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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">ecr-journal</journal-id><journal-title-group><journal-title xml:lang="ru">Экономическая наука современной России</journal-title><trans-title-group xml:lang="en"><trans-title>Economics of Contemporary Russia</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1609-1442</issn><issn pub-type="epub">2618-8996</issn><publisher><publisher-name>Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.33293/1609-1442-2026-29(1)-123-136</article-id><article-id custom-type="edn" pub-id-type="custom">NPAPMB</article-id><article-id custom-type="elpub" pub-id-type="custom">ecr-journal-1182</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЭКОНОМИЧЕСКАЯ ПОЛИТИКА И ХОЗЯЙСТВЕННАЯ ПРАКТИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>ECONOMICAL POLICY AND ECONOMICAL PRACTICE</subject></subj-group></article-categories><title-group><article-title>Оценка интеллектуальной зрелости промышленных систем: интеграция концепций и инструментов</article-title><trans-title-group xml:lang="en"><trans-title>Assessing the intellectual maturity of industrial systems: integration of concepts and  tools</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2429-2779</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Гилева</surname><given-names>Татьяна Альбертовна</given-names></name><name name-style="western" xml:lang="en"><surname>Gileva</surname><given-names>Tatiana A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>доктор экономических наук</p></bio><bio xml:lang="en"><p>Dr. Sci. (Economic)</p></bio><email xlink:type="simple">t-gileva@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7252-6058</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Бабкин</surname><given-names>Иван Александрович</given-names></name><name name-style="western" xml:lang="en"><surname>Babkin</surname><given-names>Ivan A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>кандидат экономических наук</p></bio><bio xml:lang="en"><p>Cand. Sci. (Economic)</p></bio><email xlink:type="simple">babkin_ivan@mail.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург; &#13;
Финансовый университет при Правительстве Российской Федерации, Москва</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Peter the Great St. Petersburg Polytechnic University, Saint Petersburg; &#13;
Financial University under the Government of the Russian Federation, Moscow</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Peter the Great St. Petersburg Polytechnic University, Saint Petersburg</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>14</day><month>03</month><year>2026</year></pub-date><volume>29</volume><issue>1</issue><fpage>123</fpage><lpage>136</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Гилева Т.А., Бабкин И.А., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Гилева Т.А., Бабкин И.А.</copyright-holder><copyright-holder xml:lang="en">Gileva T.A., Babkin I.A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.ecr-journal.ru/jour/article/view/1182">https://www.ecr-journal.ru/jour/article/view/1182</self-uri><abstract><p>Повышение уровня интеллектуализации производства является одним из трендов и стратегическим приоритетом современного промышленного развития. Управление развитием интеллектуального производства требует соответствующих инструментов, одним из которых является модель оценки зрелости. Исследования в области определения интеллектуальной зрелости ведутся на протяжении нескольких десятилетий в рамках различных научных направлений. Поэтому целью данной статьи является формирование методического подхода к оценке интеллектуальной зрелости промышленных систем в экономике данных на основе анализа и интеграции различных концепций и инструментов оценки. Показаны эволюция и особенности различных подходов и моделей оценки интеллектуальной зрелости: концепций интеллектуального капитала и управления знаниями, моделей оценки зрелости Индустрии Х.0, интеллектуального производства, искусственного интеллекта, перспектив развития промышленных метавселенных и экосистем. Выделены наиболее распространенные направления оценки в рамках каждого из подходов. Обоснована целесообразность построения модели интеллектуальной зрелости промышленной системы в формате динамического комплекса субмоделей с возможностью настройки ее структуры в зависимости от контекста конкретного предприятия. В качестве ключевого контекстного фактора, определяющего приоритетные направления и глубину проведения оценки по каждому из них, выделены стратегические цели предприятия. Сформулированы специфические принципы оценки интеллектуальной зрелости: стратегичности, иерархичности, преемственности, интеграции, избыточности, фасетного выбора, контекстной интеллектуализации проектирования, квантификации, конвергенции и проактивности. Предложена концептуальная модель оценки интеллектуальной зрелости промышленных систем.</p></abstract><trans-abstract xml:lang="en"><p>Increasing the level of production intellectualization is a current trend and a strategic priority of modern industrial development. Managing the development of intellectual production requires appropriate tools, one of which is a maturity assessment model. Research for defining intellectual maturity was conducted for several decades within various scientific disciplines. Therefore, the aim of this article is to develop a methodological approach for assessing the intellectual maturity of industrial systems in the data economy, based on the analysis and integration of various assessment concepts and tools. The evolution and specific features of various approaches and models for assessing intellectual maturity are presented: concepts of intellectual capital and knowledge management, maturity assessment models for Industry X.0, smart manufacturing, artificial intelligence, prospects for the development of industrial metaverses and ecosystems. The most common assessment areas within each approach are identified. The feasibility of building a model of an industrial system’s intellectual maturity in the form of a dynamic complex of sub-models is substantiated, allowing for its structure to be customized depending on the context of a specific enterprise. The strategic goals of the enterprise are highlighted as a key contextual factor determining the priority areas and depth of assessment for each of them. Specific principles for assessing intellectual maturity are formulated: strategic alignment, hierarchy, continuity, integration, redundancy, faceted choice, contextual intellectualization of design, quantification, convergence, and proactivity. A conceptual model for assessing the intellectual maturity of industrial systems is proposed.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>интеллектуальная зрелость</kwd><kwd>модель оценки</kwd><kwd>промышленная экосистема</kwd><kwd>интеллектуальное производство</kwd><kwd>искусственный интеллект</kwd><kwd>управление знаниями</kwd><kwd>методический подход</kwd></kwd-group><kwd-group xml:lang="en"><kwd>intellectual maturity</kwd><kwd>assessment model</kwd><kwd>industrial ecosystem</kwd><kwd>smart manufacturing</kwd><kwd>artificial intelligence</kwd><kwd>knowledge management</kwd><kwd>methodological approach</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Исследование выполнено при финансовой поддержке РНФ в рамках научного проекта № 25-18-00978.</funding-statement><funding-statement xml:lang="en">This study was supported by the Russian Science Foundation under research project No. 25-18-00978.</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Бабкин А.В., Михайлов П.А. и др. 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