Generative AI and Potential for Augmentation: A Data-Driven Analysis of Labor Market in Russia

Generative AI and Potential for Augmentation: A Data-Driven Analysis of Labor Market in Russia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Generative AI and Potential for Augmentation: A Data-Driven Analysis of Labor Market in Russia Maksim Elisov, Kirill Pshinnik, Alexandra Bordunos, Oksana Zhirosh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7173895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Drawing on the concept of Human-AI Collaboration (HAIC), this research analyzes the exposure of occupations to generative AI-driven automation in the labor market of Russia using real vacancies data. The study addresses key research questions on the types of tasks and occupations amenable to GenAI augmentation using GPT-4o for prediction of task automation potential and occupational variability with regard to augmentation prospects. Our findings contribute to the body of research revealing significant disparities in AI potential adoption across tasks and occupations. We detected no tasks or occupations prone to 100% automation; the highest automation potential of a task is 85% and that of occupation augmentation − 70%. Our major findings are threefold. First, occupations in culture, sport, leisure and entertainment, activities in the operation of real estate as well as information and communication showed the highest augmentation potential, i.e., 63%, 58.8%, and 49.6%, respectively. Second, the potential for augmentation is positively associated with the level of wages. Third, potential financial impact by 2030 is predicted to reach 10.8 trillion rubles. The findings underscore the urgency of reskilling initiatives and ethical frameworks to mitigate inequality. By bridging theoretical and practical insights, this research informs organizational strategies for responsible AI integration and highlights pathways to maximize human-AI synergy in the evolving workplace. Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing generative AI GenAI labor market tasks automation augmentation Human-AI Collaboration Figures Figure 1 Figure 2 1. Introduction The integration of AI technologies, particularly generative AI like ChatGPT or Midjourney, is increasingly recognized as a transformative force in the workplace that improves decision-making process, increases productivity and creativity in the daily routine, and streamlines operations. While several waves of concerns, a so-called “AI winter” [ 1 ], [ 2 ], witnessed skepticism, which primarily reduced interest and funding in artificial intelligence projects, the current situation can be described as “AI spring” [ 3 ], [ 4 ] with a growing body of reports and scholarly literature related to introducing generative AI tools in various occupations, tasks, and efforts to evaluate their efficiency. The potential financial impact of AI adoption on the economy of Russia by 2030 is estimated as high as 11.6 trillion Russian rubles [ 5 ]. These observations motivate further search for the possible means and grounds for proof of real shifts in workforce efficiency and for prediction of the potential for raising productivity for each occupation due to generative AI technologies. The emerging trend for the organizations is not just to experiment with generative AI, but to actively implement generative AI tools into daily routine. Accenture report [ 6 ] highlights that generative AI has the potential to significantly enhance productivity across industries while urging organizations to adopt these technologies responsibly and strategically. Accenture outlines six essentials for organizations to embrace generative AI: Business-driven mindset: foster experimentation with generative AI. People-first approach: prioritize reskilling and workforce transformation. Data readiness: prepare proprietary data for effective model training. Sustainable tech foundation: invest in infrastructure to support AI deployment. Ecosystem innovation: collaborate across sectors to enhance AI applications. Responsible AI: ensure ethical practices in AI development and deployment. While some companies are at the experimentation stage of generative AI adoption, others are already experiencing the benefits of the technology's disruptive potential. According to The Future of Jobs Report 2025 [ 7 ] by the World Economic Forum (WEF), 83% of the surveyed employers believe that advancement in AI and information processing technologies will be one of the main drivers of business transformation during 2025–2030. The Report concludes that AI adoption is growing rapidly but unevenly, with the information technology sector leading the way while industries like construction lagging behind. This disparity reflects broader trends, as advanced and middle-income economies embrace generative AI more widely than low-income economies, where its use currently remains minimal. The said Report claims that in response to AI adoption, companies manifest a strong focus on adapting existing workforces via reskilling and upskilling (77%) and acquiring new talent to navigate the changing landscape brought about by AI. To maximize benefits and avoid widening inequality, AI development should focus on augmenting human capabilities rather than replacing them, supported by appropriate frameworks, incentives, and regulations. For the purpose of this project, we use the term “automate” with regard to specific tasks and the term “augment” with regard to an occupation. The high potential of augmentation of an occupation implies a high number of tasks that can be automated within the occupation. Literature on the affordances of the use of AI in various industries and application for various tasks is gaining momentum. The current research was motivated by the need to overcome inflated expectations and subsequent disappointment of large-scale companies from investing into adoption of generative AI technologies by searching for exact measures how increased efficiency could be estimated and improved. Overall, the research findings aim to provide a basis for predictions of opportunities to transform particular job functions in order to foster efficiency by the use of generative AI. This paper reports a part of our broader research effort that aims to introduce generative AI-use training intervention for personnel and measure the impact of the said intervention on the personnel efficiency. To select and prioritise tasks and occupations for such a training intervention, we draw upon the methodology presented in [ 8 ], which introduced an approach to evaluate task exposure and automation potential using GPT-4 LLM and measured overall exposure in the contemporary U.S. labor market. Our project thus contributes to the growing body of literature on the methodologies and results of the evaluation of potential use of generative AI tools and its impact on the labour markets, e.g., China [ 9 ], Latin America (Chile, Peru, Mexico) [ 10 ], EU and, specifically, Germany [ 11 ]. Our project contribution is threefold: we analyze the data of the labour market in Russia and advise on the task prioritization for AI-use educational intervention; the data for the analysis were collected from real vacancies (as compared to expert description of tasks in O-NET base in 7) we used a newer model, i.e., Chat GPT-4o, for data analysis. Specifically, we address the following research questions: RQ1. What types of tasks in various occupations could be automated by generative AI? RQ2. Is it possible to assess the potential of such tasks for automation with the GPT-4o model? RQ3. How do the occupations range by the potential of augmentation? RQ4. What is the potential financial impact of generative AI augmentation for the labour market? The above research questions motivated the following study design: first, we parsed vacancies from the website of a staff recruitment service provider Headhunter ( https://hh.ru/ ); then, we uploaded the collected data to ChatGPT-4о LLM and prompted it to, first, retrieve all tasks from vacancies descriptions, and, second, build detailed predictions about the GenAI-driven automation potential of the retrieved tasks for particular occupations; further, we assigned weights to the retrieved tasks based on the frequency of their occurrence in the resulting list of tasks; finally, we categorized the tasks by functional areas, key competencies, and industrial fields, and completed the data analysis by estimation of the economic impact of the adoption of GenAI. As a result, we identified no tasks or occupations that are susceptible to complete (100%) automation; the maximum automation potential observed for any individual task is 85%, while the highest augmentation potential for an occupation is 70%. The sectors of culture, sport, leisure and entertainment; real estate operations; and information and communication exhibited the greatest augmentation potential — 63%, 58.8%, and 49.6%, respectively. A positive correlation between augmentation potential and wage levels was identified (Fig. 2 ). The projected potential financial impact by 2030 in Russia is estimated to reach 10.8 trillion rubles. The rest of this paper takes the form of five sections. Section 2 presents an overview of the literature related to LLM potential for augmentation; Section 3 , details the methods of data collection and analysis; Section 4 presents the results, Section 5 , interprets the results and offers the directions of future research. 2. LLMs potential for augmentation. The ways humans use AI is being investigated from various perspectives, and the conceptualizations of this phenomenon has been increasingly shifting towards the idea of shared agency when humans and AI do or can leverage each other’s strengths, e.g., [ 12 ], [ 13 ], [ 14 ], [ 15 ], [ 16 ]. In this work we rely on the concept of Human-AI Collaboration (HAIC), specifically in the sense articulated in [ 17 ], i.e., when humans and AI “ perform interdependent actions”. In the analyzed literature, HAIC is seen as a transformative aspect of the labor process; with regard to AI potential for augmentation, the main themes are related to improved efficiency and productivity, creativity and innovation, decision-making, and quality. 2.1. Efficiency and productivity LLMs have a significant impact on operational efficiency. Experimental studies demonstrate that the application of generative AI can substantially reduce the time required to perform routine or standardized tasks while simultaneously enhancing result quality. For example, the use of ChatGPT in professional writing leads to reduced time expenditures and higher quality assessments of the final product. In practical terms, when composing press releases or commercial proposals, ChatGPT helps structure the text, correct style and grammar, and thus accelerates document preparation while minimizing errors [ 18 ]. In the IT sector, GitHub Copilot considerably speeds up code development processes by enabling programmers to complete tasks 55.8% faster compared to traditional methods. Moreover, the Copilot not only generates templated solutions but also suggests optimizations, identifies potential errors, and assists in improving software architecture – an especially valuable feature under tight deadlines and intense competition [ 19 ]. Furthermore, LLMs are finding application in market research, where their ability to generate plausible responses to consumer inquiries allows for cost-effective data collection. In the study by Brand et al. [ 20 ], it is shown that modeling consumer preferences with the help of LLMs yields results comparable to those obtained from traditional surveys, but with considerably less time and financial investment. For instance, companies can use LLMs to simulate audience reactions to a new product line, enabling them to quickly adjust marketing strategies and identify potential strengths and weaknesses in the offering at early stages of product development. Besides, significant changes are observed at the macroeconomic and labor market levels. Impact assessments indicate that the adoption of LLM technologies can not only automate a substantial portion of work tasks but also markedly boost overall labor productivity. Eloundou et al. [ 8 ] demonstrate that, thanks to LLMs, approximately 15% of all work tasks can be completed significantly faster; when supported by LLM-powered software, this figure increases to between 47% and 56%. Such improvements facilitate the optimization of internal processes within organizations, the reallocation of human resources, and the emergence of new professional roles associated with coordinating human–AI interactions. Similar conclusions are corroborated by Dell’Acqua et al. [ 21 ], consultants who used AI for tasks within AI capability frontier completed 12.2% more tasks, 25.1% faster, with over 40% higher quality results; lower-performing consultants improved by 43% and higher-performing ones by 17% compared to their baselines. 2.2. Creativity and Innovation Kaufman and Beghetto proposed a 4C model of creativity: mini-c, little-c, Pro-c, Big-C [ 22 ]. Here we mostly address Pro-c, i.e., professional creativity, a domain-specific socially-recognised form of creativity, as the most relevant for this study. An example of the positive impact of HAIC on human innovative thinking is freeing up time and attention of humans for more creative tasks by assigning routine, automatable, tasks to AI. In [ 23 ] AI assistance led to a significant increase in creativity among higher-skilled employees, enabling them to generate more innovative solutions and improve performance. When AI handled the repetitive and codified parts of a task, employees could focus on more complex, creative problem-solving. Another example of such an impact on team work is described in [ 24 ], which, among other research questions, investigated how team expertise is leveraged in new product development tasks with the use of AI. During ideation stage, without AI assistance, technical and commercial specialists tended to produce ideas within their professional background while overseeing other perspectives; with AI assistance, however, the said specialists engaged in more holistic, interdisciplinary thinking, which enabled them to produce more balanced ideas without compromising the effectiveness of the suggested solutions. From a theoretical perspective, a systematic literature review [ 25 ] suggests that HAIC has the potential to transform organisational innovation practices via seven dimensions: Predicting, Assessing, Decision-making, Problem- solving, Adjusting, Cultivating, and Absorbing. Further, [ 26 ] systematically investigate how the latest developments in AI platforms and technologies influence innovation ecosystems enabling new ways to generate value. They extend the existing theories of innovation ecosystems and propose a new conceptualized framework, an “AI innovation ecosystem” consisting of three essential elements, i.e., actors and roles, data- driven decision-making process impacted by AI, and value generated by AI. They also introduce an “AI-Collaboration Matrix within Innovation Ecosystems” that illustrates how different levels of AI adoption, combined with varying degrees of collaboration, impact innovation results. Another conceptualization of AI adoption in innovative firms is presented in [ 27 ]. Drawing upon a systematic analysis of empirical studies, they proposed a framework that represents the influence of AI adoption on innovation capabilities. The framework consists of two sets of capabilities: six enabling capabilities , and seven enhancing capabilities , both influenced by the technological, organizational, and environmental context. The enabling capabilities are necessary to allow for AI adoption, while the enhancing capabilities are experienced by innovating firms as a result of AI adoption and allow for the transformation of innovation practices. Besides, the authors suggest a taxonomy of AI applications, i.e., replace, reinforce, reveal , with the latter capable to “unveil hidden technological opportunities and unshadow unforeseeable external situations” [p.91, 27]. 2.3. Decision-making Due to a shift from rapid, heuristic-based processes performed by LLMs (System 1) to slower, more analytical and structured methods of information processing (System 2) [ 28 ]. This evolution enables the models not only to generate text but also to perform complex logical reasoning and planning. For instance, the authors in [ 29 ] illustrate the application of these models in solving complex mathematical and optimization problems that require step-by-step analysis and iterative refinement of intermediate solutions. At the cognitive level, there is an enhancement of "slow thinking", wherein LLMs are employed to support multi-stage reasoning and decision-making. The models are capable of thoroughly analyzing tasks, highlighting key aspects, and offering several solution alternatives, thereby reducing the likelihood of errors and improving the overall quality of the final output. Such capabilities are particularly crucial in areas where precision is critical—such as in the development of complex software products or in legal research, where detailed analysis and logical justification are required [ 29 ], [ 18 ]. Study [ 30 ] aimed at helping to overcome data scarcity for predictive modeling of ERP adoption. For this, GANs and VAEs were used to create synthetic ERP adoption data; the generated data closely matched real data, as confirmed by statistical tests; the system allows for more informed decision-making. In [ 31 ] the suggested system combined Digital Twins and Generative AI (like ChatGPT), which enabled it to quickly and accurately detect issues in the test scenarios as well as learn from past data. This predictive capability can transform decision-making from reactive to proactive, reducing costs, minimizing downtime, and optimizing performance. A review in [ 32 ] explores the application of AI in Finance, and concludes that integrating Generative AI models like GPT-4 with Big Data enhances, among others, predictive accuracy and creates high-quality synthetic data, which leads to major improvements in data engineering and enterprise analytics. 2.4. Quality Noy and Zhang [ 18 ] demonstrate that the use of ChatGPT in professional writing tasks significantly enhances output quality, with treatment group participants achieving scores 0.45 standard deviations higher than those in the control group, reflecting improvements in overall quality as well as in specific dimensions such as writing quality, content, and originality. The grade distribution shifted upward across the board, indicating a robust quality enhancement that was particularly pronounced among individuals with lower baseline skills, thereby reducing productivity inequality. This effect appears to stem primarily from ChatGPT substituting for routine drafting efforts, which allows users to reallocate time towards editing and refinement; notably, even when participants submitted ChatGPT’s raw output without significant modification, the quality was substantially superior to that achieved without AI assistance. Dell'Acqua et al. in [ 21 ], however, assert that the quality of outputs depends on whether the tasks assigned to AI are within or outside of the “jagged technological frontier”, i.e., AI capabilities for particular types of tasks. In their experiment, the quality of the outputs obtained with the use of GPT-4 for tasks within AI capabilities was graded 43% higher compared to the control group (no AI used) for the lower-skilled consultants and 17% higher for the higher-skilled consultants. For the tasks outside of AI capabilities, though, the outputs of the consultants relying on AI showed 19% lower quality than the outputs of those working without AI. In conclusion, the integration of LLMs across various professional domains is likely to be accompanied by complex processes that merge the automation of routine operations, the improvement of complex analytical tasks, and the emergence of new forms of interaction between humans and AI. 3. Methods This section details data collection and analysis processes we used to address our research questions. 