Almost all (95%) of employers in Singapore continue to face tech hiring challenges despite a broader availability of talent in the market, according to new research from General Assembly (GA), part of LHH, the professional talent solutions arm of The Adecco Group.
This is from a report based on a survey of senior HR professionals and talent acquisition leaders across the United States and the United Kingdom. This year marks the first time a Singapore-focused snapshot has been included, benchmarking local talent trends against global markets.
The Singapore findings reveal that while hiring pressures have eased slightly, nearly all employers continue to face challenges — driving a shift towards flexible talent models and increased investment in AI upskilling. The findings come as Singapore advances its National AI Strategy 2.0 and workforce transformation efforts under SkillsFuture.
“The findings highlight a clear shift in how organisations and individuals are approaching AI skills, with growing recognition that upskilling must be a shared responsibility,” said Sima Sadaat, country manager of General Assembly Singapore.
“In Singapore’s tech-driven economy, the ability to apply AI effectively is essential across roles, not just in technical functions. At General Assembly, we are seeing both employers and individuals take greater ownership of upskilling which is a welcome trend,” she said.
The report reveals that AI skills are increasingly seen as a shared responsibility between employers and employees — a shift that reframes AI upskilling as a mutually shared mandate.
In Singapore, this dynamic underscores a growing expectation for individuals to take a proactive role in building AI literacy, even as organisations step up their support and enablement, slightly higher than in the US (48%) and the UK (41%).
GA said that more than 80% believe organisations should take at least partial responsibility, reinforcing the need for structured learning pathways. This is especially critical given that the demand for tech talent remains high—particularly in data-related roles.
While 95% of employers in Singapore report hiring challenges—broadly in line with global markets—the gap is most acute in data analytics and data science roles, cited by 58% of employers as the hardest to fill.
Upskilling is expected to play a key role in addressing talent shortages, with 69% of employers in Singapore saying it will have a significant impact by 2026. However, cost remains a key barrier, cited by 58% of organisations, higher than in the US and UK, suggesting that scaling training programmes remains a challenge.
The shift towards in-house training in Singapore reflects a strong intent to align capability-building with business needs. However, it also reveals an opportunity where organisations may not always have the scale, speed, or specialised expertise required to keep pace with evolving skills demands — particularly in areas like AI. This creates a compelling role for external providers to act as strategic partners in accelerating capability development.
While automation is already reshaping entry-level roles, the outlook in Singapore is more measured, over half of employers report that some roles have already been automated, with others expecting gradual change.
Compared to the US and UK, there is less urgency and anxiety, indicating greater confidence in the ability to adapt through reskilling and workforce transformation.
At the same time, organisations are expanding how they access talent. Nearly three-quarters of employers are outsourcing or planning to do so, reflecting a growing reliance on cross-border and flexible workforce models. This shift is also opening new pathways into tech careers, particularly for women, by enabling more adaptable career progression.
Together, these trends are reshaping early-career pathways in Singapore. Entry-level talent will increasingly need AI-adjacent and hybrid skill sets, while freelancers stand to benefit from rising demand for project-based and flexible work as organisations expand outsourced and distributed workforce models.
