Rapidly developing artificial intelligence (AI) technologies are penetrating into all areas of society's life and being integrated into management processes. The speed of their development, self-learning, and the range of applications are astonishing. AI technologies are becoming a part of everyday life and a prerequisite for leadership and efficiency in business, politics, science, and education, including at the international level.

The consequences of such a rapid and widespread application of AI have not yet been fully studied and understood, and the introduction of AI technologies in the management of the state, which is a special social institution designed to ensure stability and regulation in society, is of particular interest in understanding the subsequent changes. Mistakes in governance can have catastrophic consequences.

This predetermined the purpose of the article, which is to examine the process of introducing artificial intelligence into the work of the state in the context of its institutional specifics. To achieve this goal, the article uses the methodology of institutional and activity-based approaches, as well as a sociological understanding of the uniqueness of AI technologies, which allows us to examine the structural, regulatory, and legal aspects of introducing AI into public administration and the daily practices of civil servants, as well as to highlight the existing risks associated with this process.

This article serves as a preparatory study for a research project aimed at identifying the most likely socio-political and institutional changes associated with the implementation of artificial intelligence (AI) in electronic government services in Russia. The methodology is based on neoinstitutional and network approaches, as well as principles of rational choice theory. This allows for the analysis of formal and informal rules, coordination between actors, and the motivations behind their behavior. The source material includes publications from the Russian Science Citation Index (RSCI), Scopus, WoS, and IEEE databases, government policy documents, and data on
AI implementation in various sectors. Particular attention is paid to examining the benefits, risks, and changes associated with the ongoing integration of AI into government services. The reviewed cases of new technology implementation demonstrate significant potential for reforming public administration, improving service efficiency, and improving communication between authorities and citizens. Significant risks associated with the implementation of AI in electronic government services are highlighted. The analysis demonstrates that the successful implementation of AI can be ensured by a balanced strategy that considers security, transparency, and the ability to trust technology. This article presents the interim results of a research project aimed at identifying digital behavior strategies for specific age groups. Younger and middle-aged generations fear the replacement of humans by AI tools, while older generations are unprepared for digital transformation. Based on the identified trends and scenarios for the implementation of AI tools in electronic services, a source study and methodological framework for the upcoming research project has been developed. 

The purpose of this study is to analyze the current state of legal regulation of the assessment of the effectiveness and efficiency of the technological policy of the Russian Federation in the field of artificial intelligence development and to develop proposals for the formation of a comprehensive system of such assessment to improve the effectiveness of public administration tools.

The research methods include the formal legal method, which was used to analyze the system of normative legal regulation both in the field of technological policy in general and in relation to public administration tools for the development of artificial intelligence, a comparative legal analysis of existing and planned elements of the assessment of effectiveness and efficiency in relation to technological policy in general and the development of artificial intelligence in particular, as well as the method of legal modeling for the development of appropriate recommendations for the development of the said assessment of effectiveness and efficiency.

The results of the study showed that the current legal regulation does not contain a unified approach to assessing the effectiveness and efficiency of measures of state support and stimulation of technological development in general and the development of artificial intelligence, in particular, aimed at achieving the final socially significant result of such development (technological leadership of Russia, including in the field of artificial intelligence). Terminological uncertainty and the prevalence of the assessment of the implementation of planned activities over the assessment of their real contribution to the achievement and ensuring of technological sovereignty and technological leadership of Russia were revealed.

The conclusions of the study are that in order to achieve and ensure technological sovereignty and technological leadership of Russia, including in the field of artificial intelligence development, the implementation of public administration tools and measures (stimulation and support of development) should be conditioned by the introduction and application of a systemic assessment of the effectiveness and efficiency of such tools and measures, including the appropriate terminology, indicators and assessment methods. A set of proposals for improving the draft regulatory legal acts in this area, developed by the Ministry of Economic Development of Russia in 2025, is substantiated.