3.1 Data collection To accumulate the data, we first made a Python-based program. The program collects job vacancies data in HeadHunter (HH.ru) API. We consider the HH.ru platform representative enough for the purpose of data collection (see https://stats.hh.ru ). The program provides functionality to search for vacancies by occupation title and to save the results in an Excel file with the .xlsx format. We collected such data for 78 most popular occupations; the criterion of popularity implies the highest numbers of vacancies posted on the said website for a particular occupation; the said 78 occupations represent more than 85% of all the open vacancies in HH.ru. We took 100 vacancies for each occupation from the January 2025 data list. On HH.ru the vacancies are automatically deactivated and become unavailable for open access 30 days after activation; hence the retrieved list might fail to respond to the changes related to, e.g., seasonality. Additional analysis revealed that increasing the sample size to over 100 vacancies did not lead to a significant improvement in estimate accuracy or produce substantial changes in the model's responses. Initially, we compiled a list of vacancy IDs matching specific parameters. The program works as follows: a GET request is sent to the HH API with the following parameters: search text (occupation titles), region code (search can be restricted to specific cities, regions, or countries, we used data for the country). The API response is checked for success, and the data are returned in JSON format. Each suitable vacancy is added to a table that includes its title and URL. The function stops as soon as the required number of vacancies has been collected or when there are no more vacancies on the current page. Next, the program processes the vacancy links obtained from the HH.ru website, extracts data for each vacancy using the HH API, cleans and structures the information, and then saves the results to a new Excel file. Standard Python libraries were used throughout this procedure. 3.2 Data pre-processing Upon collection, the textual data were preprocessed by removing HTML tags, decoding HTML entities, and eliminating special characters from the vacancy descriptions, thereby preserving only plain text. After that, the vacancies were retrieved using the identifiers collected in the previous stage. For each identifier, a GET request was sent to the HH.ru API in order to obtain structured vacancy data, including the job title, employer name, location (city), salary, and description. The program accepts an Excel file containing a list of vacancy URLs, from which it extracts and validates each link. If a URL matches the standard HH.ru vacancy format, the corresponding vacancy ID is extracted and used to query the API. The retrieved data are appended to a list, and the final output is organized in tabular form. 3.3 AI processing For the neural network-based processing of vacancies, a dedicated table was constructed containing only the textual descriptions of the vacancies. The program performs asynchronous POST requests to the OpenAI API, utilizing parameters such as the model specification, prompt, and user input. For each row in the table, an individual task is generated to query the API. These tasks are executed concurrently, which significantly improves the processing speed for large datasets. Once the API responses are received, they are written to the resulting DataFrame on a row-by-row basis, with the processing status recorded for each entry. Following this, all tasks are processed using the ChatGPT-o1-mini model. At this stage, a different model was employed due to its better performance in processing textual data. Although this model is more effective for natural language understanding tasks, it also incurs a higher usage cost. An expandable list of unique tasks is initialized. For each incoming task, the model receives both the current list of unique tasks and a new task from the complete set. If the task can be categorized as matching one of the existing unique tasks, the corresponding task rating is incremented by one. If no match is found (or if the list is initially empty), the task is appended to the list as a new unique task with an initial rating of one. Here, the rating reflects the frequency with which the task appears in the dataset. The resulting set of unique tasks is subsequently analyzed by the neural network to assess their potential for automation via large language models (LLMs). Automation potential is defined as the extent to which the use of an LLM may accelerate the execution of a given task. Tasks deemed unsuitable for automation receive a score of 0%. The structured response returned by the API is then parsed and decoded as JSON. If the response contains keys and examples, these are incorporated into the final dictionary. For each task, a distinct row is created in the output, containing the original task description, its evaluated characteristics, and, where available, examples. The final results are saved to a new Excel file. Finally, a metric termed “task weight” is introduced. It is defined as the product of the number of relevant vacancies and the automation potential score (expressed as a percentage). This value reflects each task’s contribution to the overall automation potential of a vacancy. Tasks assigned an automation score of 20% or lower are considered economically inefficient to automate, and their contribution is therefore treated as zero. The overall automation score for a vacancy is calculated as the ratio of the cumulative task weights (with the threshold applied) to the total number of tasks. The threshold is justified by the observation that the neural network does not consistently assign a 0% automation score, even for tasks that are not automatable. This behavior can be attributed to the fact that large language models (LLMs) may still be applicable in auxiliary roles—such as suggesting routes, identifying recreational activities, or commenting on user actions. While these applications may theoretically support certain tasks, they are often of marginal utility or entail higher implementation costs than the benefits they yield. Initially we generated a workflow with the two alternative scenarios: with and without the extended list (Fig. 1 ). Current research is based on the extended scenario, because it provides task weights. 4. Results 4.1 The potential of augmentation within occupations . Table A- 1 (Appendix A) generated within the above framework represents an analytical tool for the quantitative evaluation of the potential for automating professional tasks using large language models (LLMs) based on data obtained via the HH.ru API. The table reflects two key indicators: the total number of unique tasks identified in the job listings, and the average automation percentage calculated for all tasks as well as for the subset of tasks with ratings exceeding the established threshold. This approach allows not only for the assessment of the overall potential of applying LLMs but also for highlighting those aspects of professional activities in which the implementation of neural network technologies may lead to a significant increase in productivity. For the purpose of this project we use the term “automate” with regard to specific tasks and the term “augment” with regard to an occupation. The high potential of augmentation of an occupation implies a high number of tasks that can be automated within the occupation. Data analysis showed that occupations oriented toward information processing, analytics, and managerial functions demonstrate a high level of automation (Table 1 ). This is due to the fact that the nature of their tasks entails the active use of textual information, which aligns closely with the functional capabilities of modern language models (e.g., Table 2 ). The high automation metrics in these areas indicate a significant potential for improving the efficiency of work processes through the implementation of LLMs. Table 1 Top Ten Occupations with the Highest Potential for Augmentation. Occupations Average Automation % (≥ 20%) Presentation Designer 70.6% Dispatcher 67.4% Product Analyst 66.4% Writer 65.7% Data Engineer 65.3% Marketplace Manager 64.8% Financial Analyst 64.6% Head of Analytics Department 63.3% Procurement Manager 62.1% Table 2 Potential of Tasks for Automation within Presentation Designer Occupation Original Task Automatability Creating visual content for a presentation based on provided text. 85% Designing an appealing presentation to introduce a new product. 80% Creating a unique design for a new product launch. 75% Developing a presentation for a business meeting, including charts and graphs to illustrate key data. 75% Creating a visual concept for a presentation, including selecting a color palette, fonts, and graphic elements. 75% Designing a presentation for a corporate event, including theme selection, slide design, and visual content creation. 75% Creating slide animations to make information more visual and retain audience attention. 75% Conversely, our study results reveal that professions associated with manual labor or direct interaction with machinery, such as drivers, exhibit a considerably lower potential for automation (Table 3 ). In these fields, work processes depend on physical actions and specific skills that are not amenable to direct processing by language models (e. g., Table 4 ). Thus, the observed imbalance in automation indicators suggests that the effectiveness of LLM applications varies significantly depending on the nature of the tasks performed. Table 3 Ten Occupations with the Lowest Potential for Augmentation Profession Average Automation % Driver 7.60% Nanny 6.50% Locksmith 3.80% Security Guard 2.10% Electrician/Installer 1.00% Courier 0.20% Turner (Lathe Operator) 0.10% Welder 0.00% Tractor Operator 0.00% Cleaner 0.00% Table 4 Potential of Tasks for Automation within the Occupation of Driver Original Task Automatability Accompanying the executive on trips around the city, region, and other areas. 5% Ensuring the safety and well-being of passengers on public transportation. 10% Car maintenance, including keeping it clean and in working condition. 10% Providing safe and comfortable transportation for the company executive and their family. 10% Delivering goods and materials to retail locations within the city. 10% Compliance with traffic regulations. 10% Managing the process of renting or servicing the vehicle. 30% Preparing documents. 60% Expense reporting. 70% The occupations were clustered based on three distinct principles: by Functional focus, by Industry sector and by Key skills and Competencies. For each resulting cluster, the average automation level was calculated within each category, representing the proportion of tasks within that category that are susceptible to automation. This value reflects the estimated automation potential for each category under the respective clustering scheme. The data in Table 5 suggest that AI augmentation will thrive in knowledge-intensive, creative, and analytical fields, while its impact will be more limited in manual or highly regulated industries. Tailoring AI solutions to sector-specific needs (e.g., automated analytics for finance, design aids for creatives) will maximize adoption. Table 5 Prospects of Augmentation in Different Industries A. By Functional Focus Functional Area Average Potential Automation % Creativity and Communication 65.5% IT and Digital Technologies 46.1% Analytics and Business Management 44.7% Education, HR, and Development 41.5% Medicine and Healthcare 35.7% Specialized Services and Support 34.5% Engineering and Technical Manufacturing 20.9% B. By Industry Sector Industry Sector Average Potential Automation % Digital Technologies and Communications 49.0% Finance, Analytics, and Management 44.8% Education and Workforce Development 41.5% Healthcare and Social Services 35.7% Specialized Services and Transportation 34.5% Engineering, Manufacturing, and Construction 20.9% C. By Key Skills and Competencies Key Skills and Competencies Average Potential Automation % Creative Thinking and Communication 49.5% Analytical and Managerial Skills 44.8% Professional Services and Support 34.8% Technical and Software Skills 31.5% 4.2. Potential financial impact of augmentation with AI on the labor market. The presented data (Table 6 ) evaluate the potential impact of generative neural networks, particularly large language models (LLMs), on the labor market in Russia across various economic sectors. The sectors are identical to the ones used by Rosstat [ 33 ]. The analysis focuses on the extent to which LLMs can enhance worker productivity, the average monthly salaries by sector, the proportion of the national workforce employed in each area, and the resulting macroeconomic effects. The metric labeled “automation” reflects the estimated productivity increase from the integration of LLMs. Automation rates for each sector were computed as the mean of the individual automation percentages of the occupations comprising that sector. By multiplying this metric with employment distribution and salary data, the study estimates the annual economic benefit that could be realized through efficiency gains. Notably, sectors with broad employment coverage and moderate automation potential—such as Wholesale and Retail Trade, as well as education—demonstrate the highest estimated yearly savings, exceeding 2.28 and 1.32 trillion rubles respectively. Conversely, areas requiring deep domain expertise, such as scientific research and development, are associated with lower automation potential and correspondingly smaller economic gains. These findings emphasize not the replacement of workers, but rather the opportunity for task redistribution and productivity enhancement, which may lead to GDP growth, labor market transformation, and the emergence of new professional pathways. We estimate the maximum effect of the introduction of neural networks in the Russian labor market by 2030 at 10.79 trillion rubles, which is close to the estimate from the Higher School of Economics [ 5 ]. Table 6 Statistics based on Rosstat data [ 33 ] Activity Sector Automation Average Salary (RUB) Share of Population Annual Savings, Trillion RUB Wholesale and Retail Trade 27.7% ₽66,226 14.2% 2.28 Information and Communication 49.6% ₽136,988 2.4% 1.43 Education 37.7% ₽54,315 7.4% 1.33 Agriculture, Forestry, Hunting, Fishing and Aquaculture 46.3% ₽54,158 6.0% 1.32 Construction 22.3% ₽71,707 9.3% 1.30 Professional, Scientific, and Technical Activities 29.1% ₽108,253 4.1% 1.20 Scientific Research and Development 37.4% ₽120,790 Transportation and Storage 21.4% ₽76,223 8.1% 1.16 Healthcare and Social Services 34.5% ₽61,651 6.2% 1.15 Financial and Insurance Activities 36.5% ₽170,600 1.8% 0.98 Real Estate Operations 58.8% ₽55,443 2.6% 0.74 Manufacturing 9.0% ₽71,855 14.2% 0.80 Arts, Sports, Entertainment, and Recreation 63.0% ₽65,702 1.6% 0.58 Administrative and Support Service Activities 40.7% ₽50,573 3.1% 0.56 Total 81.0% 13.49 By 2030 10.79 We also divided the occupations into four categories depending on the level of automation (Table 7 ). The data on wages were retrieved from the HH.ru website. The results indicate a statistically significant positive correlation between the level of wages and automation potential across occupations (Fig. 2 ). These results are consistent with those in [ 8 ] and [ 9 ]. Table 7 Division by automation sectors Criterion, % of automation Average Automation Average Salary (thousand RUB) 60%+ 64.7% 136.1 40–59.9% 49.4% 117.8 20–39.9% 32.9% 113.0 0–19.9% 6.8% 104.9 In recent years, the Russian labor market has witnessed a marked shift in wage dynamics, particularly among traditionally manual labor occupations. These professions — historically associated with lower compensation and limited potential for automation by large language models (LLMs) — have experienced substantial wage increases, in some cases earning several times more than in prior years [ 34 ]. This structural realignment may have attenuated the observed relationship between income and augmentation. The correlation analysis across 78 professions yielded a Pearson coefficient of r = 0.381, with a statistically significant p-value of 0.0006, indicating a moderate positive association. However, the 95% confidence interval for r, ranging from 0.172 to 0.557, suggests considerable variability. The inflation of wages among less automatable occupations could be contributing to this broader interval and the dilution of a stronger trend, thereby partially masking the extent to which higher salaries may otherwise correlate with greater exposure to automation. 5. Discussion and conclusion 5.1 Summary of Findings This study set out to evaluate the possible transformative impact of generative AI (specifically GPT-4o) on workplace tasks and productivity in the Russian labor market. Overall, the results provide robust evidence that the potential for AI-driven task augmentation is significant but highly uneven across task types and occupations. Tasks heavily involving information processing, data analysis, and other text-centric activities exhibit the greatest augmentation potential. For example, occupations in fields like data science, marketing, and management — where daily work revolves around generating or analyzing text-based information — showed markedly high automation scores, indicating that a large portion of their routine tasks could be accelerated or enhanced with GPT-4o assistance. Automation is also high for certain professions, such as dispatchers, due to the ability of large language models (LLMs) to effectively receive, process, and relay information to the appropriate recipients. In contrast, roles that require physical labor, direct manipulation of the environment, or face-to-face interaction (e.g. drivers, machine operators, and similar manual-intensive jobs) consistently scored low on automatability , as their core tasks are not readily handled by current language models. This clear disparity supports our first hypothesis (RQ1) that the amenability of tasks to generative AI depends on task nature : cognitively intensive and textual tasks are far more augmentable than physically intensive ones. Crucially, our methodology leveraged GPT-4o to assess task automation potential using real-world job vacancy data, rather than relying solely on expert judgments or static occupational databases. The GPT-4o-based assessment proved to be feasible and insightful (addressing RQ2) . The model was able to parse thousands of job listings, identify the tasks involved, and evaluate each task’s likelihood of being automated or enhanced by AI. The resulting estimates were not only intuitively plausible but also exhibited convergent validity when compared with external indicators and studies. Notably, occupations that GPT-4o identified as highly augmentable tend to be those with higher average wages, and indeed we observed a positive correlation between an occupation’s automation percentage and its salary level. This aligns with prior research from the University of Pennsylvania, [ 8 ] which found that roles commanding higher wages often have greater exposure to AI technologies. In essence, GPT-4o’s predictions mirrored known patterns (e.g., that well-paid, knowledge-intensive jobs contain many tasks AI can assist with), lending support to the accuracy of our approach. The third and fourth research questions (RQ3 and RQ4), concerning how augmentation potential varies across occupations and which occupations rank highest, were also affirmatively answered. We found substantial variability across the occupational spectrum : a handful of professions emerged as clear front-runners for AI augmentation (with task automation potentials well above the chosen threshold), while others lagged far behind. Indeed, we were able to stratify jobs into four broad categories of automation readiness (from low to high), a categorization that could guide where AI interventions might be most impactful first. A high level of automation was observed in the sectors of Real Estate Operations and Arts, Sports, Entertainment, and Recreation . In the real estate domain, this can be attributed to occupations such as realtors, which involve tasks like analyzing large volumes of data, generating listings, and interacting with online platforms—all of which can be significantly enhanced by large language models (LLMs). In the creative sector, tasks such as writing poetry or generating visual artwork can also be substantially accelerated through the use of neural networks, highlighting the growing applicability of generative AI in domains traditionally viewed as human-centric. Zooming out to the macroeconomic perspective (RQ5), the findings indicate that widespread adoption of generative AI in the workplace could yield significant productivity and efficiency boosts and economic gains , albeit distributed unevenly across sectors. By combining each sector's automation potential (as estimated by GPT-4o) with employment and wage data, we estimated the potential annual efficiency gains in monetary terms. The results suggest that certain large-employment sectors with moderate AI amenability stand to gain the most in absolute terms. For instance, wholesale and retail trade and education — sectors that employ a substantial share of the workforce — could each realize yearly productivity benefits on the order of ₽1–2 trillion through task augmentation. In contrast, sectors requiring highly specialized human expertise, such as scientific R&D, showed lower augmentation percentages and consequently smaller aggregate gains. Summing across industries, the upper-bound estimate for economy-wide impact is considerable: our analysis suggests that by 2030, generative AI integration could contribute up to roughly ₽10.79 trillion in efficiency-related gains annually in Russia’s labor market. This figure is in line with independent projections by national research bodies (e.g., a similar estimate by the Higher School of Economics [ 5 ]). It is important to note that these gains reflect improved productivity and task efficiency rather than outright replacement of workers. In fact, a key insight is that generative AI’s value in this context lies in augmenting human labor — freeing workers from tedious tasks and thereby enabling labor redistribution towards more complex or creative activities — which can catalyze GDP growth, labor market transformation, and the emergence of new roles . 