The Asia Pacific Readiness Index, conducted by Salesforce, reveals that countries like Singapore, China, Japan, South Korea, and Australia have demonstrated a commendable level of preparedness to embrace the potential of generative AI.
Key Takeaways
- Countries in the Asia-Pacific region are prioritizing AI readiness, including the adoption of generative AI, to capture economic opportunities and drive growth.
- The five pillars that determine AI readiness include infrastructure, data, workforce development, ethics, and integration.
- Singapore, Japan, China, South Korea, and Australia are leading the way in AI readiness, with conducive policies and initiatives in place to enable AI adoption and implementation.
Generative AI, a technology that leverages data to produce novel outputs, has the potential to revolutionize various industries by fostering creativity and boosting productivity. The widespread adoption of generative AI is projected to yield substantial economic benefits, with experts estimating that it could contribute trillions of dollars to the global economy.
The findings of the 2023 Index reveal that Asia-Pacific economies have shown varying degrees of readiness and different approaches towards AI. However, it is noteworthy that all of these economies are placing a high priority on advancing AI and generative AI. In comparison to 2019 and 2021, AI now takes the top spot on the national agendas of all economies. The implementation of national AI strategies is already well underway, and there is a heightened awareness regarding the potential impact of AI on economic growth and development.
A recent McKinsey study projects that generative AI could add between $2.6 trillion and $4.4 trillion annually to the global economy by 2030.
The readiness of countries in the Asia-Pacific region for AI adoption is determined by five pillars: infrastructure, data, workforce development, ethics, and integration.
While overall AI readiness has improved in the region, there is still a divide between economies. Singapore, Japan, China, South Korea, and Australia rank as the top five in terms of AI readiness.
However, all countries in the region are prioritizing AI and generative AI and are implementing national strategies to harness their potential for economic growth and development. AI is seen as a driver for economic growth and digital trade and has broader implications for society.
AI readiness of Asia-Pacific economies in 2023
To help Asia-Pacific economies assess businesses’ and governments’ readiness to adopt, deploy and integrate AI, Salesforce released the 2023 Asia Pacific Readiness Index, the third edition of this bi-annual Index.
Five out of the 12 economies included in the 2023 edition – Australia, Indonesia, New Zealand, Singapore, and Thailand – have experienced an enhancement in their overall AI readiness compared to their 2021 score. This progress is a clear indication of the significant AI-related initiatives that these economies have undertaken and successfully implemented between 2021 and 2023.
Singapore has maintained its position as the top-ranked country for the third consecutive time since 2019. This achievement can be attributed to its favorable policy and business environment, which have provided a conducive atmosphere for the growth of AI. Japan and China, ranked second and third respectively, have also made significant strides in promoting AI adoption through forward-looking initiatives. South Korea and Australia, known for their contributions to the AI sector, round off the top five rankings.
Five pillars that determine AI readiness
For Asia-Pacific countries to succeed in the dynamic AI environment — including the adoption of generative AI — their economies require a sufficient level of AI readiness. Strong institutional, infrastructural, organizational and ethical foundations around AI will be critical to success now and in our global AI future. The five pillars that determine AI readiness include:
1. Infrastructure
Due to the enormous volume of data generated and used, generative AI models necessitate significant computational resources and specialized hardware. The initial step towards achieving AI readiness is to ascertain the appropriate technical infrastructure required for the intensive processing.
2. Data
Generative AI models rely heavily on the availability of substantial amounts of high-quality training data in order to learn meaningful patterns and produce realistic content. Therefore, it is of utmost importance to establish a solid foundation of reliable data and consistently refine it through iterative processes. The quality of the training data plays a crucial role in determining the outcomes and overall success of the model.
3. Workforce development
Generative AI is expected to have a profound impact on the workforce across various industries. However, it is important to note that its implementation is more likely to shift the focus within certain job roles rather than completely replacing them. The goal of successful AI integration should be to enhance the capabilities of the human workforce, rather than rendering them obsolete.
Nonetheless, there will be a growing need for individuals with AI skills and expertise. In order to meet the market’s demand for skilled AI professionals across all sectors of the economy, it is crucial for both governments and businesses to invest in cultivating a strong pool of AI talent. By doing so, we can ensure that the workforce is equipped with the necessary skills to thrive in an AI-driven future.
4. Ethics
While generative AI holds great potential to revolutionize various aspects of our lives and professional endeavors, it is crucial to acknowledge and address the associated risks. Ethical concerns, such as the possibility of generating biased or harmful content, must be taken seriously. It is imperative that we prioritize responsible and ethical innovation right from the beginning. By doing so, we can ensure that the technologies we bring to market are inclusive and intentionally designed to benefit everyone.
5. Integration
AI readiness involves not only being prepared in terms of infrastructure and data, but also having the capability to seamlessly incorporate generative AI models into practical applications and workflows. While the other pillars of AI readiness are crucial, the integration of generative AI models is particularly vital for the development of scalable, flexible, and efficient technology in the future.
