Singapore’s AI Adoption Gap: Large Firms Lead While Most Businesses Wait

A separate MOM breakdown reported adoption rates of 27.2% for firms with fewer than 200 employees, 54.8% for mid-sized firms with 200 to 500 employees, and 76.4% for firms with more than 500 employees. The pattern is consistent: the larger the company, the more likely it is to have the resources and organisational capacity to invest in AI and deploy it across operations.

This creates a potential capability gap. Larger companies can spread the cost of implementation across more teams, hire or develop specialist talent, and address governance and integration challenges at scale. Smaller firms may see the same potential benefits but have less room for experimentation and fewer internal resources to manage the transition.

Digital and knowledge-intensive sectors lead adoption

AI use is most advanced in sectors where work is already heavily digital and involves tasks such as software development, systems analysis, and data analytics. Adoption was highest in:

• Information and communications: 74.1%
• Professional services: 57.5%
• Financial and insurance services: 56.4%

These results suggest that sector readiness depends not only on access to digital infrastructure but also on whether firms have suitable use cases, digitally fluent employees, and business processes that can accommodate AI. The leading sectors are therefore not necessarily representative of the wider economy: their high adoption rates coexist with much lower adoption among smaller firms and less digitally intensive industries.

Early adopters report productivity gains

Among firms that had adopted AI, 70.7% reported improvements in worker productivity. Other reported benefits included better decision-making, cited by 13.3% of AI-using firms, and greater innovation, cited by 11.9%.

These figures show that businesses using AI are seeing practical benefits, but they should be interpreted carefully. They are reported outcomes from adopting firms, not a controlled estimate of AI’s causal effect on productivity across Singapore’s entire economy. The results indicate where firms are experiencing value; they do not show that every implementation will produce the same gains.

Cost and expertise are the biggest barriers

The leading obstacles to adoption were implementation cost, cited by 44.9% of firms, and insufficient in-house expertise, cited by 42.4%. Smaller firms also pointed to the absence of an AI strategy and low trust in AI as significant barriers.

The obstacles change somewhat with company size. Larger firms were more likely to face systems-integration complexity and data-security concerns, reflecting the difficulty of connecting AI tools to established systems and managing them across larger organisations.

This helps explain why digital readiness has not automatically translated into widespread operational adoption. The challenge is not simply whether firms can access AI tools. It is whether they can select appropriate use cases, pay for implementation, build internal capability, manage risk, and incorporate the technology into existing workflows.

AI is changing jobs more than eliminating them—for now

The survey’s early employment evidence points primarily to job and task redesign. Among AI-adopting firms, 18.9% reported redesigning job functions and 13.9% reported creating new AI-related jobs. By comparison, 6.2% reported AI-related reductions in headcount or hiring activity.

MOM therefore found no indication of significant or widespread AI-driven job displacement at this stage. The findings are more consistent with AI complementing workers and changing how work is performed than with a broad reduction in the number of jobs.

That is not a guarantee about the long-term effect of AI. It does suggest that the immediate workforce priority is helping employees adapt to changing tasks, acquire relevant skills, and move into new or redesigned roles as adoption expands.

Singapore’s firm-level adoption trails some digital leaders—but comparisons need care

Singapore’s relatively low firm-level adoption rate is notable given its reputation for digital competitiveness. The comparison with countries such as Denmark, Finland, and Sweden is directionally useful, but adoption figures should not be treated as perfectly interchangeable: surveys may use different definitions of AI, firm-size thresholds, and reference periods.

The broader lesson is that digital infrastructure and national readiness do not, by themselves, ensure that businesses will integrate AI into everyday operations. Organisational capability, workforce skills, cost, trust, data governance, and the availability of practical use cases all affect how quickly adoption spreads.

The central challenge is diffusion

MOM’s survey depicts a two-speed AI economy. A minority of larger firms and digitally intensive sectors are already reporting productivity benefits, while most businesses remain at the planning, piloting, or pre-adoption stage.

For Singapore, the next phase is therefore less about proving that AI can work and more about making responsible adoption achievable for a much broader set of firms. That means addressing implementation costs, developing in-house expertise, improving trust and governance, and preparing workers for redesigned roles.

The clearest conclusion from the survey is simple: Singapore’s AI transition has begun, but it has not yet reached most companies—and its first visible effect on work is transformation rather than widespread replacement.