AI Changes Everything: Fractures of Global Transformations
Artificial intelligence (AI) significant impacts on the economy and society, touching on a wide range of areas, from strategic management and decision-making to everyday communication. Experts from the HSE Institute for Statistical Studies and Economics of Knowledge (ISSEK) used the iFORA system to map key fractures —areas where changes have already become irreversible and will only intensify.
Reference:
The HSE Institute for Statistical Studies and Economics of Knowledge developed the Big Data Intelligent Analysis System iFORA using advanced AI technologies. It includes 850+ million documents (academic publications, patents, regulatory framework, market analysis, industry media, materials from international organizations, vacancies, and other types of sources). In 2020 iFORA was featured in Nature as an effective decision support tool for business and policy-makers. The OECD noted iFORA as a successful initiative in the field of science digitalization.
Fractures Map is iFORA ecosystem’s new analytical product. It was introduced firstly at the 16th Russian Internet Governance Forum (April 2026) under the foresight session "AI as a trigger for disruption of familiar processes: what it is changing today and how it will change governance, the economy, and society by 2035."
The map is based on 60+ million documents published in the period of 2020-2026 and selected by iFORA algorithms from a database of English-language sources. Indicator of influence strength of windows of opportunity/potential threats on areas of change is an integral index that consider their significance in analyzed documents array, dynamism (rate of change in significance), as well as the vector centrality indicator, which characterizes degree of connection between windows of opportunity and potential threats. The indicator takes values from 0 to 1, where <0.4 is below average, 0.4–0.6 is average, and >0.6 is above average.
AI goes beyond applied tasks and individual industrial solutions, transforming the very logic of how management systems, the economy, and society operate. Integrating AI algorithms into management systems improves the speed, accuracy, and validity of strategic decisions. In the economy, they are reshaping business models and employment structures, compressing product creation cycles at a pace that regulatory and educational systems cannot keep up with. In the social sphere, AI practices spread is changing trust in information, notions of authorship, and interaction formats between people and institutions. All transformations are closely intertwined: changes in governance create new economic conditions, which, in turn, influence social norms and institutions; and at their intersection, profound structural shifts and fractures appear (Table 1).
Management: Decisions are accelerated, responsibility is blurred
Intelligentization encompasses key management stages: from strategic planning to operational control and real-time monitoring. The key management shift lies not in routine tasks automation, but in integrating AI into decision-making core (Fig. 1).
The management cycle is accelerating (1.2 in Table 1), process control accuracy is increasing (1.3), and decisions are increasingly being made on big data basis (1.1). The noted changes are already visible in Russian business practices. According to HSE ISSEK’s calculations, every second company using AI uses it specifically for intelligent support of decision-making. This trend is particularly noticeable in large organizations: AI is used for management tasks in 40% of companies with 500+ employees. Integrating AI into management requires scale, data, infrastructure, and organizational maturity.
However, where AI opens up new opportunities, the most serious risks are also concentrated. In management, where data error can result in distortion of not just a single indicator, but entire decision-making chain, data quality becomes a critical factor (1.4). Companies highlight their shortage, as well as problems with preparation and processing, along with organizational barriers to AI implementation, such as difficulty of technology integration into current processes, high costs, and insufficient level of ICT infrastructure development. At the same time, institutionally unresolved problem of blurred responsibility for AI generated recommendations and actions remains (1.5). It's difficult to determine when outcome depends simultaneously on humans, data quality, AI models, and organizational processes. As algorithmic systems become increasingly integrated into control loop, their reliability becomes a key security condition (1.6). A vulnerability in a model, data, or infrastructure becomes a vulnerability for entire organization.
Economy: Productivity growth and changing market structure
In the economy, AI is no longer playing a purely applied role and is becoming part of value creation processes — from product development to customer interaction (Fig. 2). Automation of ever-widening functions range, including business process robotization, changes cost structures and approaches to organizing activities. According to HSE ISSEK’s forecasts, total AI contribution across all economic sectors to Russia's GDP could reach 11.6 trillion rubles by 2030 and 46.5 trillion rubles by 2035, that indicates transformation systemic nature.
Productivity is improved through resource optimization and data management (2.1), with functions increasingly being redistributed between humans and algorithms. At the same time, new AI-based business models are being formed (2.2). In the platform economy — from e-commerce to financial services — competitive advantage increasingly depends on the ability to analyze user behavior, personalize offers, and manage customer experiences in real time. In this logic, data ceases to be an auxiliary resource, becoming product part, basis for monetization, and tool for long-term customer retention. AI is already being used in a wide range of business processes, and its implementation is accompanied by transformation in employment structure and demand for skills (2.4). This is not so much reduction in employment as reduction in routine tasks share, redistribution of functions, and rapid shift in competency requirements. In particular, importance of skills related to working with data, applying analytical tools, and developing AI solutions is growing.