The article examines the role of AI in the military and management spheres, as well as the institutional changes that are taking place due to the increased use of autonomous digital platforms. The relevance of the topic is justified by the dynamic growth of the potential for the use of intelligent systems that radically change decision-making mechanisms, ways of allocating responsibility and management models in armed conflicts of the present time. The changes in the structures of confrontation resulting from the introduction of self-learning platforms are also discussed in detail.
The purpose of the study is to identify the mechanisms and analyze the consequences of the transformation of military strategies associated with the transition from classical forms of warfare to practices based on the use of autonomous digital platforms. The paper uses an institutional method to analyze the institutional changes taking place under the influence of the introduction of AI. The application of the system method allowed us to consider AI systems as an integral part of the modern world, in which the separate existence of man and machine is no longer possible. It is noted that the large-scale introduction of autonomous digital platforms generates diverse risks not only of a technical, but also of an anthropological and existential nature. The problem of responsibility for the results of decisions taken, the role of a person (including a controlling one) in a changing world, and the legal provision of a transforming reality are considered. At the same time, the potential of autonomous digital systems is still the subject of serious discussion, since its boundaries have not been defined. The author comes to the conclusion that the human role should not be nominal, the operator should always be able not only to monitor the activities of AI systems, but also to interpret the result of the decisions made and, if necessary, have the authority to correct them. It is obvious that the transition of AI from auxiliary functionality to the leading role of an autonomous developer of decision scenarios radically changes the system of relationships between humans and computing systems, so it needs to be adapted to modern management decision-making algorithms, as well as to ensure the proportionality of social dynamics, public expectations and the direction of institutional transformations. 

This study examines the institutional and technological challenges of integrating artificial intelligence (AI) systems into public administration and governmental services, focusing on the taxonomy of algorithmic roles in decision-making, the balance of interests in cooperation with commercial AI providers and infrastructure actors, and the safeguarding of national technological sovereignty. A qualitative interdisciplinary approach is applied, combining regulatory and legal analysis, thematic examination of empirical cases across different countries, and theoretical synthesis. Data were collected from official documents, peer-reviewed publications, and news sources, using snowball sampling for case selection and iterative coding for analytical categorization. The research develops a six-tier pyramidal model of accountability distribution according to the degree of algorithmic autonomy in decision-making chains: from full delegation («AI as Captain»), provision of ready-made solutions for human approval («AI as Navigator»), configuration of option sets («AI as Adviser»), environmental analysis with trigger signaling («AI as Observer»), execution of labor-intensive tasks under operator supervision («AI as Workforce»), to routine operational support without decision-making capacity («AI as Routine Assistant»). The model is mapped against risk gradations (high, limited, minimal) to assess error consequences.
The findings reveal the dilemma of public-private partnerships, which facilitate access to innovation but simultaneously reinforce dependence and systemic vulnerabilities. The study also substantiates the role of sovereign AI as a strategic response to these risks. For effective integration of AI into governmental services, it recommends mandatory classification of systems by autonomy and criticality levels. The proposed six-level taxonomy enables a differentiated approach to accountability allocation, reducing institutional gaps and risks of bias, while enhancing resilience and strategic security. 

The aim of the study is to answer the question of how the data economy is changing urban governance, moving from traditional models to data-based approaches, where smart cities become data centers, and data is a key resource for making informed decisions, optimizing processes, and improving the quality of life of citizens. The relevance of this topic is due to the need to improve the effectiveness of data-driven management and proactive provision of services. The research uses such methods as comparative analysis, generalization, methods of scien- tometric analysis and qualitative content analysis of scientific publications.
Based on the conducted research, the authors identify the main trends in data-based smart city management, challenges in the field of smart city management and propose measures aimed at reducing existing threats.
Thus, special attention should be paid to the creation of a secure environment for collaborative data processing, the security of urban infrastructure, its protection, as well as the training of qualified personnel capable of taking into account vulnerabilities at the design stage of both the physical and IT infrastructure of a smart city.

This article examines the impact of the development and widespread adoption of artificial intelligence (AI) tools on the education system and labor market in the modern economy. It outlines important research directions related to the development of AI tools and the processes of their integration in «education-labor market system». A comprehensive analysis of the implementation and integration of AI technologies into existing production processes and the implications for the modern education system is presented.
The study aims not only to assess the transformation of teaching practices under the influence of AI but also to examine its indirect impact on the labor market, driven by the fundamental shift in professional competencies in demand due to its widespread adoption.
Methodology and Approaches: the study is based on an analysis of current trends and includes a practical case demonstrating the potential of AI in processing educational content. Particular attention is paid to assessing the risks and limitations associated with the widespread adoption of AI. Results: key trends in the implementation of AI in the education system are identified and systematized, including personalization of learning, automation of teaching, and the emergence of new educational formats and practices. Promising areas for using AI in education are identified, and the associated risks of its implementation are systematically assessed. The transformation of existing professions and changes in the employment structure due to the spread of AI are examined.
Conclusions: while AI has enormous potential to improve people's lives, it is also associated with a deepening digital divide — it can become a barrier for some and a privilege for others, rather than a means of creating an inclusive society. The interaction of AI with the education system as a social institution represents a complex and multifaceted process of its innovative development. The widespread adoption of AI tools in educational processes is not simply a technical modernization, but an institutional transformation affecting all aspects of the education system. The widespread adoption of AI in the world of work impacts the employment structure and professional composition of the workforce, thereby changing the requirements for the education system. 

