This research was conducted in partnership with YouGov, who surveyed 1,073 white collar workers based in Singapore. Fieldwork ran from September 9-14, 2025.
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Bottom Line Up Front
Summary: Singapore workers are ready to innovate further with AI, but today’s tools still create a lot of friction, creating generic outputs, copy-paste workflows and assistants that answer rather than finish work. We explore the frustrations shared across current use cases and generational divides.
Solution: Embed agentic, context‑aware AI directly into existing systems so it executes multi‑step workflows and delivers finished work, reducing rework and returning time to higher‑value tasks.
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AI helps…and yet it currently makes more work.
AI adoption and spend is rapidly increasing, with IDC forecasting that AI and GenAI investments in Asia Pacific will reach $175 billion by 2028. However, this substantial investment isn't achieving its full potential because current tools and workflows fall short of expected productivity gains.
Singapore’s white-collar workforce reports that current AI tools are only modestly effective in day-to-day work, with half (49%) saying they meet needs “somewhat well,” and a full 70% experiencing frustrations.
The top pain points are generic outputs (35%), copy‑paste between tools (28%), and tools that only answer questions without taking action (23%).
The top five frustrations with current AI tools reported by white-collar workers, with “generic outputs that lack quality” leading the way.
1 in 5 respondents (20%) cite poor or lack of integration with existing software, which fragments workflows. As a result, more time is being allocated to administrative tasks, which still require manual effort.
Today’s AI sits in the workflow but not in the work. The burden of context setting, editing and tool-switching show that Singaporean teams aren’t getting finished outputs, so productivity gains are stalling. The path forward is agentic, integrated AI that operates inside existing systems, uses company context to raise output quality and automates multi-step tasks so time shifts from admin to strategy and outcomes.
Younger workers are the early engine of AI-driven productivity, but their momentum is hitting quality and workflow limits.