Authors : Vusi S. Mncube

Volume/Issue : Volume 10 - 2025, Issue 9 - September

Google Scholar : https://tinyurl.com/y2uabs5w

Scribd : https://tinyurl.com/3xpj47x6

DOI : https://doi.org/10.38124/ijisrt/25sep1465

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Abstract : This study introduces two new conceptual frameworks—the Displacement–Augmentation Continuum (DAC) and the Sectoral Impact and Resilience Model (SIRM)—to examine the complex interaction of artificial intelligence/machine learning (AI/ML) technologies with human labour. Grounded in interdisciplinary literature, empirical trend analysis, and policy analysis, the study dismisses the naive binary models that dichotomise labour as displaced or augmented, along with the deterministic sectoral risk approaches. DAC reconceptualises work transformation along a continuum, identifying five stages ranging from total displacement to human-led AI collaboration, and stresses that most transformations involve shifts in human–machine interaction rather than job displacement per se. The SIRM model maps sectoral exposure to automation against adaptive capacity, producing a dynamic matrix that guides differentiated policy responses. Rooted in task-based economic theory, sociotechnical systems thinking, and resilience theory, these models provide an integrative view of both micro-level task change and macro-sectoral restructuring. These frameworks offer policymakers, educators, and business leaders, effective tools for influencing the AI-driven future of work.

Keywords : Artificial Intelligence (AI), Machine Learning (ML), Human Labour, Displacement-Augmentation Continuum (DAC), Sectoral Impact and Resilience Model (SIRM), Future Work.

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