5.2 Interpretation of Results The above findings carry several important interpretations for theory and practice. First, they strongly reinforce the notion that task characteristics are a decisive factor in determining AI’s impact. This was anticipated by our theoretical framing: according to the Technology Acceptance Model (TAM) [ 35 ], the likelihood of adopting a new technology depends on its perceived usefulness and ease of use for a given job function. GPT-4o effectively has a higher “perceived usefulness” for tasks that are already digital and information-centric, since it can be readily applied to generate text, analyze language, or expedite information workflows. Our empirical evidence supports this TAM-based expectation – roles where an AI like GPT-4o can be easily applied showed far greater productivity uplift than roles where it cannot. In practical terms, jobs involving routine cognitive processing were most conducive to AI augmentation , because GPT-4o could seamlessly slot into those workflows (e.g. drafting reports, writing code, summarizing data). By contrast, in occupations centered on physical skills or interpersonal interaction, the model’s utility is inherently limited, explaining the low augmentation indices observed there. This divergence underscores a critical point: current generative AI excels at substituting or speeding up information processing sub-tasks , but it struggles with tasks requiring embodiment, physical manipulation, or complex social intelligence . From a human–AI collaboration (HAIC) standpoint, this means the optimal division of labor is one where AI handles the text-based or procedural elements while humans focus on the physical, empathic, and judgment-based components of work. Such a synergy was noted in our results as a “synergistic effect,” particularly in fields like marketing, strategic planning, or innovation, where human creativity coupled with AI-driven analytical support can enhance overall outcomes. Another key interpretation is the validation of GPT-4o as a predictive tool for workforce analytics . One of our research questions (RQ2) probed whether a large language model could reliably estimate task augmentation potential. The findings are encouraging: GPT-4o task exposure estimates correlated with independent benchmarks (e.g., wage levels, known patterns from prior studies) and yielded plausible sector-wise projections. This suggests that advanced generative models can serve not just as productivity aids, but also as analytical instruments to forecast technological impacts . In our case, using the model to analyze real job postings provided a data-driven way to quantify AI exposure at scale, complementing or even accelerating traditional expert surveys. This approach is a novel contribution of our work, demonstrating how AI can help map out its own impact on labor markets in a more dynamic fashion. It is worth noting, however, that while the model’s estimates were broadly consistent with external data and conclusions on task exposure in [ 8 ], they should be interpreted as indicative rather than definitive. Generative AI can sometimes overestimate its capabilities or overlook tacit job requirements, so human validation remains important. Nonetheless, the correspondence we observed bolsters confidence in leveraging models like GPT-4o for preliminary assessments of automation potential in rapidly evolving job landscapes. The fact that higher-wage occupations showed greater AI augmentation potential is an intriguing outcome with labor economics implications. Historically, automation in earlier industrial eras often threatened lower-skill, routine jobs; by contrast, generative AI appears to target many higher-skill professions (e.g., lawyers, analysts, developers) because those jobs involve abundant information work that AI can optimize. Our data confirmed that industries with higher average salaries tend to have a larger share of tasks that are automatable by GPT-4o. This might initially raise concern about possible disruption of well-paid professional roles. However, our interpretation, consistent with the concept of augmentation, is that these roles are more likely to be transformed than eliminated . Professionals in high-exposure fields stand to become more productive by offloading routine aspects of their job to AI, potentially increasing the value of their creative and supervisory skills . In fact, evidence from related studies indicates that generative AI can help level the playing field within such occupations: for example, an experiment [ 18 ] found that when office workers used ChatGPT for writing tasks, even those with lower prior skills saw substantial quality improvements, narrowing performance gaps. This hints that AI augmentation, if accessible, could reduce certain skill disparities within high-skill jobs by allowing a broader range of workers to achieve high-quality outputs. From a theoretical view, this resonates with the HAIC framework – rather than a zero-sum replacement, we are observing a complementary enhancement where human strengths and AI strengths together yield better productivity and quality than either could alone. It also underscores the importance of human capital: those workers and organizations that effectively adapt and incorporate AI are likely to reap disproportionate benefits (higher output, new innovations), which could widen gaps between firms or individuals if others lag in adoption. This dynamic invites careful consideration of how to ensure broad-based gains from AI, a topic we address below. Finally, the macro-level findings provide a strategic interpretation for economic planning . The projection of trillions of rubles in potential efficiency gains highlights that generative AI could become a significant driver of productivity growth in the coming decade. However, these gains will not materialize automatically; they depend on the cumulative choices of enterprises and workers across many sectors. The fact that the largest gains accrue in sectors like retail and education (which are not traditionally seen as tech-heavy) is insightful – it suggests that even moderate technological improvements, when applied to very large labor pools, can yield huge aggregate benefits. Therefore, a broad-based diffusion of AI tools (even for relatively simple augmentations in day-to-day tasks like documentation, reporting, or scheduling) could have outsized economic effects. On the other hand, the lower gains projected for specialized domains (e.g., scientific R&D or finance) imply that in those fields, either the technology has less of a foothold or the work is already highly optimized. It may also reflect that in expert domains, AI is currently used more for quality enhancement than for labor saving. In all cases, our interpretation aligns with the view that human–AI collaboration is key to unlocking these macro benefits – the emphasis is on productivity enhancement and task reallocation, not straightforward job replacement . The emergence of new professional pathways and the reallocation of human effort from mundane to higher-level tasks could, if managed well, lead to positive-sum outcomes (e.g., improved services, new industries, and growth in demand for AI-savvy talent). This paints a picture of the future workforce that is augmented by AI: many jobs will evolve to incorporate AI oversight or co-working, new roles (such as AI workflow coordinators, prompt engineers, or AI ethicists) will become commonplace, and overall economic productivity may surge during the “GenAI era” as these technologies diffuse. 5.3 Implications for Policy and Practice The uneven yet significant impact of generative AI on work tasks carries important implications for business leaders, workers, and policymakers . At the forefront, organizations should approach AI integration strategically and humanely. Rather than indiscriminately automating tasks, employers are advised to adopt a “people-first” augmentation strategy – identifying which tasks can be reliably handed off to AI and retraining employees to focus on the complementary aspects of their roles. This aligns with industry guidance such as Accenture call for a People-First Approach that prioritizes reskilling and workforce transformation as companies embrace generative AI [ 6 ]. Our findings highlight which occupations and task types should be prioritized for such interventions. For example, roles in analytics, marketing, and other high-exposure areas could be early targets for deploying GPT-based tools to handle routine data processing or content generation. By doing so, organizations can boost efficiency in these functions while simultaneously freeing employees to concentrate on creative strategy, complex decision-making, and interpersonal responsibilities that AI cannot fulfill. However, realizing these gains requires substantial investment in training and change management . Employers should invest in upskilling programs that make staff proficient in using AI tools (AI literacy), and cultivate an organizational culture that views AI as a collaborative partner rather than a threat. Notably, resistance or “AI anxiety” among employees is a real obstacle; to overcome it, leaders should emphasize success stories, involve employees in AI adoption plans, and ensure transparency about how the technology works and what data it uses. When workers understand AI’s limitations and strengths, and see it as augmenting their work rather than spying on or replacing them, they are more likely to embrace it, leading to better outcomes. Policymakers and regulators likewise have a crucial role to play in guiding the GenAI-driven transition in labor markets. Education and vocational training policies must be updated to reflect the changing skill demands: curricula should integrate data literacy, prompt engineering, and human–AI collaboration skills, preparing new entrants for AI-enhanced workplaces. Governments could partner with industry to create reskilling initiatives for mid-career workers in at-risk occupations, ensuring that those whose tasks are highly automatable are given pathways to move into more secure roles. The evidence that high-wage, high-skill jobs are also heavily exposed to AI means that continuous learning is imperative even for well-educated professionals; thus, policy support for lifelong learning and professional development in AI-related competencies will be beneficial across the board. Furthermore, labor regulations and social safety nets may need updating. As tasks shift, job descriptions and classifications might need revision to accurately capture new AI-in-the-loop responsibilities. Policymakers should also monitor for any emergent inequalities: for instance, if certain groups or regions adopt AI more slowly, targeted support or incentives might be required to prevent widening productivity gaps. On the flip side, if AI drastically increases output in certain sectors, there may be a case for sharing the gains (through higher wages or reduced working hours) to ensure workers benefit from the productivity dividend. Responsible AI governance is another policy implication — as organizations deploy GPT-4o-like systems, concerns around data privacy, algorithmic bias, and accountability for AI-generated errors will grow. Regulators should establish clear guidelines that encourage innovation while protecting workers’ rights and societal values. This includes enforcing transparency in AI decision-making (especially in high-stakes domains), mandating human oversight for critical decisions, and perhaps defining ethical standards for human–AI workplace interactions. From a broader perspective, our study supports the view that maximizing the benefits of generative AI requires focusing on augmentation over replacement . This principle should be enshrined in both company practice and policy frameworks. International labor studies (such as [ 7 ]) echo this emphasis: a majority of employers plan to focus on adapting their workforce through reskilling (77% of surveyed companies) and stress that AI should be used to complement human workers rather than replace them , accompanied by appropriate supportive measures and regulations. In practical terms, companies should establish internal guidelines for human-AI collaboration, delineating which decisions or tasks must remain under human control and how AI outputs are to be verified. They should also invest in what Accenture termed a sustainable tech foundation and ecosystem innovation : upgrading IT infrastructure to safely deploy AI at scale and collaborating with other organizations (e.g., through industry consortia or public-private partnerships) to share best practices and resources for AI adoption. By fostering an innovation ecosystem, even sectors or firms that lag in AI expertise (such as traditional industries) can catch up through knowledge transfer and shared platforms. Finally, ethical practices must guide this transition. Responsible AI use — encompassing fairness, accountability, transparency, and security — is not just a slogan but a necessity for long-term trust and efficacy. For instance, if generative AI is used to screen job candidates or evaluate performance (a possible extension of our work on task analysis), checks should be in place to prevent bias or unjust outcomes. Similarly, when AI aids in content creation, organizations should set standards to avoid the spread of misinformation or to clearly attribute AI-generated material. In summary, the implication is that human-centric policies and practices will determine whether the productivity boosts quantified in our study translate into sustainable economic and social benefits. With proactive reskilling programs, supportive governance, and a commitment to using AI as a tool for empowerment, the workforce can not only weather the AI revolution but thrive alongside it. 5.4 Limitations While this research provides valuable insights, several limitations must be acknowledged: Scope of Data (Russia-specific) : Our analysis was confined to the Russian labor market and relied on job vacancy data from a single platform (HH.ru). Labor dynamics, task compositions, and AI adoption rates can differ in other countries or even within non-digital segments of the Russian economy. Thus, caution is needed in generalizing the quantitative results beyond the studied context. Future studies should expand to diverse data sources and geographic contexts to verify if similar patterns hold. Use of GPT-4o for Task Evaluation : We utilized the GPT-4o model to estimate task automation potential, which introduces uncertainties inherent to the AI’s judgments. While we found GPT-4o’s assessments credible and consistent with external benchmarks, they are ultimately model-generated estimates . The model might misinterpret certain task descriptions or lack up-to-date knowledge of niche job requirements, potentially leading to biased or inaccurate automation scores. There was no direct human validation of each task rating in this study. This limitation suggests our findings are best viewed as indicative estimates of automation potential, not precise predictions of actual outcomes in every workplace. Task Definition and Thresholding : The way tasks were parsed from job postings and the threshold for “automatable” tasks could affect results. Job listings may not enumerate all tasks comprehensively, and they often focus on current needs, possibly underrepresenting infrequent but important duties. Moreover, we applied a certain cutoff (automation probability) to decide if a task is counted as automatable. Different threshold choices or weighting schemes might change the measured percentages. We partially addressed this by introducing a “task weight” metric (tasks × vacancies) to highlight economically significant tasks, but the approach still simplifies the complex spectrum of task automation feasibility into binary or average metrics. In reality, many tasks lie in a gray area of partial automation, which our summary metrics might not fully capture. Temporal and Technological Evolution : The study provides a snapshot based on GPT-4 generation technology as of now. AI capabilities are rapidly advancing; future models might overcome some limitations (for instance, better handling of multimodal inputs or physical reasoning) that currently constrain automation potential. Conversely, the regulatory environment and public sentiment towards AI could shift, affecting adoption. Our projection up to 2030 assumes a certain trajectory of technology improvement and uptake, but unforeseen breakthroughs or setbacks could alter that path. Thus, the long-term macroeconomic estimates carry uncertainty – they represent a maximum potential if AI is adopted extensively, rather than a guaranteed outcome. Focus on LLMs (Generative AI) Only : We specifically examined generative text-based AI. However, workplace automation can also come from other AI domains (computer vision, robotics, expert systems) and from broader process innovations. Some occupations currently showing low LLM augmentation potential (e.g., drivers or trades) might eventually be significantly impacted by non-LLM AI such as autonomous vehicles or robotic automation. Our study does not encompass these technologies. Likewise, even in high-LLM occupations, there are non-textual tasks (like creating visual designs, or performing hands-on experiments) that GPT-4o cannot assist with. The total automation potential of an occupation might be underestimated if other AI tools are considered, or overestimated if we assume GPT-4o alone can tackle everything. We focused on the generative text aspect, so our conclusions should be integrated with analyses of other AI tools for a complete picture. No Direct Measurement of Productivity Gains : We inferred potential productivity improvements from task automation rates and prior research, but we did not measure actual productivity changes in workplaces using GPT-4o. Factors like the learning curve in adopting AI, the quality of AI outputs, and human oversight required could reduce the realized productivity vs. the theoretical maximum. Additionally, productivity gains do not automatically translate to economic gains if, for example, organizational or market constraints prevent output from increasing. Our macroeconomic benefit calculations assume efficiency translates proportionally into cost savings or output – an assumption that may not hold in every scenario (e.g., if demand for the product/service is fixed). Empirical studies tracking companies that implement GPT assistance would complement our approach by revealing how much of the estimated potential is captured in practice. In summary, these limitations suggest that our findings should be viewed as exploratory and illustrative of broad trends rather than precise forecasts. They open several avenues for refinement, as discussed next, and highlight the need for ongoing research and validation as generative AI technologies and their workplace applications continue to evolve. 