voprstatВопросы статистикиVoprosy Statistiki2313-63832658-5499The Federal State Budgetary Institution "Scientific Research Institute for Socio-Economic Statistics of the Federal State Statistics Service" (Statistics Research Institute of Rosstat)10.34023/2313-6383-2025-32-5-7-17voprstat-1989Research ArticleВОПРОСЫ МЕТОДОЛОГИИISSUES OF METHODOLOGYМониторинг разработки и применения технологий искусственного интеллекта: основные методологические подходыMonitoring the Development and Application of Artificial Intelligence Technologies: Key Methodological Approacheshttps://orcid.org/0000-0003-2957-2291АбашкинВ. Л.AbashkinV. L.

Абашкин Василий Львович – канд. экон. наук, главный эксперт, Центр статистики и мониторинга информационного общества и цифровой экономики, Институт статистических исследований и экономики знаний

101000, г. Москва, ул. Мясницкая, д. 11

Vasily L. Abashkin – Cand. of Sci. (Econ.), Chief Expert, Centre for Statistics and Monitoring of Information Society and Digital Economics, Institute for Statistical Studies and Economics of Knowledge

11, Myasnitskaya Str., Moscow, 101000

vabashkin@hse.ruhttps://orcid.org/0009-0000-6267-3209СахноМ. К.SakhnoM. K.

Сахно Михаил Камоевич – стажер-исследователь, лаборатория исследований науки и технологий международного научно-образовательного Форсайт-центра, Институт статистических исследований и экономики знаний

101000, г. Москва, ул. Мясницкая, д. 11

Mikhail K. Sakhno – Research Assistant, Laboratory for Science and Technology Studies, International Research and Educational Foresight Centre, Institute for Statistical Studies and Economics of Knowledge

11, Myasnitskaya Str., Moscow, 101000

msahno@hse.ruhttps://orcid.org/0000-0002-5833-4332АбдрахмановаГ. И.AbdrakhmanovaG. I.

Абдрахманова Гульнара Ибрагимовна – канд. экон. наук, директор Центра статистики и мониторинга информационного общества и цифровой экономики, Институт статистических исследований и экономики знаний

101000, г. Москва, ул. Мясницкая, д. 11

Gulnara I. Abdrakhmanova – Cand. of Sci. (Econ.), Director, Centre for Statistics and Monitoring of Information Society and Digital Economics, Institute for Statistical Studies and Economics of Knowledge

11, Myasnitskaya Str., Moscow, 101000

gabdrakhmanova@hse.ruНациональный исследовательский университет «Высшая школа экономики»РоссияNational Research University Higher School of Economics (HSE University)Russian Federation202504112025325717Copyright © Абашкин В.Л., Сахно М.К., Абдрахманова Г.И., 20252025Абашкин В.Л., Сахно М.К., Абдрахманова Г.И.Abashkin V.L., Sakhno M.K., Abdrakhmanova G.I.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://voprstat.elpub.ru/jour/article/view/1989

В статье рассматриваются методологические подходы к организации и проведению комплексного статистического мониторинга разработки и применения технологий искусственного интеллекта (ИИ). Актуальность темы обусловлена высокой значимостью технологий ИИ для экономики и общества и признанием их одними из ведущих технологий текущего десятилетия в России и мире. Для комплексной оценки развития ИИ необходима надежная и апробированная методология исследования.

На основе анализа существующих подходов к наблюдению за ИИ, включая тематику исследований, доступные источники и методы получения данных, применяемых в международной и отечественной практике, разработаны понятийный аппарат, классификаторы технологий ИИ и связанных с ними товаров и услуг, концептуальная модель мониторинга и система показателей. Предложенный подход к ведению мониторинга был апробирован в 2023–2024 гг. в ходе специализированных обследований организаций и вузов. Новый инструментарий сбора сведений об ИИ внедрен в практику федерального статистического наблюдения.

По мнению авторов, реализация мониторинга позволит получать количественные и качественные характеристики создания, распространения и перспектив развития технологий ИИ в отраслях экономики и социальной сферы, а также оценивать эффекты от их внедрения.

The article presents methodological approaches to organizing and conducting comprehensive statistical monitoring of the development and application of artificial intelligence (AI) technologies. The relevance of this topic is driven by the high significance of AI technologies for the economy and society, and their recognition as one of the leading technologies of the current decade, both globally and in Russia. A comprehensive assessment of AI development requires a robust and well-developed research methodology.

