East Asia is a key driver of global economic and technological progress. While many of its economies lead in AI adoption and literacy, they are grappling with a shrinking workforce due to ageing populations and declining birth rates. To address this challenge, the AI Opportunity Fund: Asia-Pacific, led by AVPN with support from Google.org and the Asian Development Bank, commissioned a comparative policy study to identify effective strategies and lessons for bridging the silver divide in the region. The study builds on insights from AVPN’s AI for All landscape report, which shows that mature workers are 1.6 times more likely to express concerns about AI trustworthiness and twice as likely to face language barriers when accessing AI-related opportunities. Japan, South Korea, and Hong Kong were selected as focus markets for this analysis.
The study found that while all three economies recognise the urgency of reskilling older workers, they each face unique challenges shaped by their socio-economic contexts. Japan, with nearly 30% of its population aged 65 and above, faces strong cultural resistance to retraining despite government initiatives under its Society 5.0 vision. South Korea and Hong Kong, classified as super-aged societies with more than 20% of residents over 65, contend with early retirements and high numbers of older adults in low-skilled or low-income work. South Korea struggles with the highest poverty rates among older persons in the OECD, compounded by seniority-based pay structures and limited lifelong learning infrastructure, making the integration of older workers into the AI workforce challenging. Hong Kong lacks a dedicated policy for AI and ageing, and gaps in digital literacy and language skills limit older adults’ participation in training programmes.
To address these challenges, the study proposes tailored recommendations for each economy. In Japan, it suggests improving data collection and evaluation for more targeted insights, aligning training content with industry relevance, providing incentives for private sector involvement, and enhancing labour market mobility to increase engagement in skilling initiatives. In South Korea, the recommendations include establishing a National AI Workforce Development Committee for cross-ministerial coordination, reforming seniority-based wage systems toward performance-based models, scaling community AI learning centres, and creating industry-specific transition programmes. Long-term objectives involve developing a unified competency framework, implementing outcome-based evaluation, and institutionalising lifelong learning. For Hong Kong, the study advocates a multi-tiered approach across policy, government, organisational, programme, and societal levels, with priority given to establishing a dedicated AI Workforce Development Strategy, promoting cross-bureau coordination, investing in infrastructure, and supporting community-centric solutions.
The analysis underscores the urgent need to reskill and integrate older workers into the AI-driven economy through targeted strategies. It highlights the importance of effective government coordination in policy design and implementation, structural reforms to modernise labour and compensation systems, and community-based interventions to foster lifelong learning. The AI Opportunity Fund continues to engage policymakers and cross-sector leaders to advance reskilling strategies, aiming to ensure that mature workers are not left behind in the transition to an AI-enabled future.
