Authors : Vusi S. Mncube

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/3x4uuxur

Scribd : https://tinyurl.com/ys6hdccm

DOI : https://doi.org/10.38124/ijisrt/26jan892

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : As the world continues to witness advancements in Artificial Intelligence (AI) and Machine Learning (ML) technologies, global effects on the job market start to be dramatically realized. This systematic review consolidates empirical as well as theoretical literatures to examine how AI/ML reshapes human work across industries-adhering to emerging trends, structural issues, and emerging opportunities. Based on insights from peer-reviewed articles, industry reports, and empirical research, the study reveals a two-way dynamic of displacement and augmentation: as automation disproportionately impacts routine and low-skilled jobs, AI is simultaneously augmenting professional work and enabling new forms of labor such as gig work and human-AI collaboration. Main challenges include skills polarization, digital inequality, and psychosocial stress, especially in developing regions with inadequate digital infrastructure. Conversely, the review identifies paths of innovation, reskilling, and entrepreneurship empowerment via AI. The study integrates several theoretical frameworks—Technological Determinism, Socio-Technical Systems Theory, and Skill-Biased Technological Change—to conceptualize these innovations. Furthermore, two conceptual models—the AI/ML-Driven Labor Market Transformation Model and the Sectoral Impact and Resilience Model—are introduced to illustrate labor transformation across sectors and skill levels. The review concludes by suggesting a framework for future research, policymaking, and employment adaptation policies for the AI age.

Keywords : Artificial Intelligence (AI), Machine Learning (ML), Human Labor, Skills Gap, Displacement, Inequality.

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