The Taiwan Banker

Artificial Intelligence (AI) and robotics are advancing quickly. Applications are no longer limited to fixed machinery, and new technology has entered fields related to operations, identification, and logic. The scope of its human labor replacement will correspondingly expand from blue-collar to skilled white-collar work. The increasingly broad application areas of artificial intelligence (AI) and robotics will impact society, economy, business, and individual lifestyles. The resulting replacement of human labor is becoming an increasingly urgent topic. Applications are no longer limited to fixed machinery. New technology has entered fields related to operations, identification, and logic. The scope of its human labor replacement will correspondingly expand from blue-collar to skilled white-collar work. Businesses must keep abreast of this trend and promptly adjust production processes and human resource deployment, and even their business models, in order to increase their operating flexibility and expand their breadth. To narrow the digital gap, meanwhile, governments must develop proactive countermeasures, taking account of technological dividends. Rapid technological development and automation can serve as an engine of economic growth, driving social innovation and development, but they can also have strong distributional effects, particularly on employment and salaries.The ARI assessment focuses on three major policy areasA number of international organizations have begun to investigate possible changes coming in the digital era. The World Economic Forum (WEF)’s Global Information Technology Report includes a Networked Readiness Index (NRI), and the International Institute for Management Development (IMD) World Competitiveness report includes an indicator for “future readiness.” Their evaluation basis can be roughly divided into regulatory environment, infrastructure, readiness, and threat surfaces. In April, the Economist Intelligence Unit (EIU) published the world’s first national Automation Readiness Index (ARI), measuring the automation level of 25 countries, and the coping ability of their respective labor forces. This cross-border comparison can help Taiwan understand its readiness to global digitalization and come up with future policy directions.The ARI focuses on three policy areas: innovation environment (including research and innovation, infrastructure, and ethics/security), education policies, and labor market policies – covering a total of 30 mid-level indicators, and 52 detailed indicators (45 qualitative and 7 quantitative). It covers R&D on robotics, automation, and AI, private sector investment in R&D, the regulatory environment for innovation and for new existing industries using new technologies, transfer mechanisms for international innovation partners, support for new initiatives, national broadband strategy, cluster development plans, science and technology ethics and security institutions, data protection, information security, and public data literacy, etc. The education policies indicator mainly includes: (1) Basic education; (2) secondary education; (3) Continuing education; and (4) Academic environment. The detailed indicators include pre-school education policy, 21st century skills and knowledge development, technological education (including compulsory and secondary levels), promotion of higher education opportunities, STEM enrollment, national lifelong learning strategy, on-the-job training, career guidance planning, instructional training planning reform, enrollment in AI and data processing courses, innovation in educational operating models (such as campus autonomy and scheduling flexibility), and communication between teachers and business, etc.The many challenges of digitalization in mid-income countriesThe EIU sample included 25 countries based on income level, digitalization level, and geography. The results included 10 high-income countries, 7 mid-income countries, and 2 low-income countries (India and Indonesia). Estonia and Singapore were counted as highly digitalized countries. The UAE, Columbia, Malaysia, and Vietnam respectively represented emerging economies in the Middle East, Latin America, and Southeast Asia. South Korea received the highest comprehensive score, followed by Germany, Singapore, Japan, Canada, and Estonia. Even though the US and China are AI powerhouses, their respective ARI rankings were only 9th and 12th. Taiwan was not evaluated, but it would score well based these indicators.Around the world, innovation environment scored 69.9, labor markets were 60.4, and education was 55.3. This shows that in response to AI and automation, countries have mainly optimized the innovation environment, and reduced the innovation gap, but paid less attention to the skills gap. Importantly, the EIU believes that the challenges posed by AI and automation require extensive participation and serious response from all sectors of society, including governments, industry, education experts, and other stakeholders. Different countries however have different views on the impact on employment, so even if the private sector is active, society as a whole lacks discussion and communication on the topic. The result is that related policies are in their infancy, even in advanced countries. Middle-income countries, meanwhile, will face more challenges. Their low-income counterparts still rely on agriculture, and are less affected by these trends, while high-income countries have been making more investments. For those stuck in the middle, it takes more effort to find business opportunities from AI.The EIU also emphasizes the importance of Science, Technology, Engineering, and Mathematics (STEM) education and so-called soft technology. Aside from a few developed economies, though, most countries have still failed to adjust their curricula or re-educate their teachers to enable students to adapt to the new era after entering the workforce. Lifelong learning may become the norm in this new economy. Some countries, such as Singapore, have begun experimenting with SkillsFuture Credit Accounts, giving the public funds for lifelong learning courses. In addition to similar grants, Germany has also modified employee insurance to cover lifelong workforce skill upgrades. The EIU however believes that vocational education in most countries is still unprepared to meet the requirements of the AI era. Its focus is more on low-level technicians, rather than experts in new technology.The higher the ARI score, the smoother the transitionARI is correlated with a number of positive development factors. Its correlation with GDP is positive in countries with low per capita GDP, indicating that these countries can boost GDP by strengthening their innovation environment, human capital, and labor markets. For countries with higher GDP, though, the two are less correlated, possibly because these countries already have made advanced preparations in ARI. Since AI is an aspect of technological advancement, it enhances the productivity of labor and capital, so ARI is positively correlated with total factor productivity. Interestingly, it is negatively correlated with unemployment, indicating that although automation will replace some routine work, it will also create new jobs, giving countries with higher ARI a smoother labor transition. They are not the ones who have to worry about unemployment from automation. It is worth noting that ARI is generally negatively correlated with salary growth, indicating that countries with higher ARI may depend more on technological skills and capital. Thus, technology may have an inhibitory effect on low-skilled salaries, and also be bad for income distribution.The Taiwanese government is attaching great importance to the possible impact of digital economy and AI on society, education, and labor markets. The National Development Committee has also held several forums. It will work to carefully coordinate the policies between ministries, create a favorable environment for automation, and proactively create opportunities for Taiwan to continue to lead in AI and automation.