AI adoption rises as workforce readiness lags in Singapore, Malaysia 
image showing text of Singapore Artificial Intelligence with a night time background and glowing lights

AI use is rising fast in Singapore and Malaysia, but workforce readiness is lagging and could shape which organisations see gains in 2026, workforce intelligence company Epitome Global said.

The firm’s aggregated skills assessments across both markets from 2023 to 2025 found that only about 1 in 5 professionals consistently show behaviors it associates with AI readiness. The study cited persistence, curiosity, and reflective learning among those behaviors.

More than 70% of participants reported advanced digital literacy, but gaps remain in higher-order skills. About 56% rated themselves at a basic level in decision-making, and around 42% reported only basic confidence in computational thinking, a skill that is increasingly required to supervise AI tools.

“AI tools are scaling faster than workforce readiness,” said Kevin Chan, CEO of Epitome Global. “The differentiator will not be access to technology, but clarity around what people can actually do.”

Despite strong uptake, 65% of organisations in Singapore remain focused on basic AI use cases, signaling limits in scaling and embedding AI into the actual and core workflows.

The research outlined workplace trends that are likely to shape organizations in 2026. These include disengagement and skills decay as productivity risks, integration gaps following rapid adoption, intensifying fire-and-hire cycles as firms rebalance skills, and senior employability becoming more strategic in AI-driven organisations.

Shift toward higher-value technical roles

Epitome Global also reported talent competition intensifying across parts of Asia as markets move beyond traditional outsourcing roles and compete for the global talent market. 

The Philippines, for instance, is expanding higher-value digital and knowledge work, while Vietnam is strengthening engineering and product development, and India is increasing focus on AI engineering and data science.

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