The artificial intelligence (AI) adoption in Singapore remains limited and at an early stage, its Ministry of Manpower said in a recent report.
According to the report, a majority of firms (71.5 percent) in the city state have yet to adopt AI.
Among the 28.5 percent that have started, meaningful integration remains limited – only a small share (3.8 percent) integrating AI into core processes as most remain at planning (7.4 percent) or piloting (6 percent) stages.
Meanwhile, AI adoption is widespread among larger firms. Larger firms demonstrate higher adoption rates and deeper levels of integration compared to smaller firms.
Adoption rises from 23.9 percent among firms with fewer than 25 employees to 76.4 percent among firms with more than 500 employees.
Larger firms are also more likely to possess the digital infrastructure and organizational capabilities needed to integrate AI into existing workflows.
Digitally intensive and knowledge-based sectors are more progressive in AI adoption, said the report.
AI adoption is highest in digitally intensive and knowledge-based sectors such as information and communications (74.1 percent), professional services (57.5 percent) and financial and insurance services (56.4 percent).
Structural barriers continue to constrain uptake. Firms face persistent constraints in adopting AI.
High implementation costs (44.9 percent) and lack of in-house expertise (42.4%) are commonly cited barriers.
Smaller firms also face organizational challenges such as lack of strategy (32.4 percent) and low trust in AI (30.8 percent), while larger firms face integration complexity (56.1 percent) and data security concerns (55.4 percent).
The report also showed that AI is augmenting but not replacing labor.
There is little evidence of widespread displacement. Only 6.2 percent of firms reported reduced headcount.
Instead, firms are redesigning roles (18.9 percent) and creating new AI-related jobs (13.9 percent), indicating that AI is primarily transforming tasks rather than replacing roles.
Productivity gains are already evident among adopters. Among firms using AI, 70.7 percent report improvements in worker productivity, alongside gains in decision-making (13.3 percent) and innovation (11.9 percent), suggesting that benefits are material even at early stages of adoption.
The report also highlighted that firms are taking steps to build capabilities for AI adoption and support workforce adaptation, and there are early signs of capability-building to support AI adoption within firms.
Smaller firms are focused on foundational steps such as training (46.6%) and provision of AI tools (41.1 percent), while larger firms are moving towards more structured approaches, including governance frameworks (37.5 percent) and workflow redesign (22.5 percent).
These patterns suggest that firms are beginning to adapt work processes and roles alongside AI adoption, with workforce adjustments currently concentrated within firms rather than across firms or sectors.
The Manpower Research and Statistics Department (MRSD) conducted the establishment-based survey covering 2,560 private sector establishments employing 486,600 workers, with fieldwork conducted between January and March 2026.
“The findings show that AI adoption is still at an early stage of diffusion. A majority of firms have yet to adopt AI, and among adopters, most remain at preliminary stages such as piloting or planning stage, with only a small proportion achieving integration into core business processes,” said the report.
It noted that adoption is also uneven across firm sizes and sectors, with larger firms and digitally intensive industries leading.
At the same time, early evidence suggests that AI is complementing rather than displacing labor.
Firms report productivity gains and improvements in work processes, with impacts primarily taking the form of job role redesign rather than workforce reductions.
“Taken together, the findings suggest that while AI adoption is gaining traction, it has yet to become broad-based,
“The next phase of adoption will depend on how firms overcome structural constraints and translate early experimentation into sustained, large-scale deployment,” it said.
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