AI Automation and Labor Dynamics in Indonesian Palm Oil: Job Displacement and Skill Upgrading - Gelar Abifadilla; Euis Eka Pramiarsih; Galih Ariprayugo (Jurnal Akuntansi Keuangan dan Bisnis, 2026)
Main Purpose - This study explores artificial intelligence (AI) automation's dual impact on Indonesian palm oil labor dynamics, emphasizing the job displacement and skill upgrading interplay. Method - This study used a Systematic Literature Review (SLR) with PRISMA 2020 guidelines to analyze 25 peer-reviewed articles from Scopus and Web of Science, supported by grey literature from sources such as the WEF and ILO. The articles were selected, screened, and analyzed systematically using thematic synthesis. Main Findings - AI-driven harvest automation may reduce the need for low-skilled manual workers, but it will increase demand for workers with technical skills and green ESG competencies. At the same time, AI can help plantations overcome labor shortages by supporting a shrinking plantation workforce. However, its adoption requires inclusive EaaS models for smallholders, better infrastructure, strong data governance, and gender-inclusive workforce policies. Theory and Practical Implications - This study combines the Technology-Organization-Environment (TOE) framework and Skill-Biased Technological Change (SBTC) theory to explain how technology affects skills and work in agribusiness. In practice, the Pentahelix matrix offers strategies for government, universities, industry, communities, and media to reduce unemployment risks, protect data privacy, and support an inclusive workforce transition. Novelty - This research contributes to contextualizing global agricultural automation discourse within the Indonesian palm oil sector, positioning AI as a vital catalyst for workforce evolution amidst pronounced demographic and structural challenges.