Corporate Strategies for Retraining Workforces Displaced by Workplace Automation

Abstract

The job market is changing as a result of workplace automation, which has led to the emergence of opportunities for new technical, analytical, interpersonal and problem-solving skills while eliminating specific routine jobs. This is all being driven by the growth of artificial intelligence (AI), robotics, machine learning, advanced analytics, and digital platforms, making corporate retraining strategies even more critical. The study looked at corporate policies for retraining employees whose job or task was significantly impacted by workplace automation. The study used a secondary quantitative research approach to gather data from internationally harmonised worker-level evidence, international employer surveys, OECD evidence, International Labour Organisation (ILO) research and previous studies on automation, skills, training and organisational transformation. The main empirical evidence comes from over 90,000 employees in 37 industrialized countries, as well as evidence from the World Economic Forum's Future of Jobs 2025 employer survey and OECD evidence on AI-related training. Data analysis involved the following: Descriptive statistics; Comparative analysis of data; Evidence from regression analysis; Fixed-effects estimation; Entropy balancing; Effect-size interpretation and thematic analysis of corporate retraining strategies. The empirical findings indicated that the individual workers who received the company training faced 3.8 percentage points less chance of being automated, or about 8% of the average chance of being automated, and the trained workers increased their hourly wages by 7.1%. Training also helped to enhance workers' engagement in less automatable tasks and helped develop their digital skills. According to the World Economic Forum evidence, 85% of employers surveyed identified workforce upskilling as a key strategy for workforce, 77% planned to focus on reskilling and upskilling people to collaborate with AI, and 47% planned to shift people from jobs impacted by AI to new roles. The results showed that successful corporate retraining is not just one training program, but a system in which workforce skills are mapped, early assessment of automation risk, personal learning paths, paid learning time, management support and internal mobility, digital and AI competency, experience learning, mentoring and ongoing assessment are all interdependent. The research found that a comprehensive reskilling strategy that is embedded in the business model, internal talent mobility, job redesign and technology change is more effective in making companies resilient in terms of their workforce. The results validate a shift from a replacement to a human-technology complementarity model in which attention is paid to human resources skills building alongside automation.