The Economic and Organizational Consequences of AI-Induced Workforce Displacement: A Mixed-Methods Investigation
Shankar S. Iyer, Raman Subramanian, Rajesh Arora, Dr Brinitha Raji, Veena Christy, Aaryan Kapur, RADHAKRISHNAN NATARAJAN · Human Resources Management and Services · 19 Oct 2026
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Where this sits in the debate
- Will AI destroy more jobs than it creates? · its finding: it depends (why studies differ)
- Will AI widen inequality? · its finding supports the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
A mixed-methods study combining a survey of 2,350 workers in 12 countries with 142 interviews reports that routine-intensive occupations faced higher displacement risk and that manufacturing and administrative services saw employment declines associated with AI adoption. Displaced workers reported average income losses of 26.7% and transitions lasting about 17 months.
Abstract
Abstract: Artificial intelligence (AI) is transforming global labor markets by automating routine tasks, reshaping employment patterns, and creating significant economic and organizational challenges. This study investigates the multidimensional consequences of AI-induced workforce displacement by examining employment trends, wage polarization, worker experiences, organizational adaptation strategies, and relevant theoretical perspectives. An explanatory sequential mixed-methods design was adopted, combining quantitative analysis of survey data from 2,350 workers across 15 industries in 12 countries between January 2021 and June 2022 with longitudinal employment statistics and regression modelling, alongside qualitative analysis of 142 semi-structured interviews with displaced workers, organizational leaders, and HR professionals. Data were analyzed using SPSS v28, Stata v17, and NVivo v12 following institutional ethical approval (Protocol No. WUC-IRB-2021-047_ Dated 7 January 2021), with inter-coder reliability of κ=0.83. Results showed that routine-intensive occupations faced a 67% higher displacement risk, while manufacturing (β=-0.43, p<0.001) and administrative services (β=-0.38, p<0.001) experienced significant employment declines associated with AI adoption. Displaced workers reported an average income reduction of 26.7% and employment transitions lasting 16.8 months. Qualitative findings highlighted psychological distress, organizational culture shifts toward AI adoption, inadequate reskilling infrastructure, and insufficient institutional support, whereas organizations implementing proactive reskilling programs achieved 64% higher employee retention. Overall, AI-driven workforce displacement extends beyond job losses, requiring coordinated policy interventions, institutional support, and continuous workforce reskilling to promote equitable labor market transitions, organizational resilience, sustainable employment, and inclusive economic growth in increasin
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