5.5 Future Research Directions Building on this study insights and limitations, we identify several directions for future research: Granular Task Analysis : Future work should examine which specific task attributes (e.g. complexity, standardization, creativity level) most influence automatability. Our results hinted at task complexity and workflow standardization as factors (simple, standardized tasks were easier to automate). Rigorous analysis could involve classifying tasks by complexity or interaction level and seeing how AI performance varies, helping to fine-tune the selection of tasks for automation. Longitudinal and Experimental Studies : To gauge the real-world impact of GPT-4 and similar AI on productivity, longitudinal case studies or controlled experiments within organizations are needed. Researchers could deploy generative AI tools in certain teams and measure productivity, quality, job satisfaction, and skill change over time compared to control groups. Such studies would validate (or adjust) the projected gains and identify any unintended consequences (e.g., over-reliance on AI, changes in collaboration patterns). Multi-Modal and Integrated AI Systems : Since many jobs involve non-textual tasks, future research should extend analysis to multi-modal AI (combining language, vision, and robotics). For instance, investigating how language models can cooperate with robotic process automation or computer vision systems would provide a fuller estimate of automation potential in occupations that require both cognitive and physical activities. This includes exploring solutions for professions with low LLM suitability by integrating other AI technologies​, thereby moving towards a more holistic human-AI workflow integration. Cross-Country and Cross-Industry Comparisons : It would be valuable to replicate this study’s methodology in different labor markets (e.g., countries with varying income levels or different industry structures) and across more industries. Such comparisons could reveal how cultural, economic, or regulatory differences mediate AI’s impact on work. They might also validate whether the correlation between wages and AI exposure holds universally or is context-dependent. Additionally, sectors like healthcare, law, or public services warrant focused studies, as they have unique professional norms and data sensitivity issues that affect AI adoption. Human Capital and Inequality Impacts : Further research is needed on the labor economics aspects – particularly how AI augmentation affects employment levels, wage distributions, and skill demands over time. Will AI augmentation lead to higher wages for those who master it and stagnation for those who don’t, thereby widening income inequality? Or will it democratize expertise and reduce skill premiums (since AI can assist less-skilled workers)? Economic modeling combined with empirical labor data as AI diffusion progresses can help answer these questions. This includes examining secondary effects, such as job creation in AI oversight and maintenance, and the redeployment of labor into new tasks that AI cannot do (yet). Policy and Ethical Frameworks : Interdisciplinary research involving law, ethics, and public policy is crucial to accompany technical and economic analysis. Studies could propose and evaluate frameworks for responsible AI integration in workplaces, drawing on real-world pilots. For example, what is the effect of implementing an “AI ethics audit” in a company or giving employees a formal role in governance of AI tools? What kinds of regulatory incentives or standards most effectively encourage firms to invest in worker retraining alongside AI investments? Such research would inform guidelines to ensure that AI’s productivity gains are achieved in a fair, transparent, and socially beneficial manner​. Evolution of Human-AI Collaboration Paradigms : Finally, research should continue to explore how human-AI collaboration can be optimized. As AI systems become more capable, the design of workflows will need to dynamically allocate tasks between humans and AI agents. Future studies might draw on organizational psychology and design thinking to develop new collaboration models, prototyping how teams that include AI “colleagues” operate. This line of inquiry is aligned with our broader research agenda and could yield best practices on managing “hybrid” teams, mitigating AI-related stress, and maximizing the synergistic effect noted in our conclusion. By pursuing these future directions, scholars and practitioners can deepen understanding of generative AI’s evolving role in the workplace. The goal is to continually refine both the predictive analytics (how the anticipated impact of AI is measured) and the prescriptive guidance (the way to positively shape such an impact). This opens a possibility to better ensure that the GenAI era leads to augmented human capabilities, sustainable productivity growth, and broadly shared prosperity, rather than unintended disruption. Declarations Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution Maksim Elisov contributed to the research design, created the new software used in the work, performed data collection and analysis, prepared figures and tables, reviewed literature, drafted and revised the report.Kirill Pshinnik conceived the study idea, guided the research design, data collection and analysis. Aleksandra Bordunos contributed to the research design, data analysis, literature review, drafted and revised the report.Oksana Zhirosh contributed to the research design, data analysis, literature review, drafted and revised the report.All the authors reviewed the manuscript. Data Availability Data is provided within the manuscript. References S. Samoili, M. Lopez Cobo, B. Delipetrev, F. Martinez-Plumed, E. Gomez Gutierrez, and G. De Prato, AI Watch. Defining Artificial Intelligence 2.0 , EUR 30873 EN, Luxembourg: Publications Office of the European Union, 2021. ISBN 978-92-76-42648-6. doi:10.2760/019901. Y. Jiang, X. Li, H. Luo, S. Yin, and O. Kaynak, “Quo vadis artificial intelligence?,” Discover Artif. Intell. , vol. 2, art. 4, Mar. 2022, doi: 10.1007/s44163-022-00022-8. S. Hajkowicz, C. Sanderson, S. Karimi, A. Bratanova, and C. Naughtin, (2023). Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021. Technology in Society , 74 , doi.org/10.1016/j.techsoc.2023.102260 KPMG, “Trust, attitudes, and use of artificial intelligence,” 2025. [Online]. Available: https://kpmg.com/us/en/articles/2025/trust-attitudes-and-use-of-artificial-intelligence.html Yu. Ya. Dranev, I. I. Kuchin, and M. I. Miryakov, “Economic effect of the implementation of artificial intelligence technologies in Russia,” Institute for Statistical Studies and Economics of Knowledge, NRU Higher School of Economics, Moscow, 2025. [Online]. Available: https://issek.hse.ru/news/1022068478.html Accenture, A New Era of Generative AI for Everyone , Accenture, Mar. 2023. [Online]. Available: https://www.accenture.com/content/dam/accenture/final/accenture-com/document/Accenture-A-New-Era-of-Generative-AI-for-Everyone.pdf. World Economic Forum, Future of Jobs Report 2025, Insight Report, Jan. 2025. Accessed: Apr. 26th, 2025, [Online]. Available: https://www.weforum.org/publications/the-future-of-jobs-report-2025/ T. T. Eloundou, S. Manning, P. Mishkin, and D. Rock, “GPTs are GPTs: An early look at the labor market impact potential of large language models,” Working Paper , OpenAI, OpenResearch, Univ. of Pennsylvania, Aug. 2023. [Online]. Available:https://arxiv.org/abs/2303.10130 Q. Chen, J. Ge, H. Xie, X. Xu, and Y. Yang, “Large language models at work in China’s labor market,” China Econ. Rev. , vol. 40, art. 102413, Apr. 2025, doi: 10.1016/j.chieco.2025.102413. O. A. Herrera, L. Ripani, and E. T. Ramirez, "AI and the Increase of Productivity and Labor Inequality in Latin America: Potential Impact of Large Language Models on Latin American Workforce," 2024. [Online]. Available: https://publications.iadb.org/en/ai-and-increase-productivity-and-labor-inequality-latin-america-potential-impact-large-language M. Oschinski, “Assessing the Impact of Artificial Intelligence on Germany's Labor Market: Insights from a ChatGPT Analysis, ” 2023. [Online]. Available: https://mpra.ub.uni-muenchen.de/118300/ R. Chaves et al., “Charting a course at the human–AI frontier: A paradigm matrix informed by social sciences and humanities,” AI Soc. , vol. 40, no. 2, pp. 123–134, Mar. 2025, doi:10.1007/s00146-025-02295-w. M. Raees, I. Meijerink, I. Lykourentzou, V. J. Khan, and K. Papangelis, "From explainable to interactive AI: A literature review on current trends in human-AI interaction," Int. J. Hum.-Comput. Stud. , Art. no. 103301, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1071581924000855 H. Cui and T. Yasseri, "AI-enhanced collective intelligence," Patterns, vol. 5, no. 11, 2024. [Online] Available: https://www.cell.com/patterns/fulltext/S2666-3899(24)00233-2 J. Li, J. Huang, J. Liu, and T. Zheng, “Human-AI cooperation: Modes and their effects on attitudes,” Telemat. Inform. , vol. 73, Art. no. 101862, 2022. [Online] Available: https://www.sciencedirect.com/science/article/pii/S0736585322000958 V. Kolbjørnsrud, “Designing the Intelligent Organization: Six Principles for Human-AI Collaboration,” California Management Review , vol. 66, no. 2, pp. 44–64, Nov. 2023, doi: 10.1177/00081256231211020. D. Koutrintzes, C. Spatharis, and M. Dagioglou, “Human-Aware Design for Transferring Knowledge During Human-AI Co-Learning,” in Proceedings of the Workshop on Embracing Human-Aware AI in Industry 5.0 (HAII5.0 2024) , CEUR Workshop Proceedings, vol. 3765, pp. 67–73. [Online]. Available: https://ceur-ws.org/Vol-3765/Camera_Ready_Paper-05.pdf S. Noy and W. Zhang, “Experimental evidence on the productivity effects of generative artificial intelligence,” Science , vol. 381, pp. 187–192, 2023. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.adh2586 S. Peng, E. Kalliamvakou, P. Cihon, and M. Demirer, "The impact of AI on developer productivity: Evidence from GitHub Copilot," arXiv preprint arXiv:2302.06590, 2023. [Online]. Available: https://arxiv.org/abs/2302.06590 J. Brand, A. Israeli, and D. Ngwe, "Using LLMs for Market Research," Working Paper 23-062, Jul. 29, 2024. [Online]. Available: https://www.hbs.edu/ris/Publication%20Files/23-062_ed720ebc-ec4d-4bc3-a6ba-bad8cfbd9d51.pdf F. Dell’Acqua et al, "Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality," Working Paper 24-013, Sep. 2023. [Online]. Available: https://www.hbs.edu/ris/Publication%20Files/24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf J. C. Kaufman and R. A. Beghetto, “Beyond Big and Little: The Four C Model of Creativity,” Review of General Psychology , vol. 13, no. 1, pp. 1–12, Mar. 2009, doi: 10.1037/a0013688. [Online]. Available: https://psycnet.apa.org/record/2018-70050-001 N. Jia, X. Luo, Z. Fang, and C. Liao, “When and How Artificial Intelligence Augments Employee Creativity,” Academy of Management Journal , vol. 67, no. 1, pp. 5–32, Feb. 2024, doi: 10.5465/amj.2022.0426. F. Dell’Acqua et al., “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise,” National Bureau of Economic Research, Working Paper No. 33641, Apr. 2025. doi: 10.3386/w33641. J. Zheng, Y. Hong, and A. Richter, “Artificial Trailblazing: How Human-AI Collaboration Transforms Organizational Innovation Practices,” in Proc. 58th Hawaii Int. Conf. on System Sciences (HICSS 2025) , Jan. 2025, pp. 307–316, doi: 10.24251/HICSS.2025.038. G. Secundo, C. Spilotro, J. Gast, and V. Corvello, “The transformative power of artificial intelligence within innovation ecosystems: A review and a conceptual framework,” Rev. Manag. Sci. , 2024, doi: 10.1007/s11846-024-00828-z. F. Gama and S. Magistretti, “Artificial Intelligence in Innovation Management: A Review of Innovation Capabilities and a Taxonomy of AI Applications,” J. of Product Innov. Manage. , vol. 42, no. 1, pp. 76–111, Jan. 2025, doi: 10.1111/jpim.12698. Y. Bengio, “The Consciousness Prior,” arXiv preprint arXiv:1709.08568 , Sep. 2017. [Online]. Available: https://arxiv.org/abs/1709.08568 S. Yao et al., “Tree of Thoughts: Deliberate Problem Solving with Large Language Models,” in Advances in Neural Inf. Process. Syst. 36 (NeurIPS 2023), 2023. [Online]. Available: https://arxiv.org/abs/2305.1060 K. C. Hong, A. S. Shibghatullah, T. C. Ling, and S. Sarsam, “Generative AI-powered predictive analytics model: Leveraging synthetic datasets to determine ERP adoption success through critical success factors,” Int. J. Adv. Comput. Sci. Appl. , vol. 15, no. 5, pp. 469–482, 2024.. [Online]. M. Mateev, “Predictive analytics based on digital twins, generative AI, and ChatGPT,” in Proc. 27th World Multi-Conf. Syst., Cybern. Inform. (WMSCI) , 2023. [Online]. Available: https://www.iiis.org/DOI2023/SA437PH/ S. Joshi, "The Synergy of Generative AI and Big Data for Financial Risk: Review of Recent Developments," Int. J. Multidiscip. Res. (IJFMR) , vol. 7, no. 1, Jan.-Feb. 2025. [Online]. Available: https://www.researchgate.net/publication/388398425_The_Synergy_of_Generative_AI_and_Big_Data_for_Financial_Risk_Review_of_Recent_Developments Federal State Statistics Service (Rosstat), "Russian Statistical Yearbook 2024," [Online]. Available: Russian Statistical Yearbook. SberIndex, “Median Monthly Wages Dashboard,” Sberbank, Moscow, Russia, Feb. 2025. [Online]. Available: https://sberindex.ru/en/dashboards/median-wages F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User acceptance of computer technology: A comparison of two theoretical models,” Management Science , vol. 35, no. 8, pp. 982–1003, Aug. 1989, doi: 10.1287/mnsc.35.8.982. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":116142,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the analysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7173895/v1/049ba45939d4b7418a5a82fe.png"},{"id":94979029,"identity":"9f41139d-c4b4-4865-817c-8a4c95df1c2f","added_by":"auto","created_at":"2025-11-03 04:44:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33172,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between salary and automation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7173895/v1/68fce398f3b90c69707d8a9c.png"},{"id":99788403,"identity":"a301c69b-6e44-4327-ab0e-af30e76fd8c4","added_by":"auto","created_at":"2026-01-08 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Introduction","content":"\u003cp\u003eThe integration of AI technologies, particularly generative AI like ChatGPT or Midjourney, is increasingly recognized as a transformative force in the workplace that improves decision-making process, increases productivity and creativity in the daily routine, and streamlines operations. While several waves of concerns, a so-called \u0026ldquo;AI winter\u0026rdquo; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], witnessed skepticism, which primarily reduced interest and funding in artificial intelligence projects, the current situation can be described as \u0026ldquo;AI spring\u0026rdquo; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] with a growing body of reports and scholarly literature related to introducing generative AI tools in various occupations, tasks, and efforts to evaluate their efficiency. The potential financial impact of AI adoption on the economy of Russia by 2030 is estimated as high as 11.6 trillion Russian rubles [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These observations motivate further search for the possible means and grounds for proof of real shifts in workforce efficiency and for prediction of the potential for raising productivity\u003c/p\u003e\u003cp\u003efor each occupation due to generative AI technologies.\u003c/p\u003e\u003cp\u003eThe emerging trend for the organizations is not just to experiment with generative AI, but to actively implement generative AI tools into daily routine. Accenture report [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] highlights that generative AI has the potential to significantly enhance productivity across industries while urging organizations to adopt these technologies responsibly and strategically. Accenture outlines six essentials for organizations to embrace generative AI:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBusiness-driven mindset: foster experimentation with generative AI.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePeople-first approach: prioritize reskilling and workforce transformation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eData readiness: prepare proprietary data for effective model training.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSustainable tech foundation: invest in infrastructure to support AI deployment.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEcosystem innovation: collaborate across sectors to enhance AI applications.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eResponsible AI: ensure ethical practices in AI development and deployment.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eWhile some companies are at the experimentation stage of generative AI adoption, others are already experiencing the benefits of the technology's disruptive potential. According to The Future of Jobs Report 2025 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] by the World Economic Forum (WEF), 83% of the surveyed employers believe that advancement in AI and\u003c/p\u003e\u003cp\u003einformation processing technologies will be one of the main drivers of business transformation during 2025\u0026ndash;2030. The Report concludes that AI adoption is growing rapidly but unevenly, with the information technology sector leading the way while industries like construction lagging behind. This disparity reflects broader trends, as advanced and middle-income economies embrace generative AI more widely than low-income economies, where its use currently remains minimal. The said Report claims that in response to AI adoption, companies manifest a strong focus on adapting existing workforces via reskilling and upskilling (77%) and acquiring new talent to navigate the changing landscape brought about by AI. To maximize benefits and avoid widening inequality, AI development should focus on augmenting human capabilities rather than replacing them, supported by appropriate frameworks, incentives, and regulations. For the purpose of this project, we use the term \u0026ldquo;automate\u0026rdquo; with regard to specific tasks and the term \u0026ldquo;augment\u0026rdquo; with regard to an occupation. The high potential of augmentation of an occupation implies a high number of tasks that can be automated within the occupation.\u003c/p\u003e\u003cp\u003eLiterature on the affordances of the use of AI in various industries and application for various tasks is gaining momentum. The current research was motivated by the need to overcome inflated expectations and subsequent disappointment of large-scale companies from investing into adoption of generative AI technologies by searching for exact measures how increased efficiency could be estimated and improved. Overall, the research findings aim to provide a basis for predictions of opportunities to transform particular job functions in order to foster efficiency by the use of generative AI.\u003c/p\u003e\u003cp\u003eThis paper reports a part of our broader research effort that aims to introduce generative AI-use training intervention for personnel and measure the impact of the said intervention on the personnel efficiency. To select and prioritise tasks and occupations for such a training intervention, we draw upon the methodology presented in [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which introduced an approach to evaluate task exposure and automation potential using GPT-4 LLM and measured overall exposure in the contemporary U.S. labor market. Our project thus contributes to the growing body of literature on the methodologies and results of the evaluation of potential use of generative AI tools and its impact on the labour markets, e.g., China [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], Latin America (Chile, Peru, Mexico) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], EU and, specifically, Germany [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur project contribution is threefold:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ewe analyze the data of the labour market in Russia and advise on the task prioritization for AI-use educational intervention;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ethe data for the analysis were collected from real vacancies (as compared to expert description of tasks in O-NET base in 7)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ewe used a newer model, i.e., Chat GPT-4o, for data analysis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eSpecifically, we address the following research questions:\u003c/p\u003e\u003cp\u003eRQ1. What types of tasks in various occupations could be automated by generative AI?\u003c/p\u003e\u003cp\u003eRQ2. Is it possible to assess the potential of such tasks for automation with the GPT-4o model?\u003c/p\u003e\u003cp\u003eRQ3. How do the occupations range by the potential of augmentation?\u003c/p\u003e\u003cp\u003eRQ4. What is the potential financial impact of generative AI augmentation for the labour market?\u003c/p\u003e\u003cp\u003eThe above research questions motivated the following study design: first, we parsed vacancies from the website of a staff recruitment service provider Headhunter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hh.ru/\u003c/span\u003e\u003cspan address=\"https://hh.ru/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e);\u003c/span\u003e then, we uploaded the collected data to ChatGPT-4о LLM and prompted it to, first, retrieve all tasks from vacancies descriptions, and, second, build detailed predictions about the GenAI-driven automation potential of the retrieved tasks for particular occupations; further, we assigned weights to the retrieved tasks based on the frequency of their occurrence in the resulting list of tasks; finally, we categorized the tasks by functional areas, key competencies, and industrial fields, and completed the data analysis by estimation of the economic impact of the adoption of GenAI. As a result, we identified no tasks or occupations that are susceptible to complete (100%) automation; the maximum automation potential observed for any individual task is 85%, while the highest augmentation potential for an occupation is 70%. The sectors of culture, sport, leisure and entertainment; real estate operations; and information and communication exhibited the greatest augmentation potential \u0026mdash; 63%, 58.8%, and 49.6%, respectively. A positive correlation between augmentation potential and wage levels was identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The projected potential financial impact by 2030 in Russia is estimated to reach 10.8 trillion rubles.