TY - UNPB

T1 - Generative AI and Potential for Augmentation: A Data-Driven Analysis of Labor Market in Russia

AU - Елисов, Максим

AU - Пшинник, Кирилл

AU - Бордунос, Александра Константиновна

AU - Жирош, Оксана

PY - 2025/10/30

Y1 - 2025/10/30

N2 - Drawing on the concept of Human-AI Collaboration (HAIC), this research analyzes the exposure of occupations to generative AI-driven automation in thelabor market of Russia using real vacancies data. The study addresses key research questions on the types of tasks and occupations amenable to GenAIaugmentation using GPT-4o for prediction of task automation potential and occupational variability with regard to augmentation prospects. Our findingscontribute to the body of research revealing significant disparities in AI potential adoption across tasks and occupations. We detected no tasks oroccupations prone to 100% automation; the highest automation potential of a task is 85% and that of occupation augmentation − 70%. Our major findings arethreefold. First, occupations in culture, sport, leisure and entertainment, activities in the operation of real estate as well as information and communicationshowed the highest augmentation potential, i.e., 63%, 58.8%, and 49.6%, respectively. Second, the potential for augmentation is positively associated with thelevel of wages. Third, potential financial impact by 2030 is predicted to reach 10.8 trillion rubles. The findings underscore the urgency of reskilling initiativesand ethical frameworks to mitigate inequality. By bridging theoretical and practical insights, this research informs organizational strategies for responsible AIintegration and highlights pathways to maximize human-AI synergy in the evolving workplace.

AB - Drawing on the concept of Human-AI Collaboration (HAIC), this research analyzes the exposure of occupations to generative AI-driven automation in thelabor market of Russia using real vacancies data. The study addresses key research questions on the types of tasks and occupations amenable to GenAIaugmentation using GPT-4o for prediction of task automation potential and occupational variability with regard to augmentation prospects. Our findingscontribute to the body of research revealing significant disparities in AI potential adoption across tasks and occupations. We detected no tasks oroccupations prone to 100% automation; the highest automation potential of a task is 85% and that of occupation augmentation − 70%. Our major findings arethreefold. First, occupations in culture, sport, leisure and entertainment, activities in the operation of real estate as well as information and communicationshowed the highest augmentation potential, i.e., 63%, 58.8%, and 49.6%, respectively. Second, the potential for augmentation is positively associated with thelevel of wages. Third, potential financial impact by 2030 is predicted to reach 10.8 trillion rubles. The findings underscore the urgency of reskilling initiativesand ethical frameworks to mitigate inequality. By bridging theoretical and practical insights, this research informs organizational strategies for responsible AIintegration and highlights pathways to maximize human-AI synergy in the evolving workplace.

U2 - 10.21203/rs.3.rs-7173895/v1

DO - 10.21203/rs.3.rs-7173895/v1

M3 - Препринт

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

ER -