A Tale of Differing AI Priorities in APAC: Hans Dekkers, IBM
The Asia-Pacific region is a vibrant and diverse landscape in the adoption and implementation of Artificial Intelligence (AI), with each country prioritising different aspects based on its unique circumstances. Hans Dekkers, General Manager of IBM APAC, highlights these contrasting approaches, noting that while some nations focus on cost reduction and automation, others emphasise technological advancement and addressing labor shortages. As Dekkers states, "These different initiatives, as well as each country's unique circumstances, seem to influence different priorities". This difference in focus is significantly influenced by government policies, industry initiatives, and the unique economic and cultural landscapes of each country.
Singapore the AI Hub
Singapore, for instance, has positioned itself as a major AI hub, with a focus on building a digital economy driven by innovation and a skilled workforce. The country's National AI Strategy 2.0, launched in December 2023, allocated S$70 million to advance AI research, enhance engineering capabilities, cultivate talent, foster collaboration, and create a trusted digital ecosystem. Hans observes that Singapore aspires to become a global partner and major business AI hub which fosters creators, practitioners, and users through programs like AI Singapore and by building a supportive infrastructure and ecosystem". This is reinforced by collaborations like the one between IBM and the National University of Singapore (NUS) to establish a research and innovation center aimed at accelerating the adoption of AI innovations within Singapore. This proactive approach reflects Singapore's ambition to stay ahead technologically and become a global AI leader.
AI to Boost Productivity in Australia
In contrast, Australia's AI strategy is driven by the need to boost productivity, address AI skills shortages, and tackle national challenges such as job creation and economic recovery. According to Hans, "Australia's focus on cost reduction, automation, and investment highlights its commitment to leveraging AI for economic productivity and competitiveness, exemplified by programs like the Digital Solutions initiative". Programs like the Digital Solutions initiative, as well as a plan to develop a national AI strategy, aim to strengthen the country’s AI capabilities and attract investment. This illustrates a focus on practical applications of AI to enhance economic performance and competitiveness, reflecting its unique circumstances and priorities.
Practical AI for Developing Economy
The diversity in AI adoption is further exemplified by other countries in the APAC region. Hans notes, "More developed economies tend to focus on cutting-edge AI applications, while developing economies often prioritise practical solutions for immediate challenges". In India, the emphasis is on financial services and technology development, with AI being deployed in the service sector and for solutions that can scale across its large population. This is demonstrated by SUD Life’s use of AI in capital markets and investment products. Additionally, local language processing and cultural nuances significantly influence AI development, as seen in Thailand's development of language-specific models. For instance, Kasikorn Business-Technology Group (KBTG) developed its own Thai language LLM called “THaLLE,” specifically for finance, reflecting the region’s focus on addressing local language and cultural needs.
AI's ROI
Hans also highlights that the long-term benefits of AI can often clash with traditional ROI metrics, which focus on short-term gains. He suggests that businesses adopt comprehensive AI evaluation frameworks that assess both financial metrics and broader impacts, such as job roles and data governance. He emphasises the importance of balancing short-term wins with long-term AI strategies through a two-step process of prioritising structured assessments and evaluating technical feasibility. As Hans explains, "A holistic costing strategy involving business, technology, data and finance teams is critical for accounting for all expenses across the project lifecycle". He also notes that "Organisations should establish a cross-functional AI board responsible for determining ROI from a line-of-business perspective, and develop a workforce strategy that helps their people innovate".
AI VS Humans
The development of AI also raises concerns about ethical deployment, particularly when systems are constantly learning from human behaviour. Hans underscores the importance of "implementing strong governance frameworks and maintaining human oversight of AI systems to ensure ethical deployment". He notes the importance of organisations maintaining a "focus on transparent and responsible AI development that prioritises fairness and accountability". This is demonstrated by organisations such as KBTG, which maintains a human-in-the-loop review of AI-generated outputs. IBM’s AI Ethics Council also reviews new AI products and services to ensure they align with the company’s AI principles.
Furthermore, as AI becomes more sophisticated, Hans notes that it is crucial to ensure that AI remains a tool for human progress, rather than leading to job displacement or social disruption. He explains that the focus must be on using AI to augment human intelligence, not replicate it. He believes that human-AI collaboration should evolve towards human-centered innovation that enhances human capabilities, incorporating empathy and transparency in AI systems. He points out, "The APAC AI Outlook envisions human-AI collaboration evolving beyond basic productivity tools toward human-centered innovation that enhances capabilities rather than replacing them". To ensure smooth adoption, organisations should prioritise cultural alignment and employee training. He points to the example of a leading ASEAN automotive company that focused on up-skilling and re-skilling employees to foster acceptance of AI.