Decision support systems (DSS) are a promising technology based on artificial intelligence. While such systems are currently used in a number of fields and industries, the legal field remains one of the most challenging to implement.
The purpose of this paper is to analyze the psychological aspects of user-AI interaction within these systems. Based on an analysis of existing models of human-technology interaction and the author's methodology, the study design is presented.
The results of a focus group with experts from executive and judicial authorities (N = 8) are presented: the role of DSS in legal work, and the benefits and concerns associated with using such systems. Parameters relevant for profiling and further adapting systems to specific users are highlighted.
The article also discusses the prospects and issues of implementing DSS in law enforcement practice. 

The state’s shift to electronic formats of service delivery simultaneously expands the formal availability of services and delineates new contours of social differentiation for older citizens. The official discourse accompanying digital transformation not only describes technical changes but also codifies role expectations regarding recipients of social support measures and participants in governance processes, thereby shaping the distribution of rights and responsibilities. The purpose of the study is to determine how normative and strategic documents construct the roles and status of the older generation, and which institutional mechanisms facilitate or hinder their access to electronic services and their participation in governance practices.
The methods comprise qualitative content analysis with elements of frame analysis of a corpus of federal and regional documents from the past seven years; the unit of analysis consisted of textual fragments that explicitly mention older citizens, mechanisms for easing access, and factors of digital differentiation. Coding proceeded across blocks capturing role representations, inclusion measures, and hidden barriers, followed by axial comparison of categories to identify stable linkages between principles of service delivery and actual regulatory requirements.
The results indicate the predominance of the image of the older citizen as a recipient of benefits and a target of service delivery, with weak institutionalization of active roles of participation and partnership; accessibility measures are predominantly compensatory in nature (preservation of offline alternatives, training, user support, and proactive, application-free provision of certain services). In parallel, a “digital by default” regime is being reinforced, identity verification procedures are becoming more complex, familiar face-to-face channels are being reduced, and incentives tied to online submissions are being introduced, which together draw a dividing line within the age group according to levels of digital skills, resources, and support. The conclusions are that, despite a general orientation toward inclusion, digital modernization reproduces a dependent status for the older generation while simultaneously generating new bases of inequality; to mitigate these effects, it is necessary to institutionalize equivalent multichannel service provision, expand practices of genuine participation by older citizens in the design and evaluation of services, and conduct regular audits of hidden barriers to digital interaction. 

The execution of public policy in the realm of economic digital transformation is fundamentally intertwined with the modernization of management systems at the microeconomic level — particularly within industrial and service-oriented manufacturing enterprises. This article aims to pinpoint the principal drivers affecting labor productivity shifts in the digital economy and to explore how a process-based methodology can support the design and deployment of a digital enterprise management platform. The process-based approach serves as the core analytical framework, conceptualizing labor productivity enhancement as an integrated, systemic intervention. In practical terms, this involves creating and implementing novel managerial mechanisms and tools, structured around defined clusters of business processes and their associated quantifiable performance metrics. To illustrate this in practice, the article presents a case study: the «Mobility System» developed by the MOLNET Group — an intelligent automation platform for managing distributed networks of complex engineering infrastructure. This system enables end-to-end orchestration of both primary and auxiliary business processes and generates digital behavioral models of production operations for one of the country’s leading telecommunications providers. A key focus of the article is the synergistic integration of human and machine intelligence within the digital platform — an emerging capability that redefines collaborative labor. This integration leverages dispersion analysis and artificial intelligence techniques to model business processes, construct digital twins, and derive targeted interventions to boost productivity. The study demonstrates that digital enterprise management platforms function as critical bridges between high-level digital transformation strategies and tangible improvements in labor productivity and operational efficiency. They facilitate the adoption of adaptive, process-driven, and human-machine collaborative solutions at the operational tier. The article concludes with an economic evaluation of the anticipated benefits stemming from the deployment of such a digital platform. Methodologically, the research draws upon systems analysis, strategic management theory, and the strategizing methodology pioneered by Academician Vladimir L. Kvint. These frameworks enrich and evolve the traditional process approach, rendering it more agile, intelligent, and forward-looking — particularly in response to the demands of ongoing digital transformation. 

