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https://www.zaobao.com.sg/forum/views/story20260223-8619550?utm_source=android-share&utm_medium=app
2026-02-23
Author: Karen Tay 郑智月 is the founder and Chief Executive Officer of local consulting firm Inherent Pte. Ltd.
Author: Stephanie Sy 薛芬妮 is the founder and Chief Executive Officer of artificial intelligence and data company Thinking Machines
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In his recent Budget speech, Prime Minister Lawrence Wong stated that the government will further step up investments in artificial intelligence (AI) — from infrastructure and enterprise applications to talent development. The direction is clear: AI will shape Singapore’s next phase of economic competitiveness. Standing still is no longer an option.
However, as companies accelerate AI deployment, a less noticed question is emerging: why, despite sustained investment and significant productivity gains, does adoption within organizations often appear hesitant?
A product manager working in Silicon Valley told me that AI now enables him to complete work that previously required collaboration among three to five people. Research integration is faster, first drafts are clearer, and iteration cycles are significantly compressed. Then he added, “If I keep working at this level of efficiency, my role might not exist next year.”
This remark captures the current tension. When AI is primarily defined as an efficiency tool — faster output, leaner teams, more quantifiable performance — individuals find it difficult to see how their long-term value within the system is enhanced. If better performance seems to imply a diminished role, hesitation toward AI becomes understandable.
Productivity is undoubtedly important. AI can compress writing cycles, accelerate research, and reduce routine tasks. These gains are real. But productivity is only the baseline, not the end point. The more critical question is: as AI is introduced, is it also simultaneously elevating higher-order cognition and decision-making within organizations? The capability referred to here is not about hierarchical position, but about clearly defining problems, examining assumptions, integrating complex information, and taking responsibility for judging the next course of action. AI can either strengthen this capability or leave it unchanged.
In some organizations, AI is mainly used for retrieval and polishing. Input a prompt, receive an output, and if the text is acceptable, pass it along. Efficiency improves, but the level of thinking does not change. In other teams, AI is treated as a second brain — an extension of human thinking and judgment. Before submitting reports, they use it to examine arguments, generate counterpoints, compare different frameworks, and simulate potential consequences. In this mode of use, AI does not replace judgment; it expands the depth and scope of judgment, forcing people to define problems more precisely.
We worked with a team that initially adopted AI simply to speed up document drafting. Later, before circulating documents, they began using AI to help organize strategic logic and identify blind spots. When entering meetings, everyone had greater clarity and accountability regarding their own arguments. Productivity gains remained, but at the same time, cognitive capability also improved. This compounding effect of “efficiency layered with capability” is the true transformative significance of AI.
If AI can enhance higher-order capabilities, training and evaluation systems must reinforce this direction. If emphasis is placed solely on tool proficiency, organizations will produce more efficient executors. If training revolves around problem definition, contextual understanding, and rigorous judgment, organizations will cultivate more mature decision-makers. Evaluation frameworks are equally critical. If outcomes are measured only by time saved or costs reduced, these metrics will naturally drive behavior, yet they cannot show whether people’s capabilities are actually improving.
We may need to ask further: Has decision quality improved? Are more people taking responsibility for structured thinking? When employees leave their roles, are they more capable of defining and shaping work than when they entered? These questions relate to long-term resilience.
SkillsFuture has long emphasized maintaining employability relevance amid industrial change. This system has given Singapore adaptability. But AI alters the basis of relevance. When routine analysis and integration can be scaled and automated, value will increasingly concentrate on those who can define problems, identify opportunities, and shape direction.
In such an environment, merely aligning with the next job is no longer sufficient. More importantly, one must possess the capability to shape work. This does not mean everyone must become an entrepreneur; rather, it means being able to recognize unmet needs, integrate resources, and conduct responsible experimentation within and beyond existing structures.
If AI is introduced with such expectations, it will become an accelerator of capability leaps. Even if roles change, those with higher-order cognitive and decision-making abilities will be better positioned to define their contributions in the next phase.
Singapore has no natural resources. Our long-standing advantage lies in the capabilities of our people. When we invest in AI infrastructure, we are not only making technological choices; we are also setting societal expectations about capability. We can view AI as an engine of efficiency, or we can see it as a mechanism to elevate higher-order capabilities across society.
In the AI era, competitiveness depends not only on the speed of technological deployment, but also on whether such capabilities are broadly enhanced, and whether institutions provide people the space to exercise them. This is the deeper design question behind AI investment. It will determine not only efficiency, but also resilience and innovative capacity.
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Author: Karen Tay 郑智月 is the founder and Chief Executive Officer of local consulting firm Inherent Pte. Ltd.
Author: Stephanie Sy 薛芬妮 is the founder and Chief Executive Officer of artificial intelligence and data company Thinking Machines
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