Cloud infrastructure and distributed architecture enable rapid scaling of AI-based products and services (2.3), lowering entry barriers, and accelerating new solutions commercialization. At the same time, access to storage, data centers, and specialized equipment is becoming prerequisite for AI development. Dependence on suppliers of computing power and infrastructure (2.5) creates the preconditions for concentration of data, technologies, and algorithms among large players (2.6). On average, approximately 4.8% of Russian organizations use AI technologies, but among large companies, their implementation rate reaches 14.9% versus 4.1% among small businesses. This reflects the uneven distribution of AI and advantages of companies with access to data and infrastructure.
Society: Empowerment and Crisis of Trust
In society, AI acts not so much as a tool of automation, but as a trigger for the reassembly of basic practices of knowledge production, communication, and socialization (Fig. 3).
We are witnessing massive shift towards mass content generation (3.1): thanks to AI, barriers to entry into the creation of texts, images, and videos are virtually disappearing, strengthening position of individual authors and small teams. Generative AI is already used by one in six internet users in Russia, indicating significant potential for technology's further dissemination, emergence of multimodal content, and new forms of self-expression.
Along with transforming creative process, AI is also changing education. Adaptive educational systems (3.2) are rapidly developing in digital environment, making learning more personalized and continuous. Hybrid models that actively use AI in learning, text processing, and information retrieval are becoming more widespread. The integration of AI into social and content platforms (3.3) enhances role of algorithms in shaping information agenda and structuring social connections.
Similar to above discussed areas of management and economy, key risks also become most clearly evident in public sphere at new opportunities concentration points. Thus, synthetic content mass production increases risks of fake news dissemination, makes it difficult to assess information reliability and reduces trust in it (3.4). A significant part of AI users already associate its development with increased dependence on algorithms and opportunities for manipulating public opinion, while around 30% are concerned about growing influence of AI. At the same time, issues of digital environment, privacy and security of personal data vulnerability are becoming more acute (3.5): expansion of AI use is accompanied by information volume exponential growth, turning its protection into systemic risk. The increasing accessibility of content generation tools is blurring authorship and intellectual property rights boundaries (3.6). Potential losses to creators in Russia's creative industries from generative AI could reach 1 trillion rubles by 2030, necessitating review of existing content monetization models and legal protection. Taken together, these processes point to emergence of new social environment in which not only access to advanced technologies but also society's ability to build mechanisms of trust, control, and responsibility in face of ubiquitous AI presence are of key importance.
Comment
Konstantin Vishnevskiy
Director of HSE ISSEK Centre for Strategic Analysis and Big Data
AI development is characterized by confluence of two opposing processes. On the one hand, technology is becoming mass-market tool: barrier to entry is lowering, and new participants are becoming involved in development and application. On the other hand, control over data, computing power, infrastructure, and models is increasingly concentrated in limited number of technology leaders. At the societal level, AI expands possibilities for communication, self-expression, education, and knowledge generation and dissemination, but it also complicates mechanisms of trust, forcing society to redefine what information is considered reliable and worthy of attention. In governance, AI opens up opportunities to move from reactive model to predictive one, in which decisions are made not after the fact, but based on early detection of changes in markets, citizens, and production systems behavior. At the macro level, AI is becoming infrastructure for new productivity and, like electricity or the internet, is gradually penetrating all sectors, changing logic of interaction between economic agents. The economic impact of AI is unevenly distributed: winners are not those who implement targeted solutions, but those who restructure business models, organizational processes, data management, and employee competencies around multi-agent AI systems.
Mapping key fractures using the iFORA system shows that AI-driven transformation is no longer a matter of individual industries, but rather profound restructuring of governance, economic, and social systems. The effectiveness of AI-driven transformation will depend on whether institutions can transform AI from innovation into robust development infrastructure with transparent rules, responsibility mechanisms, and risk management.
Sources: calculations based on the iFORA big data mining system (copyright holder — HSE ISSEK); the project results in accordance with approved list of topics for science and methodological support, provided for by the State Assignment of the National Research University Higher School of Economics for 2026.
The review authors: Anna Aksenova, Sofia Privorotskaya, Vladislav Beshlyaga, Danila Kopeikin, and Konstantin Vishnevsky.
Recommended citation:
Aksenova А. S., Privorotskaya S. G., Beshlyaga V. S., Kopeikin D. S., Vishnevsky К. О. (2026) AI Changes Everything: Fractures of Global Transformations. — HSE ISSEK.
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