The relevance of this study arises from the limited effectiveness of digital transformation projects in large enterprises, which are often implemented without alignment with strategic development objectives. In the context of the sixth long wave of technological conjuncture as defined by N.D. Kondratiev, large enterprises have already accumulated vast volumes of big data but lack platforms offering tools for transforming the results of data processing into managerial decisions aimed at improving labor productivity—interpreted in this study as a measurable expression of the strategic interests of the enterprise as an object of strategizing. The aim of the study is to develop an intelligent and adaptive model for strategic management of process development, based on the informed selection of priority areas of transformation within the enterprise’s digital platform. The proposed platform architecture is designed to support managerial decision-making in the strategizing of digital process development under conditions of high technological volatility and organizational fragmentation.
The research methodology consists of several stages. At the initial stage, a classification of business processes was developed, reflecting their functional roles within the designed digital control contour. This was followed by an expert assessment of digital maturity based on a structured survey with criteria including automation, integration with corporate information systems, and the formalization of operational regulations. In parallel, retrospective performance data were analyzed to quantitatively assess the contribution of each process group to labor productivity growth.
The results of both assessments were used to position processes within a matrix of strategic significance, representing the relationship between digital maturity and productivity impact. This made it possible to identify priority transformation areas, high-performing processes, lowpotential segments, and optimization zones where targeted digital solutions and organizational adjustments are recommended.
The proposed architecture of the digital strategic management platform builds upon the results of business process segmentation and implements key functions such as goal setting, scenario analysis, performance monitoring, and coordination of transformation participants. The classification of business processes provides a structural foundation for aligning strategic objectives with the current operational tasks of the enterprise. The findings support the initial hypothesis regarding the feasibility of interpreting labor productivity as a utility function and suggest that the proposed platform architecture offers a viable basis for transitioning from fragmented approaches to a more structured and manageable model of digital transformation. Future research will focus on piloting the proposed model across various industrial sectors. 

The paper presents an innovative method of business planning using artificial intelligence technology. In rapidly changing conditions, the possibility of reliable forecasting and planning with an error of less than 10% is in high demand. The introduction of artificial intelligence technology and automation of analysis and planning processes allows us to create a completely new dynamic multi-agent model of financial business planning that quickly responds to changes in external macroeconomic factors and reduces the risk of human factor influence, which became the result of the study. Having set the goal of developing a new, relevant, modern and highly accurate technological approach to business planning, the authors studied a number of modern scientific studies on the introduction of artificial intelligence in the processes of financial planning and forecasting, systematized them and identified interesting and practically implementable ideas. As a result, an approach was proposed that allows for fairly flexible and quickly implemented business planning, showing a highly reliable result in a short period and implementing the possibility of promptly changing the parameters of the company's activities. However, its implementation requires modification of business planning processes and implementation of an autonomous multi-agent system, which are also developed and proposed in the study. The article will be of interest to practicing economists and business representatives involved in business planning, as well as to scientists and students involved in projects to stimulate entrepreneurial activity. 

Relevance. Fundamentally new infrastructure and production solutions implemented in the Arctic macroregion can subsequently be scaled both in the subarctic regions and in the country as a whole, which determines the importance of analyzing Arctic projects, problems, decisions made and updates research into various aspects of the subject area.
Objective of the study: to study the content of Arctic projects (in the context of priority projects of development support zones), problems and digital solutions in their implementation. Research objectives: to characterize priority projects of development support zones; to study advanced cases of oil and gas companies of China and the Russian Federation in the field of artificial intelligence. Research methods: systems approach, logical analysis, synthesis, content analysis of open sources, modeling. Results. The introduction of digital technologies in the implementation of Arctic resource projects consists in the preliminary application of intelligent equipment, the use of big data, machine learning and other IT technologies in the processing and analysis of data for exploration and development. The implementation of AI technologies in the resource industries has just begun and, despite the operational effect obtained, has not yet brought the desired large-scale results. The assessment of the effectiveness of Arctic investment projects should be based on a set of indicators of commercial, socio-economic and budgetary efficiency. The proposed conceptual model for assessing the economic efficiency of DT includes three levels of assessment depending on the maturity of the twin and the genesis of the formation of the economic effect. The maximum economic effect from the implementation of DT is achieved through the automation of decision-making, the integration of DT into production processes in real time and a significant reduction in total operating costs. Autonomous and cognitive DT of a high level of maturity provide management flexibility, a strategic increase in the value of the company and the ability to quickly respond to changes in the external environment. 

