. The experiences of Singapore, China and Estonia have demonstrated the effectiveness of digital platforms in managing state assets. Uzbekistan’s national initiatives, such as the “Digital Uzbekistan 2030” strategy and the E-Auksion electronic platform, confirm the high potential of digitalization. The recommendations include the creation of a unified digital platform, infrastructure development (5G and data centers), personnel training and improvement of the legal framework. The study’s findings demonstrate that the comprehensive implementation of digital technologies is the key to strengthening economic security and sustainable development. 10.18721/JE.18508 338.24.021.8 (575.1) state asset management digitalization digital transformation economic security public administration e-government https://economy.spbstu.ru/article/2025.115.8/ 08_mahmudova_azizov.pdf

RAR RUS 130-148 Ambartsumyan Anastas Digital transformation of the Uzbek economy: results and prospects The issue of improving the efficiency of production and management processes is becoming increasingly significant t in the context of the introduction of extensive digitalization opportunities and artificial intelligence (AI) elements into the country’s economy. The aim of this work is to study the state and prospects of the digital transformation of the economy of Uzbekistan. This goal predetermined the objectives of the study, which consist in collecting, systematizing and analyzing available sources of scientific and statistical information, formulating results and determining directions for further research on this problem. In this regard, the subject of the study is the digital transformation of the country’s economy. The author used such research methods as analysis, systematization and generalization of materials published on this topic; comparative analysis of statistical data; synthesis of identified points of view, research results and, based on them, formulation of promising areas for the introduction of AI into the country’s economy. Within the framework of the presented research, some provisions of regulatory documents, relevant scientific publications, analytical data, certain indicators of the level of implementation of AI, basic primary sources from statistical authorities were used. The main results of the study, as well as directions for further research on this topic, are presented. At present, the development of advanced software aimed at enhancing the efficiency of production process management is becoming increasingly relevant. Beyond addressing operational tasks, such systems are expected to facilitate collaboration among the structural divisions of enterprises and support the resolution of issues arising at the intersection of their areas of responsibility. Within the digital environment, users are able to exchange data, knowledge and problem-solving ideas. Industrial enterprises are demonstrating steadily growing interest in such solutions. The results indicate that AI is emerging as a key driver of transformation within modern economic systems. Its integration into production processes, the service sector and public administration carries considerable scientific and practical significance, creating opportunities to improve efficiency, optimize resource utilization and generate new markets. AI has undergone an evolution from narrowly specialized algorithms to sophisticated systems capable of self-learning and decision-making under conditions of uncertainty. Countries that succeed in combining market-based management tools with effective state regulation will secure a decisive advantage in the global competition of the 21st century. 10.18721/JE.18509 330.3 artificial intelligence digital economy national economy investments regulations technologies services information big data https://economy.spbstu.ru/article/2025.115.9/ 09_ambartsumyan.pdf