ARTIFICIAL INTELLIGENCE IN THE SYNCHRONIZATION OF CROSS-CUTTING SOCIO-ECONOMIC AND SCIENTIFIC-TECHNOLOGICAL INDICATORS OF RUSSIA'S STRATEGIC DEVELOPMENT
Problem. Despite the principle of balanced strategic planning documents enshrined in Federal Law No. 172-FZ of June 28, 2014, "On Strategic Planning in the Russian Federation," the integration of cross-sectoral socio-economic (SE) and scientific-technological development (ST) data appears highly fragmented. The Russian system lacks a unified information base for end-to-end analytics. This fragmentation limits the completeness, comparability, and timeliness of management information, leads to indicator duplication, and hinders comprehensive multi-level forecasting. Methodology. The research was conducted within the framework of an institutional-analytical approach and normative-legal analysis of legislation, relying on the theory of multi-level governance. Content analysis of strategic documents and bibliographical analysis of global experience in integrating e-government data were applied. This allowed for the systematization of six key indicator groups and the formation of a logical model of cross-cutting indicators for synchronizing SE and ST goals within a unified information loop. Research Results. A conceptual approach to applying artificial intelligence technologies for automated processing of cross-cutting data (identifiable based on the typology of cross-cutting indicators) is proposed, built upon the domain architecture of the "Gostech" platform. It is demonstrated that predictive analytics and machine learning algorithms are capable of performing continuous "pulse diagnostics" of the economy, identifying latent interdependencies, and automatically resolving conflicting target values. Practical Application. The developed concept creates a scientific and practical basis for overcoming administrative barriers and forming a unified indicator architecture. Its implementation will ensure seamless interagency interaction, inter-level coordination, and the ability to use intelligent analytical services in government bodies. This is a critically important step towards transitioning from reactive to predictive governance and ensuring national technological sovereignty.