Amid the accelerating digital transformation of public administration, the development of resilient mechanisms for ensuring food security and promoting health-preserving nutrition in the Arctic Zone of the Russian Federation is gaining particular relevance. This study aims to provide a scientific rationale for a digital nutrition management model based on the integration of intelligent technologies into the system of state social policy.
The objective of the research is to develop a scientifically grounded model for digital nutrition and food security management in the Arctic under intensifying climatic, logistical, and infrastructural constraints. In the context of increasing climate instability, nutritional and cognitive deficits, digital inequality, and limited access to medical care, there is a growing need to shift from traditional food assistance to a flexible system of adaptive and intelligent population nutrition governance.
The methodology combines structural-functional and comparative analysis, digital modeling, elements of behavioral diagnostics, microbiome-based approaches, and geospatial analytics. Scenario-based risk assessments, biosensor monitoring technologies, and tools for analyzing nutritional vulnerability are employed, taking into account demographic, climatic, and behavioral factors.
The results include the development of an original platform-based model for digital nutrition management, integrating telemedicine solutions, intelligent algorithms for dietary assessment and adjustment, digital dietary behavior traces, biosensors, wearable devices, and domestic digital products (e.g., «1C: Planned Nutrition», and the cloud-based «Scientific Nutrition Analysis Platform» (NIAP)). A system of indicators for early detection of alimentary risks in northern and Arctic municipalities is proposed, along with innovative mechanisms for personalized nutritional support, including digital diet twins and chrono-nutritional adaptation algorithms.
The conclusions affirm that digital nutrition in the Russian Arctic functions as a strategic resource for social sovereignty, adaptability, and sustainable development. Adaptive intelligent nutrition solutions enable the state to rapidly adjust social policy measures in response to regional challenges, prevent dietary deficiencies, and contribute to achieving the goals of national projects in demographics, healthcare, and digital transformation. 

This article is devoted to analyzing the attitude of personnel in a transport enterprise towards the implementation of artificial intelligence (AI) for monitoring operator states. Drawing on the diffusion of innovations, technology acceptance and change management frameworks, it hypothesises that acceptance is shaped not only by technical features but also by psychological, socio cultural and communicative factors.
The empirical foundation includes two sequentially connected stages: prototype testing in a simulation environment and field trials during real operations.
The method involved semi-structured interviews with 20 employees (purposeful selection by work experience and age), followed by thematic and frequency analysis; to compare subjective “before/after” assessments, a nonparametric test of differences was applied. The findings show that the acceptance of an AI solution is determined not only by technical parameters but also by psychological, sociocultural, and communicative factors: perceptions of “observability” and control, algorithmic transparency, ergonomics, and staff participation in refinement.
Based on the data, managerial mechanisms for reducing resistance are proposed: extended communication and explainability, user involvement in iterative design, targeted training, and a structured feedback protocol. Thus, an empirically grounded model of guided AI implementation in high-responsibility organizations is proposed, relevant for the practices of state and municipal governance. 

The aim of the study was to identify the key characteristics of digital healthcare innovations to determine optimal strategic approaches for their development. A text corpus of regional digital healthcare practices, selected by the Ministry of Health of the Russian Federation and the Central Research Institute of Organization and Informatization of Healthcare (FSBI “RIOIH”) of the Ministry of Health of Russia, was compiled as the empirical basis for the analysis. Using the statistical TF-IDF method to identify semantically significant terms, key patterns characterizing digital healthcare solutions are identified. The predominant role of innovations aimed at organizing primary healthcare is revealed. The most characteristic artificial intelligence technologies in digital healthcare innovations are robotic voice assistants and computer vision technologies. Imperfections in regulatory frameworks concerning the application of medical technologies based on artificial intelligence, the existing digital infrastructure, and issues of ethics and safety in the use of medical data hinder the widespread adoption of innovations. In this regard, strategic approaches to the implementation of artificial intelligence in the context of digital transformation are being defined. The relevance of developing and implementing non-medical digital innovations based on artificial intelligence into the routine processes of medical organizations is demonstrated. The advisability of the widespread use of artificial intelligence in routine innovations to create a holistic data ecosystem necessary for forecasting and strategic decision-making is explained.