\u003c/p\u003e\u003cp\u003eThe rest of this paper takes the form of five sections. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents an overview of the literature related to LLM potential for augmentation; Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e, details the methods of data collection and analysis; Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results, Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e5\u003c/span\u003e, interprets the results and offers the directions of future research.\u003c/p\u003e"},{"header":"2. LLMs potential for augmentation.","content":"\u003cp\u003eThe ways humans use AI is being investigated from various perspectives, and the conceptualizations of this phenomenon has been increasingly shifting towards the idea of shared agency when humans and AI do or can leverage each other\u0026rsquo;s strengths, e.g., [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this work we rely on the concept of Human-AI Collaboration (HAIC), specifically in the sense articulated in [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], i.e., when humans and AI \u0026ldquo;\u003cem\u003eperform interdependent actions\u0026rdquo;.\u003c/em\u003e In the analyzed literature, HAIC is seen as a transformative aspect of the labor process; with regard to AI potential for augmentation, the main themes are related to improved efficiency and productivity, creativity and innovation, decision-making, and quality.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Efficiency and productivity\u003c/h2\u003e\n \u003cp\u003eLLMs have a significant impact on operational efficiency. Experimental studies demonstrate that the application of generative AI can substantially reduce the time required to perform routine or standardized tasks while simultaneously enhancing result quality. For example, the use of ChatGPT in professional writing leads to reduced time expenditures and higher quality assessments of the final product. In practical terms, when composing press releases or commercial proposals, ChatGPT helps structure the text, correct style and grammar, and thus accelerates document preparation while minimizing errors [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. In the IT sector, GitHub Copilot considerably speeds up code development processes by enabling programmers to complete tasks 55.8% faster compared to traditional methods. Moreover, the Copilot not only generates templated solutions but also suggests optimizations, identifies potential errors, and assists in improving software architecture \u0026ndash; an especially valuable feature under tight deadlines and intense competition [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eFurthermore, LLMs are finding application in market research, where their ability to generate plausible responses to consumer inquiries allows for cost-effective data collection. In the study by Brand et al. [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], it is shown that modeling consumer preferences with the help of LLMs yields results comparable to those obtained from traditional surveys, but with considerably less time and financial investment. For instance, companies can use LLMs to simulate audience reactions to a new product line, enabling them to quickly adjust marketing strategies and identify potential strengths and weaknesses in the offering at early stages of product development.\u003c/p\u003e\n \u003cp\u003eBesides, significant changes are observed at the macroeconomic and labor market levels. Impact assessments indicate that the adoption of LLM technologies can not only automate a substantial portion of work tasks but also markedly boost overall labor productivity. Eloundou et al. [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] demonstrate that, thanks to LLMs, approximately 15% of all work tasks can be completed significantly faster; when supported by LLM-powered software, this figure increases to between 47% and 56%. Such improvements facilitate the optimization of internal processes within organizations, the reallocation of human resources, and the emergence of new professional roles associated with coordinating human\u0026ndash;AI interactions. Similar conclusions are corroborated by Dell\u0026rsquo;Acqua et al. [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], consultants who used AI for tasks within AI capability frontier completed 12.2% more tasks, 25.1% faster, with over 40% higher quality results; lower-performing consultants improved by 43% and higher-performing ones by 17% compared to their baselines.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Creativity and Innovation\u003c/h2\u003e\n \u003cp\u003eKaufman and Beghetto proposed a 4C model of creativity: mini-c, little-c, Pro-c, Big-C [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Here we mostly address Pro-c, i.e., professional creativity, a domain-specific socially-recognised form of creativity, as the most relevant for this study. An example of the positive impact of HAIC on human innovative thinking is freeing up time and attention of humans for more creative tasks by assigning routine, automatable, tasks to AI. In [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] AI assistance led to a significant increase in creativity among higher-skilled employees, enabling them to generate more innovative solutions and improve performance. When AI handled the repetitive and codified parts of a task, employees could focus on more complex, creative problem-solving. Another example of such an impact on team work is described in [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], which, among other research questions, investigated how team expertise is leveraged in new product development tasks with the use of AI. During ideation stage, without AI assistance, technical and commercial specialists tended to produce ideas within their professional background while overseeing other perspectives; with AI assistance, however, the said specialists engaged in more holistic, interdisciplinary thinking, which enabled them to produce more balanced ideas without compromising the effectiveness of the suggested solutions.\u003c/p\u003e\n \u003cp\u003eFrom a theoretical perspective, a systematic literature review [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] suggests that HAIC has the potential to transform organisational innovation practices via seven dimensions: Predicting, Assessing, Decision-making, Problem- solving, Adjusting, Cultivating, and Absorbing. Further, [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] systematically investigate how the latest developments in AI platforms and technologies influence innovation ecosystems enabling new ways to generate value. They extend the existing theories of innovation ecosystems and propose a new conceptualized framework, an \u0026ldquo;AI innovation ecosystem\u0026rdquo; consisting of three essential elements, i.e., actors and roles, data- driven decision-making process impacted by AI, and value generated by AI. They also introduce an \u0026ldquo;AI-Collaboration Matrix within Innovation Ecosystems\u0026rdquo; that illustrates how different levels of AI adoption, combined with varying degrees of collaboration, impact innovation results. Another conceptualization of AI adoption in innovative firms is presented in [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Drawing upon a systematic analysis of empirical studies, they proposed a framework that represents the influence of AI adoption on innovation capabilities. The framework consists of two sets of capabilities: six \u003cem\u003eenabling capabilities\u003c/em\u003e, and seven \u003cem\u003eenhancing capabilities\u003c/em\u003e, both influenced by the technological, organizational, and environmental context. The \u003cem\u003eenabling capabilities\u003c/em\u003e are necessary to allow for AI adoption, while the \u003cem\u003eenhancing capabilities\u003c/em\u003e are experienced by innovating firms as a result of AI adoption and allow for the transformation of innovation practices. Besides, the authors suggest a taxonomy of AI applications, i.e., \u003cem\u003ereplace, reinforce, reveal\u003c/em\u003e, with the latter capable to \u0026ldquo;unveil hidden technological opportunities and unshadow unforeseeable external situations\u0026rdquo; [p.91, 27].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Decision-making\u003c/h2\u003e\n \u003cp\u003eDue to a shift from rapid, heuristic-based processes performed by LLMs (System 1) to slower, more analytical and structured methods of information processing (System 2) [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. This evolution enables the models not only to generate text but also to perform complex logical reasoning and planning. For instance, the authors in [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] illustrate the application of these models in solving complex mathematical and optimization problems that require step-by-step analysis and iterative refinement of intermediate solutions.\u003c/p\u003e\n \u003cp\u003eAt the cognitive level, there is an enhancement of \u0026quot;slow thinking\u0026quot;, wherein LLMs are employed to support multi-stage reasoning and decision-making. The models are capable of thoroughly analyzing tasks, highlighting key aspects, and offering several solution alternatives, thereby reducing the likelihood of errors and improving the overall quality of the final output. Such capabilities are particularly crucial in areas where precision is critical\u0026mdash;such as in the development of complex software products or in legal research, where detailed analysis and logical justification are required [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eStudy [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] aimed at helping to overcome data scarcity for predictive modeling of ERP adoption. For this, GANs and VAEs were used to create synthetic ERP adoption data; the generated data closely matched real data, as confirmed by statistical tests; the system allows for more informed decision-making. In [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] the suggested system combined Digital Twins and Generative AI (like ChatGPT), which enabled it to quickly and accurately detect issues in the test scenarios as well as learn from past data. This predictive capability can transform decision-making from reactive to proactive, reducing costs, minimizing downtime, and optimizing performance. A review in [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] explores the application of AI in Finance, and concludes that integrating Generative AI models like GPT-4 with Big Data enhances, among others, predictive accuracy and creates high-quality synthetic data, which leads to major improvements in data engineering and enterprise analytics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Quality\u003c/h2\u003e\n \u003cp\u003eNoy and Zhang [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] demonstrate that the use of ChatGPT in professional writing tasks significantly enhances output quality, with treatment group participants achieving scores 0.45 standard deviations higher than those in the control group, reflecting improvements in overall quality as well as in specific dimensions such as writing quality, content, and originality. The grade distribution shifted upward across the board, indicating a robust quality enhancement that was particularly pronounced among individuals with lower baseline skills, thereby reducing productivity inequality. This effect appears to stem primarily from ChatGPT substituting for routine drafting efforts, which allows users to reallocate time towards editing and refinement; notably, even when participants submitted ChatGPT\u0026rsquo;s raw output without significant modification, the quality was substantially superior to that achieved without AI assistance. Dell\u0026apos;Acqua et al. in [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], however, assert that the quality of outputs depends on whether the tasks assigned to AI are within or outside of the \u0026ldquo;jagged technological frontier\u0026rdquo;, i.e., AI capabilities for particular types of tasks. In their experiment, the quality of the outputs obtained with the use of GPT-4 for tasks within AI capabilities was graded 43% higher compared to the control group (no AI used) for the lower-skilled consultants and 17% higher for the higher-skilled consultants. For the tasks outside of AI capabilities, though, the outputs of the consultants relying on AI showed 19% lower quality than the outputs of those working without AI.\u003c/p\u003e\n \u003cp\u003eIn conclusion, the integration of LLMs across various professional domains is likely to be accompanied by complex processes that merge the automation of routine operations, the improvement of complex analytical tasks, and the emergence of new forms of interaction between humans and AI.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Methods","content":"\u003cp\u003eThis section details data collection and analysis processes we used to address our research questions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data collection\u003c/h2\u003e\u003cp\u003eTo accumulate the data, we first made a Python-based program. The program collects job vacancies data in HeadHunter (HH.ru) API. We consider the HH.ru platform representative enough for the purpose of data collection (see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stats.hh.ru\u003c/span\u003e\u003cspan address=\"https://stats.hh.ru\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The program provides functionality to search for vacancies by occupation title and to save the results in an Excel file with the .xlsx format. We collected such data for 78 most popular occupations; the criterion of popularity implies the highest numbers of vacancies posted on the said website for a particular occupation; the said 78 occupations represent more than 85% of all the open vacancies in HH.ru. We took 100 vacancies for each occupation from the January 2025 data list. On \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHH.ru\u003c/span\u003e the vacancies are automatically deactivated and become unavailable for open access 30 days after activation; hence the retrieved list might fail to respond to the changes related to, e.g., seasonality. Additional analysis revealed that increasing the sample size to over 100 vacancies did not lead to a significant improvement in estimate accuracy or produce substantial changes in the model's responses. Initially, we compiled a list of vacancy IDs matching specific parameters. The program works as follows: a GET request is sent to the HH API with the following parameters: search text (occupation titles), region code (search can be restricted to specific cities, regions, or countries, we used data for the country). The API response is checked for success, and the data are returned in JSON format. Each suitable vacancy is added to a table that includes its title and URL. The function stops as soon as the required number of vacancies has been collected or when there are no more vacancies on the current page. Next, the program processes the vacancy links obtained from the HH.ru website, extracts data for each vacancy using the HH API, cleans and structures the information, and then saves the results to a new Excel file. Standard Python libraries were used throughout this procedure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Data pre-processing\u003c/h2\u003e\u003cp\u003eUpon collection, the textual data were preprocessed by removing HTML tags, decoding HTML entities, and eliminating special characters from the vacancy descriptions, thereby preserving only plain text. After that, the vacancies were retrieved using the identifiers collected in the previous stage. For each identifier, a GET request was sent to the HH.ru API in order to obtain structured vacancy data, including the job title, employer name, location (city), salary, and description. The program accepts an Excel file containing a list of vacancy URLs, from which it extracts and validates each link. If a URL matches the standard HH.ru vacancy format, the corresponding vacancy ID is extracted and used to query the API. The retrieved data are appended to a list, and the final output is organized in tabular form.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 AI processing\u003c/h2\u003e\u003cp\u003eFor the neural network-based processing of vacancies, a dedicated table was constructed containing only the textual descriptions of the vacancies. The program performs asynchronous POST requests to the OpenAI API, utilizing parameters such as the model specification, prompt, and user input. For each row in the table, an individual task is generated to query the API. These tasks are executed concurrently, which significantly improves the processing speed for large datasets. Once the API responses are received, they are written to the resulting DataFrame on a row-by-row basis, with the processing status recorded for each entry.\u003c/p\u003e\u003cp\u003eFollowing this, all tasks are processed using the \u003cem\u003eChatGPT-o1-mini\u003c/em\u003e model. At this stage, a different model was employed due to its better performance in processing textual data. Although this model is more effective for natural language understanding tasks, it also incurs a higher usage cost. An expandable list of unique tasks is initialized. For each incoming task, the model receives both the current list of unique tasks and a new task from the complete set. If the task can be categorized as matching one of the existing unique tasks, the corresponding task rating is incremented by one. If no match is found (or if the list is initially empty), the task is appended to the list as a new unique task with an initial rating of one. Here, the rating reflects the frequency with which the task appears in the dataset.\u003c/p\u003e\u003cp\u003eThe resulting set of unique tasks is subsequently analyzed by the neural network to assess their potential for automation via large language models (LLMs). Automation potential is defined as the extent to which the use of an LLM may accelerate the execution of a given task. Tasks deemed unsuitable for automation receive a score of 0%.\u003c/p\u003e\u003cp\u003eThe structured response returned by the API is then parsed and decoded as JSON. If the response contains keys and examples, these are incorporated into the final dictionary. For each task, a distinct row is created in the output, containing the original task description, its evaluated characteristics, and, where available, examples. The final results are saved to a new Excel file.\u003c/p\u003e\u003cp\u003eFinally, a metric termed \u0026ldquo;task weight\u0026rdquo; is introduced. It is defined as the product of the number of relevant vacancies and the automation potential score (expressed as a percentage). This value reflects each task\u0026rsquo;s contribution to the overall automation potential of a vacancy. Tasks assigned an automation score of 20% or lower are considered economically inefficient to automate, and their contribution is therefore treated as zero. The overall automation score for a vacancy is calculated as the ratio of the cumulative task weights (with the threshold applied) to the total number of tasks.\u003c/p\u003e\u003cp\u003eThe threshold is justified by the observation that the neural network does not consistently assign a 0% automation score, even for tasks that are not automatable. This behavior can be attributed to the fact that large language models (LLMs) may still be applicable in auxiliary roles\u0026mdash;such as suggesting routes, identifying recreational activities, or commenting on user actions. While these applications may theoretically support certain tasks, they are often of marginal utility or entail higher implementation costs than the benefits they yield.\u003c/p\u003e\u003cp\u003eInitially we generated a workflow with the two alternative scenarios: with and without the extended list (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Current research is based on the extended scenario, because it provides task weights.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e4.1 The potential of augmentation within occupations\u003c/strong\u003e.