Singapore has spent the last two years building real momentum around AI adoption. Policymakers, employers, educators, and workers are increasingly aligned on the importance of AI capability in shaping workforce competitiveness. The national intent is clear, the funding is there, and awareness of AI among the working population has never been higher.
What is less clear is whether that momentum is actually changing how work gets done inside most organisations. Many professionals today are producing faster, more polished output, but it’s unclear if they can be critical about their output, which comes with experience.
But we also need to recognise the scale of behavioural change being asked of the workforce. Many professionals today have built their careers around ways of working that reward precision, hierarchy, and predictability. AI changes not just the tools people use but also the nature of how work gets done. That transition naturally takes time, especially at a workforce scale. Policies and national initiatives help create momentum, but mindset change inside organisations has always moved more slowly than headlines.
Why adoption is still highly individual
One main blocker we are seeing is that AI adoption is still highly siloed and deeply individual. We see individuals experimenting, but the majority of them are doing so only in their personal chat windows. Little is shared structurally beyond the person who found it. Without an institutional memory layer or shared playbook that captures what is working, you get a patchwork where a few power users produce impressive outputs, while everyone else continues working in roughly the same way they always have. The gain lives and dies with the individual. For organisation-wide impact, a deliberate redesign of workflows, KPIs, or operating models will eventually need to follow. Either through top-down directives or a conscientious effort by the entire staff.
The next blocker is arguably the most practical: if it isn’t broken, why fix it? Not everyone is a productivity advocate and lies awake worrying about workflow inefficiency. While some firms are redesigning roles and creating new AI-related positions, without AI visibly taking anyone’s job tomorrow, we tend, as creatures of habit, to stay with what works.
The third blocker is structural, particularly among those who have attempted to look into AI implementation. The most commonly cited constraints are high implementation costs and lack of in-house expertise. The tools exist. In many cases, the willingness exists too. But the bridge between “I’ve heard of AI” and “we’ve redesigned our workflow around it” is still too long and too expensive for most SMEs to cross without support.
The gap between training and workplace application
Many upskilling efforts lose momentum, not at the programme level itself, but in the whole operational layer between someone finishing a programme and actually deploying AI in their day-to-day work. During training, learners build tangible, reusable templates, prompt libraries, workflow documentation, and examples that they can use the next working day.
The harder challenge begins when that momentum needs to scale beyond the individual. As a learner, you’d have tried tools, broken things, and built something that worked. You’re excited. But a colleague who has only heard headlines about AI replacing jobs will need to be managed with patience rather than evangelism. Sustainable adoption across an entire organisation requires structured rollout, management buy-in, clear guardrails, and enough time for trust to build.
What HR leaders should focus on next
For HR leaders, this matters because AI capability is deeply role-contextual. A marketer, finance analyst, and healthcare worker do not need the same AI fluency. Their workflows, decisions, and risks are different. Without training tied to someone’s actual role and updated as the landscape shifts, organisations risk building a number to report rather than a capability workers can actually use. That is when it becomes performative upskilling.
A stronger approach to workforce upskilling would create a firmer bridge between structured training and rapid on-the-job application. Companies should set aside dedicated hours each month for employees to test AI on actual tasks, just like how R&D time is currently ringfenced in tech companies, or how “timetabled time” is set aside for teachers to dedicate time to innovation and professional development. Policy can reinforce this by tying enhanced grants to organisations that implement and report on these pilots.
Once organisations have a core group of upskilled champion users, they can guide the rest of the team to start small by perhaps summarising meeting notes or automating a two-step workflow. The goal is to learn by doing something real and low-stakes, because the cost of not learning is ultimately higher than the cost of getting it wrong. Done is better than perfect.
Grants and national initiatives can reduce the risk of taking the first step, but they cannot redesign workflows on behalf of the employer. This shift must occur internally, within the organisational fabric. For enduring change, AI must be structurally integrated into one’s standard operational flow rather than being an operational side experiment.
About the author
Felicia is the director of Tribe Academy, where she leads strategic initiatives to upskill emerging tech talent and meet evolving corporate needs. Under her direction, Tribe Academy develops professionals equipped with practical industry skills for tomorrow’s digital economy.
