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The deepening technological gap between industrial regions, the strengthening of external economic constraints and the need to achieve technological sovereignty determine the relevance of the transition to the intellectual format of industrial ecosystems development with a high degree of complexity, dynamic self-organisation and cognitive transformation. The article aims to develop a conceptual framework for assessing and managing the intelligence maturity of industrial ecosystems in the context of data economy and institutional transformation. Methodologically, the research rests on ontological and system-structural approaches, and the synthesis of the concepts related to Industries 5.0–6.0, cognitive economics, digital sovereignty and maturity models. The methods include narrative analysis, comparative typology, normalisation of indicators, index modelling and expert calibration of weighting coefficients. The evidence base is the data of analytical materials prepared by the National Center for the Development of Artificial Intelligence under the Government of the Russian Federation for 2023–2024. The study has formed a conceptual framework including: 1) a multi-level maturity typology reflecting the evolution of ecosystems from basic digitalisation to institutionalised intellectuality; 2) a categorical model for assessing the intelligence maturity of industrial ecosystems with the calculation of a composite index; 3) a system of criteria and indicators for eleven dimensions of assessment; 4) a method for stratification and ranking of industrial ecosystems by the level of intelligence maturity; 5) management functions of the framework as a tool for strategic planning, monitoring and cognitive transformation. The framework provides a structured platform for standardising methods for assessing and managing the intelligence maturity of industrial ecosystems, creating a basis for institutionalising AI, shaping development programmes and transitioning to adaptive, reflective and strategically managed industrial ecosystems.
Глухов et al. (Wed,) studied this question.