\u003c/h2\u003e\n \u003cp\u003eTable A-\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e (Appendix A) generated within the above framework represents an analytical tool for the quantitative evaluation of the potential for automating professional tasks using large language models (LLMs) based on data obtained via the HH.ru API. The table reflects two key indicators: the total number of unique tasks identified in the job listings, and the average automation percentage calculated for all tasks as well as for the subset of tasks with ratings exceeding the established threshold. This approach allows not only for the assessment of the overall potential of applying LLMs but also for highlighting those aspects of professional activities in which the implementation of neural network technologies may lead to a significant increase in productivity. For the purpose of this project we use the term \u0026ldquo;automate\u0026rdquo; with regard to specific tasks and the term \u0026ldquo;augment\u0026rdquo; with regard to an occupation. The high potential of augmentation of an occupation implies a high number of tasks that can be automated within the occupation.\u003c/p\u003e\n \u003cp\u003eData analysis showed that occupations oriented toward information processing, analytics, and managerial functions demonstrate a high level of automation (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This is due to the fact that the nature of their tasks entails the active use of textual information, which aligns closely with the functional capabilities of modern language models (e.g., Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The high automation metrics in these areas indicate a significant potential for improving the efficiency of work processes through the implementation of LLMs.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop Ten Occupations with the Highest Potential for Augmentation.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOccupations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Automation % (\u0026ge;\u0026thinsp;20%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresentation Designer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDispatcher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProduct Analyst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWriter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData Engineer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarketplace Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Analyst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHead of Analytics Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcurement Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePotential of Tasks for Automation within Presentation Designer Occupation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOriginal Task\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAutomatability\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreating visual content for a presentation based on provided text.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDesigning an appealing presentation to introduce a new product.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreating a unique design for a new product launch.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloping a presentation for a business meeting, including charts and graphs to illustrate key data.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreating a visual concept for a presentation, including selecting a color palette, fonts, and graphic elements.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDesigning a presentation for a corporate event, including theme selection, slide design, and visual content creation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreating slide animations to make information more visual and retain audience attention.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eConversely, our study results reveal that professions associated with manual labor or direct interaction with machinery, such as drivers, exhibit a considerably lower potential for automation (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In these fields, work processes depend on physical actions and specific skills that are not amenable to direct processing by language models (e. g., Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Thus, the observed imbalance in automation indicators suggests that the effectiveness of LLM applications varies significantly depending on the nature of the tasks performed.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTen Occupations with the Lowest Potential for Augmentation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Automation %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNanny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocksmith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecurity Guard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrician/Installer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCourier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurner (Lathe Operator)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWelder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTractor Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCleaner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePotential of Tasks for Automation within the Occupation of Driver\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOriginal Task\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAutomatability\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccompanying the executive on trips around the city, region, and other areas.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnsuring the safety and well-being of passengers on public transportation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCar maintenance, including keeping it clean and in working condition.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProviding safe and comfortable transportation for the company executive and their family.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelivering goods and materials to retail locations within the city.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompliance with traffic regulations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManaging the process of renting or servicing the vehicle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreparing documents.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpense reporting.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe occupations were clustered based on three distinct principles: by Functional focus, by Industry sector and by Key skills and Competencies. For each resulting cluster, the average automation level was calculated within each category, representing the proportion of tasks within that category that are susceptible to automation. This value reflects the estimated automation potential for each category under the respective clustering scheme. The data in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e suggest that AI augmentation will thrive in knowledge-intensive, creative, and analytical fields, while its impact will be more limited in manual or highly regulated industries. Tailoring AI solutions to sector-specific needs (e.g., automated analytics for finance, design aids for creatives) will maximize adoption.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProspects of Augmentation in Different Industries\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eA. By Functional Focus\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFunctional Area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Potential Automation %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreativity and Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIT and Digital Technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnalytics and Business Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation, HR, and Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine and Healthcare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecialized Services and Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngineering and Technical Manufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eB. By Industry Sector\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndustry Sector\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Potential Automation %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Technologies and Communications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinance, Analytics, and Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation and Workforce Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthcare and Social Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecialized Services and Transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngineering, Manufacturing, and Construction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eC. By Key Skills and Competencies\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKey Skills and Competencies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Potential Automation %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreative Thinking and Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnalytical and Managerial Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional Services and Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnical and Software Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Potential financial impact of augmentation with AI on the labor market.\u003c/h2\u003e\n \u003cp\u003eThe presented data (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) evaluate the potential impact of generative neural networks, particularly large language models (LLMs), on the labor market in Russia across various economic sectors. The sectors are identical to the ones used by Rosstat [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. The analysis focuses on the extent to which LLMs can enhance worker productivity, the average monthly salaries by sector, the proportion of the national workforce employed in each area, and the resulting macroeconomic effects. The metric labeled \u0026ldquo;automation\u0026rdquo; reflects the estimated productivity increase from the integration of LLMs. Automation rates for each sector were computed as the mean of the individual automation percentages of the occupations comprising that sector. By multiplying this metric with employment distribution and salary data, the study estimates the annual economic benefit that could be realized through efficiency gains. Notably, sectors with broad employment coverage and moderate automation potential\u0026mdash;such as Wholesale and Retail Trade, as well as education\u0026mdash;demonstrate the highest estimated yearly savings, exceeding 2.28 and 1.32 trillion rubles respectively. Conversely, areas requiring deep domain expertise, such as scientific research and development, are associated with lower automation potential and correspondingly smaller economic gains. These findings emphasize not the replacement of workers, but rather the opportunity for task redistribution and productivity enhancement, which may lead to GDP growth, labor market transformation, and the emergence of new professional pathways. We estimate the maximum effect of the introduction of neural networks in the Russian labor market by 2030 at 10.79 trillion rubles, which is close to the estimate from the Higher School of Economics [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistics based on Rosstat data [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActivity Sector\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAutomation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Salary (RUB)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShare of Population\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual Savings, Trillion RUB\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWholesale and Retail Trade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽66,226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation and Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽136,988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽54,315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture, Forestry, Hunting, Fishing and Aquaculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽54,158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽71,707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional, Scientific, and Technical Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽108,253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScientific Research and Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽120,790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransportation and Storage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽76,223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthcare and Social Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽61,651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial and Insurance Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽170,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReal Estate Operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽55,443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽71,855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArts, Sports, Entertainment, and Recreation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽65,702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdministrative and Support Service Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e₽50,573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBy 2030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWe also divided the occupations into four categories depending on the level of automation (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The data on wages were retrieved from the HH.ru website. The results indicate a statistically significant positive correlation between the level of wages and automation potential across occupations (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These results are consistent with those in [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] and [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDivision by automation sectors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriterion, % of automation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Automation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Salary (thousand RUB)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60%+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;59.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;39.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;19.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn recent years, the Russian labor market has witnessed a marked shift in wage dynamics, particularly among traditionally manual labor occupations. These professions \u0026mdash; historically associated with lower compensation and limited potential for automation by large language models (LLMs) \u0026mdash; have experienced substantial wage increases, in some cases earning several times more than in prior years [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. This structural realignment may have attenuated the observed relationship between income and augmentation. The correlation analysis across 78 professions yielded a Pearson coefficient of r\u0026thinsp;=\u0026thinsp;0.381, with a statistically significant p-value of 0.0006, indicating a moderate positive association. However, the 95% confidence interval for r, ranging from 0.172 to 0.557, suggests considerable variability. The inflation of wages among less automatable occupations could be contributing to this broader interval and the dilution of a stronger trend, thereby partially masking the extent to which higher salaries may otherwise correlate with greater exposure to automation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion and conclusion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Summary of Findings\u003c/h2\u003e\u003cp\u003eThis study set out to evaluate the possible transformative impact of generative AI (specifically GPT-4o) on workplace tasks and productivity in the Russian labor market. Overall, the results provide robust evidence that \u003cb\u003ethe potential for AI-driven task augmentation is significant but highly uneven across task types and occupations.\u003c/b\u003e Tasks heavily involving information processing, data analysis, and other text-centric activities exhibit the greatest augmentation potential. For example, occupations in fields like data science, marketing, and management \u0026mdash; where daily work revolves around generating or analyzing text-based information \u0026mdash; showed markedly high automation scores, indicating that a large portion of their routine tasks could be accelerated or enhanced with GPT-4o assistance. Automation is also high for certain professions, such as dispatchers, due to the ability of large language models (LLMs) to effectively receive, process, and relay information to the appropriate recipients. In contrast, roles that require physical labor, direct manipulation of the environment, or face-to-face interaction (e.g. drivers, machine operators, and similar manual-intensive jobs) consistently scored \u003cb\u003elow on automatability\u003c/b\u003e, as their core tasks are not readily handled by current language models. This clear disparity supports our first hypothesis (RQ1) that the \u003cb\u003eamenability of tasks to generative AI depends on task nature\u003c/b\u003e: cognitively intensive and textual tasks are far more augmentable than physically intensive ones.\u003c/p\u003e\u003cp\u003eCrucially, our methodology leveraged GPT-4o to assess task automation potential using real-world job vacancy data, rather than relying solely on expert judgments or static occupational databases. The \u003cb\u003eGPT-4o-based assessment proved to be feasible and insightful (addressing RQ2)\u003c/b\u003e. The model was able to parse thousands of job listings, identify the tasks involved, and evaluate each task\u0026rsquo;s likelihood of being automated or enhanced by AI. The resulting estimates were not only intuitively plausible but also exhibited \u003cb\u003econvergent validity\u003c/b\u003e when compared with external indicators and studies. Notably, occupations that GPT-4o identified as highly augmentable tend to be those with higher average wages, and indeed we observed a positive correlation between an occupation\u0026rsquo;s automation percentage and its salary level. This aligns with prior research from the University of Pennsylvania, [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] which found that roles commanding higher wages often have greater exposure to AI technologies. In essence, GPT-4o\u0026rsquo;s predictions mirrored known patterns (e.g., that well-paid, knowledge-intensive jobs contain many tasks AI can assist with), lending support to the accuracy of our approach. The third and fourth research questions (RQ3 and RQ4), concerning how augmentation potential varies across occupations and which occupations rank highest, were also affirmatively answered. We found substantial \u003cb\u003evariability across the occupational spectrum\u003c/b\u003e: a handful of professions emerged as clear front-runners for AI augmentation (with task automation potentials well above the chosen threshold), while others lagged far behind. Indeed, we were able to stratify jobs into four broad categories of automation readiness (from low to high), a categorization that could guide where AI interventions might be most impactful first.\u003c/p\u003e\u003cp\u003eA high level of automation was observed in the sectors of \u003cem\u003eReal Estate Operations\u003c/em\u003e and \u003cem\u003eArts, Sports, Entertainment, and Recreation\u003c/em\u003e. In the real estate domain, this can be attributed to occupations such as realtors, which involve tasks like analyzing large volumes of data, generating listings, and interacting with online platforms\u0026mdash;all of which can be significantly enhanced by large language models (LLMs). In the creative sector, tasks such as writing poetry or generating visual artwork can also be substantially accelerated through the use of neural networks, highlighting the growing applicability of generative AI in domains traditionally viewed as human-centric.