AI-Enabled Automation, Labor Market Vulnerabilities, and Structural Transformation within an Applied Informatics Framework for Economic Analysis

The accelerated expansion of machine learning and intelligent automation is profoundly reshaping the future of work and the structure of global labor markets, transforming production processes, skill requirements, and employment patterns.This paper provides a critical analysis, based on the specialized literature, of the main vulnerabilities generated by AI-based automation, with a focus on the automation of repetitive tasks, the reconfiguration of occupations, and the intensification of gaps between existing and emerging skills.In a multidisciplinary framework, the study examines the mechanisms through which AI influences job polarization, productivity dynamics, and the redistribution of economic opportunities, highlighting differential effects across sectors and categories of workers.A distinct focus is placed on the risks associated with algorithmic management and the digitalization of performance evaluation, including the impact on work quality, occupational health, autonomy, and the contestability of decisions.The paper also discusses the ethical and public policy challenges regarding the transparency of algorithmic systems, data governance and worker protection in automated work environments.The conclusions support the need for adaptive public policies that combine investments in education and continuous training with governance standards and occupational security, to strengthen the resilience, inclusion and competitiveness of the workforce in the era of smart technologies.