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Singapore workers among world’s least AI-sceptical, yet lowest in daily workplace adoption

Singapore workers among world's least AI-sceptical, yet lowest in daily workplace adoption

Just 6% of Singapore workers use AI as a core part of their day-to-day work, with flawed corporate rollouts to blame.

SINGAPORE – July 8, 2026  — Salesforce, the world’s #1 AI CRM, today unveiled new research revealing Singapore’s desk workers are among the least sceptical about AI globally, yet rank the lowest for adoption. The gap points to a specific, solvable problem: corporate rollouts that fall short, leaving workers unable to make the most out of AI in their day-to-day work.

The survey of more than 1,500 desk workers spanning four continents found that just 29% of Singapore respondents identify as AI sceptics — well below the global average of 37%, and significantly lower than the 53% recorded in the United States, United Kingdom, and France. Yet, this has failed to translate for Singapore businesses as a competitive advantage. Only 6% of Singapore desk workers say AI is a core part of their daily work, placing the market among the lowest globally and nearly half the global average of 11%. The data reveals an adoption paradox: willingness without traction.

Where Corporate Rollouts are Stalling

This gap between attitude and action is explained not by reluctance but by disappointment.  Among Singapore workers who have experienced unsuccessful AI pilots (31%), the reasons point squarely at pilot quality. Of those Singapore respondents,  40% cited generic outputs as a reason for failure, the highest proportion of any market surveyed and ten percentage points above the global average figure of 30%. 38% of them flagged low trust in outputs compared with a global average of 28%. 30% also said results lacked business context, against a global average of 22%. The results show a consistent pattern: Singapore’s workers are not resistant to AI in principle, but they are being held back by tools that do not meet the bar of relevance, accuracy, and reliability required for professional use.

From Experimentation to Execution

The Salesforce research also identified more than 500 workers globally who successfully graduated from initial pilots to deep, daily usage. What differentiated them was not enthusiasm — it was the ecosystem built around the tools: role-specific training, AI embedded into existing workflows, and non-negotiable data security. For Singapore business leaders, the data makes a compelling case: the barrier to AI adoption is not cultural reluctance but a delivery gap. Corporate rollouts that are disconnected from the business context and existing workflows, will continue to fail regardless of how open workers are to the technology. Closing the gap requires moving from experimentation to execution and advancing contextual, trustworthy AI experiences that Singapore’s workforce has already signalled it is ready for.

“Singapore workers are not standing in the way of AI – they’re waiting for AI that works for them. While workers’ enthusiasm towards AI is a head start, poor pilots are leaving real business potential on the table. Every organisation needs to become an Agentic Enterprise to remain competitive, grow and capture opportunities in this era. However, leaders have to move past generic tools and use AI that is trusted, grounded in business context and built into daily work,” said Paul Carvouni, SVP & GM, ASEAN, Salesforce. “Do that, and adoption will not just follow. It becomes a competitive advantage for Singapore and the region.”


Methodology

In partnership with YouGov, Salesforce conducted a double-blind online survey among over 1,500 desk workers — defined as workers who consider their day-to-day job primarily mental labour over manual or task-based labour and were required to have at least minimal familiarity with AI — in Australia, India, Japan, Singapore, France, Germany, Italy, Netherlands, Spain, Saudi Arabia, United Kingdom, Mexico, United States, and Canada. The survey was conducted from December 2025 to January 2026. The sample is representative across job roles, industries, and business size.