\u003c/p\u003e\u003cp\u003eZooming out to the macroeconomic perspective (RQ5), the findings indicate that widespread adoption of generative AI in the workplace could yield \u003cb\u003esignificant productivity and efficiency boosts and economic gains\u003c/b\u003e, albeit distributed unevenly across sectors. By combining each sector's automation potential (as estimated by GPT-4o) with employment and wage data, we estimated the potential annual efficiency gains in monetary terms. The results suggest that certain large-employment sectors with moderate AI amenability stand to gain the most in absolute terms. For instance, \u003cb\u003ewholesale and retail trade\u003c/b\u003e and \u003cb\u003eeducation\u003c/b\u003e \u0026mdash; sectors that employ a substantial share of the workforce \u0026mdash; could each realize yearly productivity benefits on the order of \u003cb\u003e₽1\u0026ndash;2 trillion\u003c/b\u003e through task augmentation. In contrast, sectors requiring highly specialized human expertise, such as scientific R\u0026amp;D, showed lower augmentation percentages and consequently smaller aggregate gains. Summing across industries, the \u003cb\u003eupper-bound estimate\u003c/b\u003e for economy-wide impact is considerable: our analysis suggests that by 2030, generative AI integration could contribute up to roughly \u003cb\u003e₽10.79\u003c/b\u003e trillion in efficiency-related gains annually in Russia\u0026rsquo;s labor market. This figure is in line with independent projections by national research bodies (e.g., a similar estimate by the Higher School of Economics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]). It is important to note that these gains reflect improved productivity and task efficiency rather than outright replacement of workers. In fact, a key insight is that generative AI\u0026rsquo;s value in this context lies in \u003cb\u003eaugmenting human labor\u003c/b\u003e \u0026mdash; freeing workers from tedious tasks and thereby enabling labor redistribution towards more complex or creative activities \u0026mdash; which can catalyze \u003cb\u003eGDP growth, labor market transformation, and the emergence of new roles\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Interpretation of Results\u003c/h2\u003e\u003cp\u003eThe above findings carry several important interpretations for theory and practice. First, they strongly reinforce the notion that \u003cb\u003etask characteristics are a decisive factor\u003c/b\u003e in determining AI\u0026rsquo;s impact. This was anticipated by our theoretical framing: according to the Technology Acceptance Model (TAM) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the likelihood of adopting a new technology depends on its perceived usefulness and ease of use for a given job function. GPT-4o effectively has a higher \u0026ldquo;perceived usefulness\u0026rdquo; for tasks that are already digital and information-centric, since it can be readily applied to generate text, analyze language, or expedite information workflows. Our empirical evidence supports this TAM-based expectation \u0026ndash; roles where an AI like GPT-4o can be easily applied showed far greater productivity uplift than roles where it cannot. In practical terms, \u003cb\u003ejobs involving routine cognitive processing were most conducive to AI augmentation\u003c/b\u003e, because GPT-4o could seamlessly slot into those workflows (e.g. drafting reports, writing code, summarizing data). By contrast, in occupations centered on physical skills or interpersonal interaction, the model\u0026rsquo;s utility is inherently limited, explaining the low augmentation indices observed there. This divergence underscores a critical point: current generative AI excels at \u003cem\u003esubstituting or speeding up information processing sub-tasks\u003c/em\u003e, but it \u003cb\u003estruggles with tasks requiring embodiment, physical manipulation, or complex social intelligence\u003c/b\u003e. From a human\u0026ndash;AI collaboration (HAIC) standpoint, this means the \u003cb\u003eoptimal division of labor\u003c/b\u003e is one where AI handles the text-based or procedural elements while humans focus on the physical, empathic, and judgment-based components of work. Such a synergy was noted in our results as a \u0026ldquo;synergistic effect,\u0026rdquo; particularly in fields like marketing, strategic planning, or innovation, where human creativity coupled with AI-driven analytical support can enhance overall outcomes.\u003c/p\u003e\u003cp\u003eAnother key interpretation is the \u003cb\u003evalidation of GPT-4o as a predictive tool for workforce analytics\u003c/b\u003e. One of our research questions (RQ2) probed whether a large language model could reliably estimate task augmentation potential. The findings are encouraging: GPT-4o task exposure estimates correlated with independent benchmarks (e.g., wage levels, known patterns from prior studies) and yielded plausible sector-wise projections. This suggests that advanced generative models can serve not just as productivity aids, but also as \u003cb\u003eanalytical instruments to forecast technological impacts\u003c/b\u003e. In our case, using the model to analyze real job postings provided a data-driven way to quantify AI exposure at scale, complementing or even accelerating traditional expert surveys. This approach is a novel contribution of our work, demonstrating how AI can help map out its own impact on labor markets in a more dynamic fashion. It is worth noting, however, that while the model\u0026rsquo;s estimates were broadly consistent with external data and \u003cb\u003econclusions on task exposure in\u003c/b\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], they should be interpreted as indicative rather than definitive. Generative AI can sometimes overestimate its capabilities or overlook tacit job requirements, so human validation remains important. Nonetheless, the correspondence we observed bolsters confidence in leveraging models like GPT-4o for preliminary assessments of automation potential in rapidly evolving job landscapes.\u003c/p\u003e\u003cp\u003eThe fact that \u003cb\u003ehigher-wage occupations showed greater AI augmentation potential\u003c/b\u003e is an intriguing outcome with labor economics implications. Historically, automation in earlier industrial eras often threatened lower-skill, routine jobs; by contrast, generative AI appears to target many higher-skill professions (e.g., lawyers, analysts, developers) because those jobs involve abundant information work that AI can optimize. Our data confirmed that industries with higher average salaries tend to have a larger share of tasks that are automatable by GPT-4o. This might initially raise concern about possible disruption of well-paid professional roles. However, our interpretation, consistent with the concept of augmentation, is that \u003cb\u003ethese roles are more likely to be transformed than eliminated\u003c/b\u003e. Professionals in high-exposure fields stand to become more productive by offloading routine aspects of their job to AI, potentially \u003cb\u003eincreasing the value of their creative and supervisory skills\u003c/b\u003e. In fact, evidence from related studies indicates that generative AI can help level the playing field within such occupations: for example, an experiment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] found that when office workers used ChatGPT for writing tasks, even those with lower prior skills saw substantial quality improvements, narrowing performance gaps. This hints that AI augmentation, if accessible, could reduce certain skill disparities \u003cb\u003ewithin\u003c/b\u003e high-skill jobs by allowing a broader range of workers to achieve high-quality outputs. From a theoretical view, this resonates with the HAIC framework \u0026ndash; rather than a zero-sum replacement, we are observing a \u003cem\u003ecomplementary enhancement\u003c/em\u003e where human strengths and AI strengths together yield better productivity and quality than either could alone. It also underscores the importance of human capital: those workers and organizations that effectively \u003cb\u003eadapt and incorporate AI\u003c/b\u003e are likely to reap disproportionate benefits (higher output, new innovations), which could widen gaps between firms or individuals if others lag in adoption. This dynamic invites careful consideration of how to ensure broad-based gains from AI, a topic we address below.\u003c/p\u003e\u003cp\u003eFinally, the macro-level findings provide a \u003cb\u003estrategic interpretation for economic planning\u003c/b\u003e. The projection of trillions of rubles in potential efficiency gains highlights that generative AI could become a significant driver of productivity growth in the coming decade. However, these gains will not materialize automatically; they depend on the cumulative choices of enterprises and workers across many sectors. The fact that the largest gains accrue in sectors like retail and education (which are not traditionally seen as tech-heavy) is insightful \u0026ndash; it suggests that even moderate technological improvements, when applied to very large labor pools, can yield huge aggregate benefits. Therefore, a broad-based diffusion of AI tools (even for relatively simple augmentations in day-to-day tasks like documentation, reporting, or scheduling) could have outsized economic effects. On the other hand, the lower gains projected for specialized domains (e.g., scientific R\u0026amp;D or finance) imply that in those fields, either the technology has less of a foothold or the work is already highly optimized. It may also reflect that in expert domains, AI is currently used more for quality enhancement than for labor saving. In all cases, our interpretation aligns with the view that \u003cb\u003ehuman\u0026ndash;AI collaboration is key to unlocking these macro benefits\u003c/b\u003e \u0026ndash; the emphasis is on \u003cb\u003eproductivity enhancement and task reallocation, not straightforward job replacement\u003c/b\u003e. The emergence of new professional pathways and the reallocation of human effort from mundane to higher-level tasks could, if managed well, lead to positive-sum outcomes (e.g., improved services, new industries, and growth in demand for AI-savvy talent). This paints a picture of the future workforce that is augmented by AI: many jobs will evolve to incorporate AI oversight or co-working, new roles (such as AI workflow coordinators, prompt engineers, or AI ethicists) will become commonplace, and overall economic productivity may surge during the \u0026ldquo;GenAI era\u0026rdquo; as these technologies diffuse.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Implications for Policy and Practice\u003c/h2\u003e\u003cp\u003eThe uneven yet significant impact of generative AI on work tasks carries important \u003cb\u003eimplications for business leaders, workers, and policymakers\u003c/b\u003e. At the forefront, organizations should approach AI integration strategically and humanely. Rather than indiscriminately automating tasks, employers are advised to adopt a \u003cb\u003e\u0026ldquo;people-first\u0026rdquo; augmentation strategy\u003c/b\u003e \u0026ndash; identifying which tasks can be reliably handed off to AI and retraining employees to focus on the complementary aspects of their roles. This aligns with industry guidance such as Accenture call for a \u003cem\u003ePeople-First Approach\u003c/em\u003e that prioritizes reskilling and workforce transformation as companies embrace generative AI [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Our findings highlight which occupations and task types should be prioritized for such interventions. For example, roles in analytics, marketing, and other high-exposure areas could be early targets for deploying GPT-based tools to handle routine data processing or content generation. By doing so, organizations can \u003cb\u003eboost efficiency\u003c/b\u003e in these functions while simultaneously freeing employees to concentrate on creative strategy, complex decision-making, and interpersonal responsibilities that AI cannot fulfill. However, realizing these gains requires substantial investment in \u003cb\u003etraining and change management\u003c/b\u003e. Employers should invest in upskilling programs that make staff proficient in using AI tools (AI literacy), and cultivate an organizational culture that views AI as a collaborative partner rather than a threat. Notably, resistance or \u0026ldquo;AI anxiety\u0026rdquo; among employees is a real obstacle; to overcome it, leaders should emphasize success stories, involve employees in AI adoption plans, and ensure transparency about how the technology works and what data it uses. When workers understand AI\u0026rsquo;s limitations and strengths, and see it as augmenting their work rather than spying on or replacing them, they are more likely to embrace it, leading to better outcomes.\u003c/p\u003e\u003cp\u003ePolicymakers and regulators likewise have a crucial role to play in guiding the GenAI-driven transition in labor markets. \u003cb\u003eEducation and vocational training policies\u003c/b\u003e must be updated to reflect the changing skill demands: curricula should integrate data literacy, prompt engineering, and human\u0026ndash;AI collaboration skills, preparing new entrants for AI-enhanced workplaces. Governments could partner with industry to create reskilling initiatives for mid-career workers in at-risk occupations, ensuring that those whose tasks are highly automatable are given pathways to move into more secure roles. The evidence that high-wage, high-skill jobs are also heavily exposed to AI means that continuous learning is imperative even for well-educated professionals; thus, policy support for lifelong learning and professional development in AI-related competencies will be beneficial across the board. Furthermore, \u003cb\u003elabor regulations and social safety nets\u003c/b\u003e may need updating. As tasks shift, job descriptions and classifications might need revision to accurately capture new AI-in-the-loop responsibilities. Policymakers should also monitor for any emergent inequalities: for instance, if certain groups or regions adopt AI more slowly, targeted support or incentives might be required to prevent widening productivity gaps. On the flip side, if AI drastically increases output in certain sectors, there may be a case for sharing the gains (through higher wages or reduced working hours) to ensure workers benefit from the productivity dividend. \u003cb\u003eResponsible AI governance\u003c/b\u003e is another policy implication \u0026mdash; as organizations deploy GPT-4o-like systems, concerns around data privacy, algorithmic bias, and accountability for AI-generated errors will grow. Regulators should establish clear guidelines that encourage innovation while protecting workers\u0026rsquo; rights and societal values. This includes enforcing transparency in AI decision-making (especially in high-stakes domains), mandating human oversight for critical decisions, and perhaps defining ethical standards for human\u0026ndash;AI workplace interactions.\u003c/p\u003e\u003cp\u003eFrom a broader perspective, our study supports the view that maximizing the benefits of generative AI requires focusing on \u003cb\u003eaugmentation over replacement\u003c/b\u003e. This principle should be enshrined in both company practice and policy frameworks. International labor studies (such as [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]) echo this emphasis: a majority of employers plan to focus on adapting their workforce through reskilling (77% of surveyed companies) and stress that AI should be used to \u003cb\u003ecomplement human workers rather than replace them\u003c/b\u003e, accompanied by appropriate supportive measures and regulations. In practical terms, companies should establish internal guidelines for human-AI collaboration, delineating which decisions or tasks must remain under human control and how AI outputs are to be verified. They should also invest in what Accenture termed a \u003cem\u003esustainable tech foundation\u003c/em\u003e and \u003cem\u003eecosystem innovation\u003c/em\u003e: upgrading IT infrastructure to safely deploy AI at scale and collaborating with other organizations (e.g., through industry consortia or public-private partnerships) to share best practices and resources for AI adoption. By fostering an innovation ecosystem, even sectors or firms that lag in AI expertise (such as traditional industries) can catch up through knowledge transfer and shared platforms. Finally, \u003cb\u003eethical practices\u003c/b\u003e must guide this transition. Responsible AI use \u0026mdash; encompassing fairness, accountability, transparency, and security \u0026mdash; is not just a slogan but a necessity for long-term trust and efficacy. For instance, if generative AI is used to screen job candidates or evaluate performance (a possible extension of our work on task analysis), checks should be in place to prevent bias or unjust outcomes. Similarly, when AI aids in content creation, organizations should set standards to avoid the spread of misinformation or to clearly attribute AI-generated material. In summary, the implication is that \u003cb\u003ehuman-centric policies and practices\u003c/b\u003e will determine whether the productivity boosts quantified in our study translate into sustainable economic and social benefits. With proactive reskilling programs, supportive governance, and a commitment to using AI as a tool for empowerment, the workforce can not only weather the AI revolution but thrive alongside it.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Limitations\u003c/h2\u003e\u003cp\u003eWhile this research provides valuable insights, several limitations must be acknowledged:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eScope of Data (Russia-specific)\u003c/b\u003e: Our analysis was confined to the Russian labor market and relied on job vacancy data from a single platform (HH.ru). Labor dynamics, task compositions, and AI adoption rates can differ in other countries or even within non-digital segments of the Russian economy. Thus, caution is needed in generalizing the quantitative results beyond the studied context. Future studies should expand to diverse data sources and geographic contexts to verify if similar patterns hold.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eUse of GPT-4o for Task Evaluation\u003c/b\u003e: We utilized the GPT-4o model to estimate task automation potential, which introduces uncertainties inherent to the AI\u0026rsquo;s judgments. While we found GPT-4o\u0026rsquo;s assessments credible and consistent with external benchmarks, they are ultimately \u003cem\u003emodel-generated estimates\u003c/em\u003e. The model might misinterpret certain task descriptions or lack up-to-date knowledge of niche job requirements, potentially leading to biased or inaccurate automation scores. There was no direct human validation of each task rating in this study. This limitation suggests our findings are best viewed as indicative estimates of automation potential, not precise predictions of actual outcomes in every workplace.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTask Definition and Thresholding\u003c/b\u003e: The way tasks were parsed from job postings and the threshold for \u0026ldquo;automatable\u0026rdquo; tasks could affect results. Job listings may not enumerate all tasks comprehensively, and they often focus on current needs, possibly underrepresenting infrequent but important duties. Moreover, we applied a certain cutoff (automation probability) to decide if a task is counted as automatable. Different threshold choices or weighting schemes might change the measured percentages. We partially addressed this by introducing a \u0026ldquo;task weight\u0026rdquo; metric (tasks \u0026times; vacancies) to highlight economically significant tasks, but the approach still simplifies the complex spectrum of task automation feasibility into binary or average metrics. In reality, many tasks lie in a gray area of partial automation, which our summary metrics might not fully capture.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTemporal and Technological Evolution\u003c/b\u003e: The study provides a snapshot based on GPT-4 generation technology as of now. AI capabilities are rapidly advancing; future models might overcome some limitations (for instance, better handling of multimodal inputs or physical reasoning) that currently constrain automation potential. Conversely, the regulatory environment and public sentiment towards AI could shift, affecting adoption. Our projection up to 2030 assumes a certain trajectory of technology improvement and uptake, but unforeseen breakthroughs or setbacks could alter that path. Thus, the long-term macroeconomic estimates carry uncertainty \u0026ndash; they represent a \u003cb\u003emaximum potential\u003c/b\u003e if AI is adopted extensively, rather than a guaranteed outcome.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFocus on LLMs (Generative AI) Only\u003c/b\u003e: We specifically examined generative text-based AI. However, workplace automation can also come from other AI domains (computer vision, robotics, expert systems) and from broader process innovations. Some occupations currently showing low LLM augmentation potential (e.g., drivers or trades) might eventually be significantly impacted by non-LLM AI such as autonomous vehicles or robotic automation. Our study does not encompass these technologies. Likewise, even in high-LLM occupations, there are non-textual tasks (like creating visual designs, or performing hands-on experiments) that GPT-4o cannot assist with. The \u003cb\u003etotal automation potential of an occupation\u003c/b\u003e might be underestimated if other AI tools are considered, or overestimated if we assume GPT-4o alone can tackle everything. We focused on the generative text aspect, so our conclusions should be integrated with analyses of other AI tools for a complete picture.