THE IMPACT OF AUTOMATION AND ARTIFICIAL INTELLIGENCE ON EMPLOYMENT, WAGES, AND PRODUCTIVITY

Abstract

The modern labor market is undergoing significant transformations under the influence of automation and artificial intelligence (AI) technologies. These changes impact employment structure, wage levels, and overall labor productivity.While automation creates new opportunities for increasing production efficiency, it also poses challenges related to job displacement. One of the key issues is balancing technological progress with socio-economic stability, which requires comprehensive analysis and effective adaptation mechanisms.This study proposes a mathematical model that describes the interrelationship between automation levels, workforce retraining opportunities, government policies, and education levels. The proposed system of equations is presented in a matrix form, enabling the analysis of employment dynamics in response to technological shifts. The model allows for assessing the impact of automation on the labor market under different levels of government support and investment in educational initiatives.To evaluate the effects of technological innovations, three key scenarios are considered: Low automation and high reskilling levels – this scenario helps maintain stable employment and mitigate the negative impact of automation. It demonstrates that the active implementation of retraining programs facilitates an effective transition of workers to new job roles. High automation and low reskilling levels – this leads to a significant decline in employment levels and a decrease in average wages. It may cause increased social tensions and economic instability due to rising unemployment.Effective government policy and educational support – this scenario reduces the adverse effects of automation by expanding workforce adaptation programs, thereby stabilizing the labor market.Numerical modeling results indicate that retraining, government support, and investments in educational initiatives are the key factors enabling labor market adaptation to automation and AI integration. The findings can be utilized to develop government regulations, shape policies for educational program development, and forecast the socio-economic consequences of technological advancements.This research may be useful for academics, labor market analysts, and government agencies involved in employment policies and workforce adaptation strategies. The applied approaches and modeling results provide insights into assessing automation risks and developing optimal mechanisms for effectively integrating technological changes into the socio-economic environment.