Based on the analysis of existing approaches to AI observation, including research topics in this field, available data sources, and data collection methods used in international and domestic practices, the study has developed a terminological framework, classifiers for AI technologies and related goods and services, a conceptual model of monitoring, and a system of indicators. The proposed monitoring approach was tested in 2023–2024 during specialized surveys of organizations and universities. The new AI data collection toolkit has been officially implemented in the federal statistical observation system.

According to the authors, the implementation of monitoring will enable the acquisition of quantitative and qualitative characteristics regarding the creation, diffusion, and future prospects of AI technologies across economic sectors and social domains, as well as facilitate the assessment of the effects of their implementation.

искусственный интеллект (ИИ)мониторингстатистикасистема показателейфедеральное статистическое наблюдениеartificial intelligence (AI)monitoringstatisticssystem of indicatorsfederal statistical observationСтатья подготовлена в рамках Программы фундаментальных исследований Национального исследовательского университета «Высшая школа экономики». Авторы выражают признательность коллегам из Института статистических исследований и экономики знаний НИУ ВШЭ, участвовавшим в разработке методологии по отдельным направлениям мониторинга искусственного интеллекта.The article was prepared within the framework of the Basic Research Program of the HSE University. The authors thank their colleagues from the Institute for Statistical Studies and Economics of Knowledge at HSE University for their contributions to developing the methodology for specific areas of artificial intelligence monitoring.ReferencesRashid A., Kausik A. AI Revolutionizing Industries Worldwide: A Comprehensive Overview of its Diverse Applications // Hybrid Advances. 2024. Vol. 7. Article 100277. doi: https://doi.org/10.1016/j.hybadv.2024.100277.Rashid A., Kausik A. AI Revolutionizing Industries Worldwide: A Comprehensive Overview of its Diverse Applications. Hybrid Advances. 2024;7:Article 100277. Available from: https://doi.org/10.1016/j.hybadv.2024.100277.Gil D. et al. AI for Management: An Overview // Canals J., Heukamp F. (eds) The Future of Management in an AI World. Ch. 1. Cham: Palgrave Macmillan, 2020. 254 p. (IESE Business Collection) P. 3–19. doi: https://doi.org/10.1007/978-3-030-20680-2_1.Gil D. et al. AI for Management: An Overview. In: Canals J., Heukamp F. (eds) The Future of Management in an AI World. Ch. 1. Cham: Palgrave Macmillan; 2020. 254 p. (IESE Business Collection) P. 3–19. Available from: https://doi.org/10.1007/978-3-030-20680-2_1.Qudus L. Leveraging Artificial Intelligence to Enhance Process Control and Improve Efficiency in Manufacturing Industries // International Journal of Computer Applications Technology and Research. 2025. Vol. 14. Iss. 2. P. 18–38. doi: https://doi.org/10.7753/IJCATR1402.1002.Qudus L. Leveraging Artificial Intelligence to Enhance Process Control and Improve Efficiency in Manufacturing Industries. International Journal of Computer Applications Technology and Research. 2025;14(2):18–38. Available from: https://doi.org/10.7753/IJCATR1402.1002.Hoyer W.D. et al. Transforming the Customer Experience Through New Technologies // Journal of Interactive Marketing. 2020. Vol. 51. Iss. 1. P. 57–71. doi: https://doi.org/10.1016/j.intmar.2020.04.001.Hoyer W.D. et al. Transforming the Customer Experience Through New Technologies. Journal of Interactive Marketing. 2020;51(1):57–71. Available from: https://doi.org/10.1016/j.intmar.2020.04.001.Bilgram V., Laarmann F. Accelerating Innovation with Generative AI: AI-Augmented Digital Prototyping and Innovation Methods // IEEE Engineering Management Review. 2023. Vol. 51. No. 2. P. 18–25. doi: https://doi.org/10.1109/EMR.2023.3272799.Bilgram V., Laarmann F. Accelerating Innovation with Generative AI: AI-Augmented Digital Prototyping and Innovation Methods. IEEE Engineering Management Review. 