The Threat of AI Automation on Employment Termination: A Systematic Literature Review
Abstract
The rapid diffusion of artificial intelligence (AI) across industries has intensified concerns regarding labor substitution, yet scholarly literature on AI-driven employment termination remains fragmented. This study systematically identifies displacement mechanisms, maps vulnerable sectors and workforce categories, and synthesizes mitigation strategies. Following the PRISMA 2020 protocol, a Systematic Literature Review (SLR) was conducted via Google Scholar across three search sessions, yielding 25 eligible peer-reviewed articles (2016–2026) from 520 initial records. Data were analyzed using thematic synthesis complemented by keyword co-occurrence bibliometric mapping in VOSviewer. Results indicate four interrelated displacement patterns: hierarchical task substitution, skill degradation and labor market polarization, acceleration coinciding with generative AI, and opaque algorithmic layoff practices. Service, information technology, and labor-intensive sectors are identified as most vulnerable, with middle-skilled workers experiencing the greatest structural pressure. Effective mitigation requires a simultaneous three-pillar approach: competency-based reskilling, algorithmic accountability regulation, and collaborative labor union engagement, with proactive timing highlighted as critical. These findings offer a synthesized evidence base for policymakers and organizations navigating an AI-augmented labor market. Given that only one reviewed study directly examined Indonesia, these findings serve as contextual implications rather than direct empirical assertions, underscoring the necessity for future primary research in developing national contexts.