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eNo Direct Measurement of Productivity Gains\u003c/b\u003e: We inferred potential productivity improvements from task automation rates and prior research, but we did not measure actual productivity changes in workplaces using GPT-4o. Factors like the learning curve in adopting AI, the quality of AI outputs, and human oversight required could reduce the realized productivity vs. the theoretical maximum. Additionally, productivity gains do not automatically translate to economic gains if, for example, organizational or market constraints prevent output from increasing. Our macroeconomic benefit calculations assume efficiency translates proportionally into cost savings or output \u0026ndash; an assumption that may not hold in every scenario (e.g., if demand for the product/service is fixed). Empirical studies tracking companies that implement GPT assistance would complement our approach by revealing how much of the estimated potential is captured in practice.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn summary, these limitations suggest that our findings should be viewed as \u003cb\u003eexploratory and illustrative of broad trends\u003c/b\u003e rather than precise forecasts. They open several avenues for refinement, as discussed next, and highlight the need for ongoing research and validation as generative AI technologies and their workplace applications continue to evolve.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Future Research Directions\u003c/h2\u003e\u003cp\u003eBuilding on this study insights and limitations, we identify several directions for future research:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eGranular Task Analysis\u003c/b\u003e: Future work should examine \u003cb\u003ewhich specific task attributes\u003c/b\u003e (e.g. complexity, standardization, creativity level) most influence automatability. Our results hinted at task complexity and workflow standardization as factors (simple, standardized tasks were easier to automate). Rigorous analysis could involve classifying tasks by complexity or interaction level and seeing how AI performance varies, helping to fine-tune the selection of tasks for automation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLongitudinal and Experimental Studies\u003c/b\u003e: To gauge the real-world impact of GPT-4 and similar AI on productivity, longitudinal case studies or controlled experiments within organizations are needed. Researchers could deploy generative AI tools in certain teams and measure productivity, quality, job satisfaction, and skill change over time compared to control groups. Such studies would validate (or adjust) the projected gains and identify any unintended consequences (e.g., over-reliance on AI, changes in collaboration patterns).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMulti-Modal and Integrated AI Systems\u003c/b\u003e: Since many jobs involve non-textual tasks, future research should extend analysis to \u003cb\u003emulti-modal AI\u003c/b\u003e (combining language, vision, and robotics). For instance, investigating how language models can cooperate with robotic process automation or computer vision systems would provide a fuller estimate of automation potential in occupations that require both cognitive and physical activities. This includes exploring solutions for professions with low LLM suitability by integrating other AI technologies​, thereby moving towards a more holistic human-AI workflow integration.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCross-Country and Cross-Industry Comparisons\u003c/b\u003e: It would be valuable to replicate this study\u0026rsquo;s methodology in different labor markets (e.g., countries with varying income levels or different industry structures) and across more industries. Such comparisons could reveal how cultural, economic, or regulatory differences mediate AI\u0026rsquo;s impact on work. They might also validate whether the correlation between wages and AI exposure holds universally or is context-dependent. Additionally, sectors like healthcare, law, or public services warrant focused studies, as they have unique professional norms and data sensitivity issues that affect AI adoption.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHuman Capital and Inequality Impacts\u003c/b\u003e: Further research is needed on the \u003cb\u003elabor economics aspects\u003c/b\u003e \u0026ndash; particularly how AI augmentation affects employment levels, wage distributions, and skill demands over time. Will AI augmentation lead to higher wages for those who master it and stagnation for those who don\u0026rsquo;t, thereby widening income inequality? Or will it democratize expertise and reduce skill premiums (since AI can assist less-skilled workers)? Economic modeling combined with empirical labor data as AI diffusion progresses can help answer these questions. This includes examining secondary effects, such as job creation in AI oversight and maintenance, and the redeployment of labor into new tasks that AI cannot do (yet).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePolicy and Ethical Frameworks\u003c/b\u003e: Interdisciplinary research involving law, ethics, and public policy is crucial to accompany technical and economic analysis. Studies could propose and evaluate \u003cb\u003eframeworks for responsible AI integration\u003c/b\u003e in workplaces, drawing on real-world pilots. For example, what is the effect of implementing an \u0026ldquo;AI ethics audit\u0026rdquo; in a company or giving employees a formal role in governance of AI tools? What kinds of regulatory incentives or standards most effectively encourage firms to invest in worker retraining alongside AI investments? Such research would inform guidelines to ensure that AI\u0026rsquo;s productivity gains are achieved in a fair, transparent, and socially beneficial manner​.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEvolution of Human-AI Collaboration Paradigms\u003c/b\u003e: Finally, research should continue to explore how \u003cb\u003ehuman-AI collaboration\u003c/b\u003e can be optimized. As AI systems become more capable, the design of workflows will need to dynamically allocate tasks between humans and AI agents. Future studies might draw on organizational psychology and design thinking to develop new collaboration models, prototyping how teams that include AI \u0026ldquo;colleagues\u0026rdquo; operate. This line of inquiry is aligned with our broader research agenda and could yield best practices on managing \u0026ldquo;hybrid\u0026rdquo; teams, mitigating AI-related stress, and maximizing the \u003cem\u003esynergistic effect\u003c/em\u003e noted in our conclusion.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBy pursuing these future directions, scholars and practitioners can deepen understanding of generative AI\u0026rsquo;s evolving role in the workplace. The goal is to continually refine both the \u003cem\u003epredictive analytics\u003c/em\u003e (how the anticipated impact of AI is measured) and the \u003cem\u003eprescriptive guidance\u003c/em\u003e (the way to positively shape such an impact). This opens a possibility to better ensure that the GenAI era leads to augmented human capabilities, sustainable productivity growth, and broadly shared prosperity, rather than unintended disruption.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMaksim Elisov contributed to the research design, created the new software used in the work, performed data collection and analysis, prepared figures and tables, reviewed literature, drafted and revised the report.Kirill Pshinnik conceived the study idea, guided the research design, data collection and analysis. Aleksandra Bordunos contributed to the research design, data analysis, literature review, drafted and revised the report.Oksana Zhirosh contributed to the research design, data analysis, literature review, drafted and revised the report.All the authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS. Samoili, M. Lopez Cobo, B. Delipetrev, F. Martinez-Plumed, E. Gomez Gutierrez, and G. De Prato, \u003cem\u003eAI Watch. \u003c/em\u003e\u003cem\u003eDefining Artificial Intelligence 2.0\u003c/em\u003e, EUR 30873 EN, Luxembourg: Publications Office of the European Union, 2021. ISBN 978-92-76-42648-6. doi:10.2760/019901.\u003c/li\u003e\n\u003cli\u003eY. Jiang, X. Li, H. Luo, S. Yin, and O. Kaynak, \u0026ldquo;Quo vadis artificial intelligence?,\u0026rdquo; \u003cem\u003eDiscover Artif. \u003c/em\u003e\u003cem\u003eIntell.\u003c/em\u003e, vol. 2, art. 4, Mar. 2022, doi: 10.1007/s44163-022-00022-8.\u003c/li\u003e\n\u003cli\u003eS. Hajkowicz, C. Sanderson, S. Karimi, A. Bratanova, and C. Naughtin, (2023). Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021. \u003cem\u003eTechnology in Society\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e, doi.org/10.1016/j.techsoc.2023.102260\u003c/li\u003e\n\u003cli\u003eKPMG, \u0026ldquo;Trust, attitudes, and use of artificial intelligence,\u0026rdquo; 2025. [Online]. Available: https://kpmg.com/us/en/articles/2025/trust-attitudes-and-use-of-artificial-intelligence.html\u003c/li\u003e\n\u003cli\u003eYu. Ya. Dranev, I. I. Kuchin, and M. I. Miryakov, \u0026ldquo;Economic effect of the implementation of artificial intelligence technologies in Russia,\u0026rdquo; Institute for Statistical Studies and Economics of Knowledge, NRU Higher School of Economics, Moscow, 2025. [Online]. Available: https://issek.hse.ru/news/1022068478.html\u003c/li\u003e\n\u003cli\u003eAccenture, \u003cem\u003eA New Era of Generative AI for Everyone\u003c/em\u003e, Accenture, Mar. 2023. [Online]. Available: https://www.accenture.com/content/dam/accenture/final/accenture-com/document/Accenture-A-New-Era-of-Generative-AI-for-Everyone.pdf.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e \u003c/em\u003eWorld Economic Forum, Future of Jobs Report 2025, Insight Report, Jan. 2025. Accessed: Apr. 26th, 2025, [Online]. Available: https://www.weforum.org/publications/the-future-of-jobs-report-2025/ \u003c/li\u003e\n\u003cli\u003eT. T. Eloundou, S. Manning, P. Mishkin, and D. Rock, \u0026ldquo;GPTs are GPTs: An early look at the labor market impact potential of large language models,\u0026rdquo; \u003cem\u003eWorking Paper\u003c/em\u003e, OpenAI, OpenResearch, Univ. of Pennsylvania, Aug. 2023. [Online]. Available:https://arxiv.org/abs/2303.10130 \u003c/li\u003e\n\u003cli\u003eQ. Chen, J. Ge, H. Xie, X. Xu, and Y. Yang, \u0026ldquo;Large language models at work in China\u0026rsquo;s labor market,\u0026rdquo; \u003cem\u003eChina Econ. \u003c/em\u003e\u003cem\u003eRev.\u003c/em\u003e, vol. 40, art. 102413, Apr. 2025, doi: 10.1016/j.chieco.2025.102413.\u003c/li\u003e\n\u003cli\u003eO. A. Herrera, L. Ripani, and E. T. Ramirez, \u0026quot;AI and the Increase of Productivity and Labor Inequality in Latin America: Potential Impact of Large Language Models on Latin American Workforce,\u0026quot; 2024. [Online]. Available: https://publications.iadb.org/en/ai-and-increase-productivity-and-labor-inequality-latin-america-potential-impact-large-language \u003c/li\u003e\n\u003cli\u003eM. Oschinski, \u0026ldquo;Assessing the Impact of Artificial Intelligence on Germany\u0026apos;s Labor Market: Insights from a ChatGPT Analysis,\u003cem\u003e\u0026rdquo;\u003c/em\u003e 2023. [Online]. Available: https://mpra.ub.uni-muenchen.de/118300/ \u003c/li\u003e\n\u003cli\u003eR. Chaves et al., \u0026ldquo;Charting a course at the human\u0026ndash;AI frontier: A paradigm matrix informed by social sciences and humanities,\u0026rdquo; \u003cem\u003eAI Soc.\u003c/em\u003e, vol. 40, no. 2, pp. 123\u0026ndash;134, Mar. 2025, doi:10.1007/s00146-025-02295-w.\u003c/li\u003e\n\u003cli\u003eM. Raees, I. Meijerink, I. Lykourentzou, V. J. Khan, and K. Papangelis, \u0026quot;From explainable to interactive AI: A literature review on current trends in human-AI interaction,\u0026quot; \u003cem\u003eInt. J. Hum.-Comput. Stud.\u003c/em\u003e, Art. no. 103301, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1071581924000855 \u003c/li\u003e\n\u003cli\u003eH. Cui and T. Yasseri, \u0026quot;AI-enhanced collective intelligence,\u0026quot; Patterns, vol. 5, no. 11, 2024. [Online] Available: https://www.cell.com/patterns/fulltext/S2666-3899(24)00233-2 \u003c/li\u003e\n\u003cli\u003eJ. Li, J. Huang, J. Liu, and T. Zheng, \u0026ldquo;Human-AI cooperation: Modes and their effects on attitudes,\u0026rdquo; \u003cem\u003eTelemat. Inform.\u003c/em\u003e, vol. 73, Art. no. 101862, 2022. [Online] Available: https://www.sciencedirect.com/science/article/pii/S0736585322000958\u003c/li\u003e\n\u003cli\u003eV. Kolbj\u0026oslash;rnsrud, \u0026ldquo;Designing the Intelligent Organization: Six Principles for Human-AI Collaboration,\u0026rdquo; \u003cem\u003eCalifornia Management Review\u003c/em\u003e, vol. 66, no. 2, pp. 44\u0026ndash;64, Nov. 2023, doi: 10.1177/00081256231211020. \u003c/li\u003e\n\u003cli\u003eD. Koutrintzes, C. Spatharis, and M. Dagioglou, \u0026ldquo;Human-Aware Design for Transferring Knowledge During Human-AI Co-Learning,\u0026rdquo; in \u003cem\u003eProceedings of the Workshop on Embracing Human-Aware AI in Industry 5.0 (HAII5.0 2024)\u003c/em\u003e, CEUR Workshop Proceedings, vol. 3765, pp. 67\u0026ndash;73. [Online]. Available: https://ceur-ws.org/Vol-3765/Camera_Ready_Paper-05.pdf\u003c/li\u003e\n\u003cli\u003eS. Noy and W. Zhang, \u0026ldquo;Experimental evidence on the productivity effects of generative artificial intelligence,\u0026rdquo; \u003cem\u003eScience\u003c/em\u003e, vol. 381, pp. 187\u0026ndash;192, 2023. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.adh2586 \u003c/li\u003e\n\u003cli\u003eS. Peng, E. Kalliamvakou, P. Cihon, and M. Demirer, \u0026quot;The impact of AI on developer productivity: Evidence from GitHub Copilot,\u0026quot; arXiv preprint arXiv:2302.06590, 2023. [Online]. Available: https://arxiv.org/abs/2302.06590 \u003c/li\u003e\n\u003cli\u003eJ. Brand, A. Israeli, and D. Ngwe, \u0026quot;Using LLMs for Market Research,\u0026quot; Working Paper 23-062, Jul. 29, 2024. [Online]. Available: https://www.hbs.edu/ris/Publication%20Files/23-062_ed720ebc-ec4d-4bc3-a6ba-bad8cfbd9d51.pdf \u003c/li\u003e\n\u003cli\u003eF. Dell\u0026rsquo;Acqua et al, \u0026quot;Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality,\u0026quot; Working Paper 24-013, Sep. 2023. [Online]. Available: https://www.hbs.edu/ris/Publication%20Files/24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf \u003c/li\u003e\n\u003cli\u003eJ. C. Kaufman and R. A. Beghetto, \u0026ldquo;Beyond Big and Little: The Four C Model of Creativity,\u0026rdquo; \u003cem\u003eReview of General Psychology\u003c/em\u003e, vol. 13, no. 1, pp. 1\u0026ndash;12, Mar. 2009, doi: 10.1037/a0013688. [Online]. Available: https://psycnet.apa.org/record/2018-70050-001 \u003c/li\u003e\n\u003cli\u003eN. Jia, X. Luo, Z. Fang, and C. Liao, \u0026ldquo;When and How Artificial Intelligence Augments Employee Creativity,\u0026rdquo; \u003cem\u003eAcademy of Management Journal\u003c/em\u003e, vol. 67, no. 1, pp. 5\u0026ndash;32, Feb. 2024, doi: 10.5465/amj.2022.0426. \u003c/li\u003e\n\u003cli\u003eF. Dell\u0026rsquo;Acqua et al., \u0026ldquo;The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise,\u0026rdquo; National Bureau of Economic Research, Working Paper No. 33641, Apr. 2025. doi: 10.3386/w33641. \u003c/li\u003e\n\u003cli\u003eJ. Zheng, Y. Hong, and A. Richter, \u0026ldquo;Artificial Trailblazing: How Human-AI Collaboration Transforms Organizational Innovation Practices,\u0026rdquo; in \u003cem\u003eProc. 58th Hawaii Int. \u003c/em\u003e\u003cem\u003eConf. on System Sciences (HICSS 2025)\u003c/em\u003e, Jan. 2025, pp. 307\u0026ndash;316, doi: 10.24251/HICSS.2025.038.\u003c/li\u003e\n\u003cli\u003eG. Secundo, C. Spilotro, J. Gast, and V. Corvello, \u0026ldquo;The transformative power of artificial intelligence within innovation ecosystems: A review and a conceptual framework,\u0026rdquo; \u003cem\u003eRev. Manag. \u003c/em\u003e\u003cem\u003eSci.\u003c/em\u003e, 2024, doi: 10.1007/s11846-024-00828-z. \u003c/li\u003e\n\u003cli\u003eF. Gama and S. Magistretti, \u0026ldquo;Artificial Intelligence in Innovation Management: A Review of Innovation Capabilities and a Taxonomy of AI Applications,\u0026rdquo; \u003cem\u003eJ. of Product Innov. \u003c/em\u003e\u003cem\u003eManage.\u003c/em\u003e, vol. 42, no. 1, pp. 76\u0026ndash;111, Jan. 2025, doi: 10.1111/jpim.12698. \u003c/li\u003e\n\u003cli\u003eY. Bengio, \u0026ldquo;The Consciousness Prior,\u0026rdquo; \u003cem\u003earXiv preprint arXiv:1709.08568\u003c/em\u003e, Sep. 2017. [Online]. Available: https://arxiv.org/abs/1709.08568\u003c/li\u003e\n\u003cli\u003eS. Yao et al., \u0026ldquo;Tree of Thoughts: Deliberate Problem Solving with Large Language Models,\u0026rdquo; in \u003cem\u003eAdvances in Neural Inf. \u003c/em\u003e\u003cem\u003eProcess. Syst.\u003c/em\u003e 36 (NeurIPS 2023), 2023. [Online]. Available: https://arxiv.org/abs/2305.1060 \u003c/li\u003e\n\u003cli\u003eK. C. Hong, A. S. Shibghatullah, T. C. Ling, and S. Sarsam, \u0026ldquo;Generative AI-powered predictive analytics model: Leveraging synthetic datasets to determine ERP adoption success through critical success factors,\u0026rdquo; \u003cem\u003eInt. J. Adv. \u003c/em\u003e\u003cem\u003eComput. Sci. Appl.\u003c/em\u003e, vol. 15, no. 5, pp. 469\u0026ndash;482, 2024.. [Online]. \u003c/li\u003e\n\u003cli\u003eM. Mateev, \u0026ldquo;Predictive analytics based on digital twins, generative AI, and ChatGPT,\u0026rdquo; in \u003cem\u003eProc. 27th World Multi-Conf. Syst., Cybern. Inform. (WMSCI)\u003c/em\u003e, 2023. [Online]. Available: https://www.iiis.org/DOI2023/SA437PH/ \u003c/li\u003e\n\u003cli\u003eS. Joshi, \u0026quot;The Synergy of Generative AI and Big Data for Financial Risk: Review of Recent Developments,\u0026quot; \u003cem\u003eInt. J. Multidiscip. Res. (IJFMR)\u003c/em\u003e, vol. 7, no. 1, Jan.-Feb. 2025. [Online]. Available: https://www.researchgate.net/publication/388398425_The_Synergy_of_Generative_AI_and_Big_Data_for_Financial_Risk_Review_of_Recent_Developments \u003c/li\u003e\n\u003cli\u003eFederal State Statistics Service (Rosstat), \u0026quot;Russian Statistical Yearbook 2024,\u0026quot; [Online]. Available: Russian Statistical Yearbook.\u003c/li\u003e\n\u003cli\u003eSberIndex, \u0026ldquo;Median Monthly Wages Dashboard,\u0026rdquo; Sberbank, Moscow, Russia, Feb. 2025. [Online]. Available: https://sberindex.ru/en/dashboards/median-wages \u003c/li\u003e\n\u003cli\u003eF. D. Davis, R. P. Bagozzi, and P. R. Warshaw, \u0026ldquo;User acceptance of computer technology: A comparison of two theoretical models,\u0026rdquo; \u003cem\u003eManagement Science\u003c/em\u003e, vol. 35, no. 8, pp. 982\u0026ndash;1003, Aug. 1989, doi: 10.1287/mnsc.35.8.982.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"generative AI, GenAI, labor market, tasks automation, augmentation, Human-AI Collaboration","lastPublishedDoi":"10.21203/rs.3.rs-7173895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7173895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrawing on the concept of Human-AI Collaboration (HAIC), this research analyzes the exposure of occupations to generative AI-driven automation in the labor market of Russia using real vacancies data. The study addresses key research questions on the types of tasks and occupations amenable to GenAI augmentation using GPT-4o for prediction of task automation potential and occupational variability with regard to augmentation prospects. Our findings contribute to the body of research revealing significant disparities in AI potential adoption across tasks and occupations. We detected no tasks or occupations prone to 100% automation; the highest automation potential of a task is 85% and that of occupation augmentation \u0026minus;\u0026thinsp;70%. Our major findings are threefold. First, occupations in culture, sport, leisure and entertainment, activities in the operation of real estate as well as information and communication showed the highest augmentation potential, i.e., 63%, 58.8%, and 49.6%, respectively. Second, the potential for augmentation is positively associated with the level of wages. Third, potential financial impact by 2030 is predicted to reach 10.8 trillion rubles. The findings underscore the urgency of reskilling initiatives and ethical frameworks to mitigate inequality. By bridging theoretical and practical insights, this research informs organizational strategies for responsible AI integration and highlights pathways to maximize human-AI synergy in the evolving workplace.\u003c/p\u003e","manuscriptTitle":"Generative AI and Potential for Augmentation: A Data-Driven Analysis of Labor Market in Russia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 04:43:58","doi":"10.21203/rs.3.rs-7173895/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92460210-f0ad-412f-81c1-b290d6b25f2d","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55587750,"name":"Business and commerce/Information systems and information technology"},{"id":55587751,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-01T06:38:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 04:43:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7173895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7173895","identity":"rs-7173895","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}