2023;51(2):18–25. Available from: https://doi.org/10.1109/EMR.2023.3272799.Lane M. Who Will Be the Workers Most Affected by AI?: A Closer Look at the Impact of AI on Women, Low-Skilled Workers and Other Groups // OECD Artificial Intelligence Papers, No. 26. Paris: OECD Publ., 2024. 60 p. doi: https://doi.org/10.1787/14dc6f89-en.Lane M. Who Will Be the Workers Most Affected by AI?: A Closer Look at the Impact of AI on Women, Low-Skilled Workers and Other Groups. OECD Artificial Intelligence Papers, No. 26. Paris: OECD Publ.; 2024. 60 p. Available from: https://doi.org/10.1787/14dc6f89-en.Webb M. The Impact of Artificial Intelligence on the Labor Market // SSRN Electronic Journal. Stanford University, 2019. 61 p. doi: https://doi.org/10.2139/ssrn.3482150.Webb M. The Impact of Artificial Intelligence on the Labor Market. SSRN Electronic Journal. Stanford University, 2019. 61 p. Available from: https://doi.org/10.2139/ssrn.3482150.Yu J., Qi C. The Impact of Generative AI on Employment and Labor Productivity // Review of Business. 2024. Vol. 44. No. 1. P. 53–67. URL: https://www.stjohns.edu/sites/default/files/2024-02/Review-of-Business-44%281%29-Jan-2024.pdf.Yu J., Qi C. The Impact of Generative AI on Employment and Labor Productivity. Review of Business. 2024;44(1):53–67. Available from: https://www.stjohns.edu/sites/default/files/2024-02/Review-of-Business-44%281%29-Jan-2024.pdf.Sundari S. et al. Artificial Intelligence (AI) and Automation in Human Resources: Shifting the Focus from Routine Tasks to Strategic Initiatives for Improved Employee Engagement // East Asian Journal of Multidisciplinary Research. 2024. Vol. 3. No. 10. P. 5041–5054. doi: https://doi.org/10.55927/eajmr.v3i10.11758.Sundari S. et al. Artificial Intelligence (AI) and Automation in Human Resources: Shifting the Focus from Routine Tasks to Strategic Initiatives for Improved Employee Engagement. East Asian Journal of Multidisciplinary Research. 2024;3(10):5041–5054. Available from: https://doi.org/10.55927/eajmr.v3i10.11758.OECD. Govering with Artificial Intelligence: Are Go vernments Ready? // OECD Artificial Intelligence Papers, No. 20. Paris: OECD Publ., 2024. 31 p. doi: https://doi.org/10.1787/26324bc2-en.OECD. Governing with Artificial Intelligence: Are Governments Ready? OECD Artificial Intelligence Papers, No. 20. Paris: OECD Publ.; 2024. 31 p. Available from: https://doi.org/10.1787/26324bc2-en.Galindo L., Perset K., Sheeka F. An Overview of National AI Strategies and Policies // OECD Going Digital Toolkit Notes, No. 14. Paris: OECD Publ., 2021. 26 p. doi: https://doi.org/10.1787/c05140d9-en.Galindo L., Perset K., Sheeka F. An Overview of National AI Strategies and Policies. OECD Going Digital Toolkit Notes, No. 14. Paris: OECD Publ.; 2021. 26 p. Available from: https://doi.org/10.1787/c05140d9-en.Ли Я. Нормативно-правовое регулирование генеративного искусственного интеллекта в Великобритании, США, Европейском союзе и Китае // Право. Журнал Высшей школы экономики. 2023. Т. 16. № 3. С. 245–267. doi: https://doi.org/10.17323/2072-8166.2023.3.245.267.Li Y. Specifics of Regulatory and Legal Regulation of Generative Artificial Intelligence in the UK, USA, EU and China. Law Journal of the Higher School of Economics. 2023;16(3):245–267. (In Russ.) Available from: https://doi.org/10.17323/2072-8166.2023.3.245.267.Рыбак О.П. Искусственный интеллект как объект статистического изучения // Вопросы статистики. 2020. Т. 27. № 2. С. 34–47. doi: https://doi.org/10.34023/2313-6383-2020-27-2-34-47.Rybak O.P. Artificial Intelligence as an Object of Statistical Study. Voprosy Statistiki. 