Singapore’s office workers are among the least sceptical about artificial intelligence (AI) globally, but companies are failing to convert that openness into regular workplace use, according to new research from Salesforce.
The study, conducted with YouGov, found that only 6 per cent of the island nation’s desk workers use AI as a core part of their daily work. That places Singapore below the global average of 11 per cent, despite workers in the city-state showing less resistance to the technology than peers in several major markets.
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Only 29 per cent of Singapore respondents identified as AI sceptics, compared with a global average of 37 per cent. The figure was also substantially lower than the 53 per cent recorded across the US, UK and France.
The findings point to a familiar problem in enterprise technology: workers may be willing to use AI, but poorly designed corporate deployments are limiting adoption. For Singapore, which has positioned itself as Southeast Asia’s hub for AI governance, enterprise technology and regional headquarters, the gap matters. If companies cannot turn pilots into practical tools, the country’s policy and infrastructure advantages may not translate into productivity gains.
A willingness gap, not a trust crisis
The Salesforce survey covered more than 1,500 desk workers across markets including Singapore, Australia, India, Japan, France, Germany, the UK, the US, Canada, and Saudi Arabia. Respondents were defined as workers whose roles are primarily based on mental rather than manual labour, and who had at least minimal familiarity with AI.
Among Singapore workers who experienced unsuccessful AI pilots, 40 per cent cited generic outputs as a reason for failure. That was the highest proportion among all markets surveyed and ten percentage points above the global average. Another 38 per cent pointed to low trust in outputs, compared with 28 per cent globally, while 30 per cent said results lacked business context.
Taken together, the data suggests that the issue is less about fear of AI replacing jobs and more about whether the tools are useful enough to become part of everyday work. Generic chatbots and loosely integrated assistants may generate early curiosity, but they rarely survive contact with specialised workflows, compliance requirements and internal data structures.
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“Singapore workers are not standing in the way of AI; they’re waiting for AI that works for them,” said Paul Carvouni, SVP and GM for ASEAN at Salesforce. “Leaders have to move past generic tools and use AI that is trusted, grounded in business context and built into daily work.”
The quote captures Salesforce’s commercial argument, but the broader point extends beyond one vendor. Enterprise AI adoption is moving from experimentation to implementation, and the companies that struggle are often those that treat AI as a standalone productivity layer rather than a capability embedded into sales, customer service, finance, human resources and operations systems.
Southeast Asia’s enterprise AI testbed
Singapore’s adoption paradox is particularly relevant for Southeast Asia because many regional AI decisions are made from the city-state. Multinationals often base ASEAN leadership, procurement and digital transformation teams in Singapore, while regional startups and scaleups use it as a launchpad for enterprise sales.
If Singapore companies cannot make AI work inside mature corporate environments, the challenge will be greater in neighbouring markets where digital infrastructure, data readiness and enterprise software penetration vary more widely.
Across the region, interest in AI has grown alongside the region’s digital economy. Google, Temasek and Bain estimated Southeast Asia’s internet economy at US$263 billion in gross merchandise value in 2024, with digital financial services, e-commerce, online media and travel continuing to drive technology adoption. AI is increasingly being layered into these sectors, from customer support automation in Indonesia to fraud detection in fintech and logistics optimisation across cross-border supply chains.
Yet the region remains uneven. Singapore has invested heavily in AI governance, including frameworks such as AI Verify and model governance initiatives, while markets such as Indonesia, Malaysia, Thailand, Vietnam and the Philippines are still balancing AI adoption with data protection, localisation concerns and skills gaps. For companies operating across ASEAN, the practical question is not whether employees are curious about AI, but whether AI systems can handle multilingual markets, fragmented data and sector-specific regulation.
This is where Salesforce’s findings carry regional significance. If workers reject AI because outputs are generic or unreliable, adoption will stall even in receptive markets. The same risk applies to banks testing AI copilots, retailers deploying automated service agents, and logistics firms using predictive planning tools.
The vendor race moves into workflows
Salesforce is not alone in trying to frame the next phase of AI adoption around enterprise workflows. Microsoft has pushed Copilot across Office, Dynamics and Azure. Google is embedding Gemini into Workspace and cloud products. ServiceNow is pitching AI for enterprise service management, while SAP and Oracle are adding AI functions into core business applications.
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In Asia, Zoho, and Freshworks remain relevant for small and mid-sized businesses, particularly in customer engagement and support functions. Regional systems integrators and cloud partners also play a major role, especially for companies that need AI tools customised around local languages, legacy systems and compliance requirements.
The competition is shifting from who has the most advanced model to who can make AI useful inside existing processes. That requires access to clean enterprise data, clear permissioning, auditability, and training that reflects specific job roles. Salesforce said its research identified more than 500 workers globally who had moved from initial AI pilots to deep daily usage. Their common denominator was not enthusiasm, but support structures: role-specific training, embedded workflows and strong data security.
That aligns with broader workplace data. Microsoft and LinkedIn’s 2024 Work Trend Index found that 75 per cent of knowledge workers globally were already using AI at work, while many were bringing their own tools rather than relying on employer-provided systems. For companies, that creates a governance problem. If official tools are poor, employees may turn to unsanctioned alternatives, increasing risks around data leakage, compliance and inconsistent outputs.
From pilots to productivity
The Salesforce findings should be read with some caution. The research is vendor-sponsored and naturally supports a case for enterprise AI platforms that are integrated with business data. Still, the underlying challenge is real: many companies have launched AI pilots without a clear workflow owner, measurable productivity target or data strategy.
For Singapore, the next phase of AI adoption will depend less on proof-of-concept announcements and more on operational discipline. Companies will need to decide which tasks should be automated, which require human review, and how AI outputs should be monitored. They will also need to invest in training managers, not just frontline staff, because adoption often fails when middle management cannot translate technology into process change.
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The risk is that companies misread low scepticism as a guarantee of adoption. Workers may be open to AI, but they will not use tools that slow them down, produce unreliable answers or sit outside the systems where work already happens.
Singapore has the policy environment, digital infrastructure and corporate base to become a serious enterprise AI market for Southeast Asia. Salesforce’s data suggests the bottleneck is now execution. The goodwill is there. The daily habit is not.
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