Artificial Intelligence and Employment Transformation: A Multi-Sector Analysis of Workforce Disruption and Adaptation - Kanagarla Krishna Prasanth Brahmaji (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
This academic investigation examines the bifurcated impact of artificial intelligence (AI) on contemporary labor markets, analyzing both displacement effects and employment generation across multiple sectors (n=327) during 2020-2024. Through a mixed-methods approach combining econometric analysis of industry-level data, semi-structured interviews with key stakeholders (n=142), and longitudinal case studies of AI-implementing firms (n=47), we demonstrate that while AI automation has led to a 23.4% reduction in traditional middle-skill jobs across manufacturing, logistics, and administrative sectors, it has simultaneously generated a 31.7% increase in new employment categories, particularly in AI development, human-AI collaboration, and digital transformation roles. The findings reveal significant sectoral variations in job displacement rates (ranging from 8.2% to 37.6%) and identify critical factors influencing successful workforce transition, including the timing of reskilling initiatives, the nature of institutional support, and the elasticity of labor market responses. Notably, organizations that implemented proactive reskilling programs achieved a 64% higher retention rate of displaced workers compared to those utilizing reactive approaches. The article also uncovers an emerging "adaptation gap" wherein 42% of displaced workers face significant barriers to transitioning into new roles, primarily due to misaligned skill development programs and insufficient support infrastructure. These findings have important implications for policymakers, business leaders, and educational institutions in developing targeted interventions to facilitate effective workforce adaptation in an AI-driven economy.