2020;27(2):34–47. (In Russ.) Available from: https://doi.org/10.34023/2313-6383-2020-27-2-34-47.Turovets J., Vishnevskiy K., Altynov A. How to Measure AI: Trends, Challenges and Implications // Higher School of Economics Research Paper. WP BRP 116/ STI/2020. doi: http://doi.org/10.2139/ssrn.3736851.Turovets J., Vishnevskiy K., Altynov A. How to Measure AI: Trends, Challenges and Implications. Higher School of Economics Research Paper. WP BRP 116/STI/2020. Available from: http://doi.org/10.2139/ssrn.3736851.European Commission. Technology Monitoring and Assessment – Comparing EU, US and Chinese Approaches. Luxembourg: Publ. Office of the EU, 2025. doi: https://doi.org/10.2777/0772224.European Commission. Technology Monitoring and Assessment – Comparing EU, US and Chinese Approaches. Luxembourg: Publ. Office of the EU; 2025. Available from: https://doi.org/10.2777/0772224.Земцов С.П. Потенциал создания и внедрения технологий искусственного интеллекта в регионах России // Региональные исследования. 2024. T. 1. № 83. С. 34–47. doi: https://doi.org/10.5922/1994-5280-2024-1-3.Zemtsov S.P. Potential for the Creation and Implementation of Artificial Intelligence Technologies in the Regions of Russia. Regional Research. 2024;1(83):34–47. (In Russ.) Available from: https://doi.org/10.5922/1994-5280-2024-1-3.OECD. Measuring the Digital Economy: A New Perspective. Paris: OECD Publ., 2014. 156 p. doi: https://doi.org/10.1787/9789264221796-en.OECD. Measuring the Digital Economy: A New Perspective. Paris: OECD Publ.; 2014. 156 p. Available from: https://doi.org/10.1787/9789264221796-en.UNCTAD. Manual for the Production of Statistics on the Digital Economy. 2020. Rev. Ed. Geneva: United Nations, 2021. 224 p. URL: https://unctad.org/publication/manual-production-statistics-digital-economy-2020.UNCTAD. Manual for the Production of Statistics on the Digital Economy. 2020. Rev. Ed. Geneva: United Nations; 2021. 224 p. Available from: https://unctad.org/publication/manual-production-statistics-digital-economy-2020.Montagnier P., Ek I. AI Measurement in ICT Usage Surveys: A Review // OECD Digital Economy Papers, No. 308. Paris: OECD Publ., 2021. 50 p. doi: https://doi.org/10.1787/72cce754-en.Montagnier P., Ek I. AI Measurement in ICT Usage Surveys: A Review. OECD Digital Economy Papers, No. 308. Paris: OECD Publ.; 2021. 50 p. Available from: https://doi.org/10.1787/72cce754-en.Eurostat. Eurostat Model Questionnaire for the EU Survey on ICT Usage and E-Commerce in Enterprises 2025. Eurostat G4, 2025. URL: https://circabc.europa.eu/ui/group/89577311-0f9b-4fc0-b8c2-2aaa7d-3ccb91/library/c111b59a-8976-418f-afeb-f89c7a6003b9/details.Eurostat. Eurostat Model Questionnaire for the EU Survey on ICT Usage and E-Commerce in Enterprises 2025. Eurostat G4, 2025. Available from: https://circabc.europa.eu/ui/group/89577311-0f9b-4fc0-b8c2-2aaa7d3ccb91/library/c111b59a-8976-418f-afeb-f89c7a6003b9/details.Мониторинг разработки и применения технологий искусственного интеллекта: методологическое руководство / под ред. Л.М. Гохберга, П.Б. Рудника, Г.И. Абдрахмановой. М.: ИСИЭЗ ВШЭ, 2025. 244 с. doi: https://doi.org/10.17323/978-5-7598-3111-2.Gokhberg L.M., Rudnik P.B., Abdrakhmanova G.I. (eds) Monitoring of the Development and Usage of Artificial Intelligence Technologies: A Methodological Guide. Moscow: HSE ISSEK; 2025. 244 p. (In Russ.) Available from: https://doi.org/10.17323/978-5-7598-3111-2.Искусственный интеллект в России: разработка и применение / под ред. Л.М. Гохберга, П.Б. Рудника, Г.И. Абдрахмановой. М.: ИСИЭЗ ВШЭ, 2025. 86 с. doi: https://doi.org/10.17323/978-5-7598-3038-2.Gokhberg L.M., Rudnik P.B., Abdrakhmanova G.I. (eds) Artificial Intelligence in Russia: Development and Usage. Moscow: HSE ISSEK; 2025. 86 p. (In Russ.) Available from: https://doi.org/10.17323/978-5-7598-3038-2.Шугаль Н.Б., Варламова Т.А. Подготовка высококвалифицированных кадров в области искусственного интеллекта / под науч. ред. Л.М. Гохберга. М.: ИСИЭЗ ВШЭ, 2024. 60 с. doi: https://doi.org/10.17323/978-5-7598-3024-5.Schugal N.B., Varlamova T.A. The Development ofHighly Qualified Workforce in the Field of AI. Gokhberg L.M. (ed.) Moscow: HSE ISSEK; 2024. 60 p. (In Russ.) Available from: https://doi.org/10.17323/978-5-7598-3024-5.

The authors declare that there are no conflicts of interest present.