SUPPORTING MID-CAREER WORKFORCE TRANSITION IN THE ERA OF ARTIFICIAL INTELLIGENCE AND DIGITAL TECHNOLOGIES THROUGH LIFELONG LEARNING A CASE STUDY OF SINGAPORE’S JOBS TRANSFORMATION MAPS

Abstract

This academic article aims to analyse approaches to supporting the transition of mid-career workers in the era of artificial intelligence, automation, and digital technology through a case study of Singapore’s Jobs Transformation Maps (JTMs). It examines the interrelationships among lifelong learning, anticipation of job transformation, job redesign, and skills development for mid-career workers. The article draws on official documents from Singapore, academic literature, international organisations, and Thai government agencies to synthesise systemic lessons for Thailand. The findings indicate that Singapore’s Jobs Transformation Maps are not merely labour market forecasting reports, but mechanisms that integrate future-of-work intelligence, skills requirements, job redesign, and skills development support measures. At the same time, SkillsFuture and Career Conversion Programmes (CCPs) support mid-career workers in reskilling and upskilling so that they can move into job roles with growth potential. Singapore’s approach can therefore be explained through the “anticipate–redesign–reskill” framework as a lifelong learning system that connects the information, organisational, and individual levels. However, the system continues to face limitations related to time, cost, digital learning readiness, motivation, and differences among groups of workers. For Thailand, key considerations include developing sectoral skills intelligence systems, supporting job redesign, linking occupational standards databases with labour market needs, and developing lifelong learning mechanisms suitable for mid-career workers. Lessons from Singapore should therefore be adapted in principle to fit the Thai context, rather than directly transferring Singapore’s policy model