The Impact of Generative Artificial Intelligence on the Transformation of the Russian Labor Market

According to the findings of a study by Sber’s Center for Macroeconomic Research (CMR), the adoption of GenAI can deliver an additional 1.2 percentage-point increase in labor productivity. This estimate is based on an analysis of a dataset of work tasks systematized in accordance with the All-Russian Classifier of Occupations approved by Rosstandart. The use of a standardized taxonomy of professional tasks enabled analysts to differentiate occupations by their susceptibility to AI impact and, accordingly, by their potential for efficiency gains. The analysis revealed pronounced heterogeneity in how technology affects different occupational groups.

The greatest productivity growth potential is typical of occupations in which a significant share of functions comes down to information processing, communication, and standard analytical operations. This category includes sales managers, software developers, accountants, marketers, and call-center staff. For these specialties, AI can take on a substantial share of routine workload — from drafting documents and handling inquiries to initial data analysis and content generation, — freeing up specialists’ time to address more complex, strategically important tasks.

Occupations with a medium level of exposure to GenAI include salespeople, administrators and store directors, engineers, cost-estimating engineers, as well as teachers. In these areas, technologies can optimize certain aspects of work — for example, automate report preparation, schedule planning, or the creation of teaching materials, — however, the critical expert component remains a human prerogative.

The lowest degree of transformation in the near term will be experienced by occupations involving a high share of physical labor, direct interaction with objects or customers, and those requiring well-developed empathy and situational adaptability. These include machine operators, drivers, mechanics and auto mechanics, installers and electrical installers, general laborers, packers, welders, warehouse workers, loaders, cleaners, nurses, freight forwarders, waiters, and cooks. The low automation potential of these roles is due to the difficulty of formalizing the corresponding work operations and the high requirements for spatial coordination and tactile sensitivity.

Structural changes in the labor market are also reflected in vacancy dynamics. Over the past two years, the share of vacancies requiring the use of AI tools has increased fivefold. At the same time, there has been a shift in emphasis from positions aimed at full automation to so-called augmented occupations, within which AI serves as an auxiliary tool. The share of vacancies with high automation potential decreased from 30.5 % to 24.1 %, while demand for augmented roles increased by 5 percentage points and reached 23.6 %. This trend indicates the emergence of a new paradigm of human–technology interaction, in which the key competitive advantage is not the absence of a need for human participation, but the ability to effectively integrate AI tools into professional activity.

It should be noted that the assessment of prospects for labor-market transformation in Russia is carried out by several independent institutions offering different development scenarios. For example, the Ministry of Labor focuses primarily on the risks of job cuts, forecasting a potential release of 7–8 % of the employed.

The current labor-market environment is characterized by stable employment indicators and a record-low unemployment rate — 2.2 % in the first half of the year. The absolute number of employed people increased from 74.6 million in January — March to 74.7 million in April — July. At the same time, long-term forecasts indicate that the labor shortage will persist: according to the Ministry of Labor, by 2032 the shortfall of workers may reach half a million people. Under these conditions, labor productivity growth becomes not merely a factor in boosting the competitiveness of individual companies, but also a necessary condition for maintaining macroeconomic stability.

It should be noted that a key importance in the context of technological transformation is the development of a system of continuous professional education and the formation among workers of skills for effective interaction with AI tools. This is not about mass retraining of personnel, but about the phased integration of relevant competencies into existing educational programs and corporate practices. Special attention should be paid to fostering a culture of responsible technology use, including verification of AI outputs and compliance with ethical norms when processing personal data.

An additional factor determining the trajectory of change is the development of domestic AI solutions and their integration into national technological frameworks. Defining the parameters of national AI models, agreed upon between government bodies and industry participants, creates prerequisites for the controlled deployment of technologies and the minimization of risks associated with the use of foreign platforms.

Thus, the impact of generative AI on the Russian labor market should be viewed not as a threat to employment, but as a tool for the structural modernization of the economy. The key condition for realizing a positive scenario is a balanced approach that combines technological development with investment in human capital and the improvement of the regulatory framework. In the long term, it is precisely the economy’s ability to adapt to new technological realities and to form sustainable models of interaction between humans and AI that will become the determining factor of its competitiveness.

Author: Candidate of Economic Sciences, Associate Professor, Department of World Economy and World Finance, Financial University under the Government of the Russian Federation Natalia Ivanovna Chovgan.