Artificial Intelligence and the Future of Employment: A Sociological Study

The rapid advancement of Artificial Intelligence (AI) is transforming the nature of work, employment structures, and labour relations across the globe. This sociological study examines the implications of AI-driven technologies on the future of employment, focusing on structural changes, social inequality, skill transformation, and labour market stratification. Drawing upon theoretical perspectives such as Karl Marx’s analysis of technological change and labour alienation, Max Weber’s concept of rationalization, and Emile Durkheim’s theory of division of labour, the study situates AI within broader processes of modernization and capitalism. It explores how automation, machine learning, and algorithmic management are reshaping occupational roles, leading to job displacement in routine and low-skilled sectors while simultaneously generating new opportunities in high-skilled and digital domains. The article highlights the emergence of a polarized labour market characterized by the growth of precarious gig work and the increasing demand for advanced technological competencies. It critically analyses how AI may reinforce existing social inequalities based on class, gender, and educational access, particularly in developing economies. Furthermore, the study addresses the transformation of workplace authority through algorithmic surveillance and performance monitoring, raising concerns about autonomy, dignity, and workers’ rights. Using secondary data, policy reports, and sociological literature, this research argues that the impact of AI on employment is not technologically deterministic but socially constructed and mediated by institutional frameworks, state policies, and educational systems. The study concludes that proactive policy interventions, inclusive skill development strategies, and ethical governance frameworks are essential to ensure that AI contributes to equitable and sustainable employment futures rather than deepening socio-economic disparities.