Tech roles within non-tech industries in Singapore are growing three to four times faster than those in the traditional tech sector [1].
This shift indicates that digital transformation is no longer confined to software companies or hardware manufacturers. Instead, the demand for technical expertise is migrating into traditional business domains, forcing a rethink of how the workforce is trained to handle artificial intelligence.
The head of Singapore's digital watchdog, the Infocomm Media Development Authority (IMDA), said that AI-bilingual skills have become an urgent priority for companies [1]. This refers to the ability of employees to possess both deep domain expertise in their specific industry and the technical proficiency to implement AI tools.
According to the IMDA, the growth rate of tech positions in non-tech sectors is three to four times faster than in the tech sector itself [1]. This trend suggests that the most aggressive hiring for digital roles is currently happening in fields such as finance, healthcare, and logistics, rather than within the tech industry's own borders.
Companies are now seeking a fusion of skills to keep pace with this growth [1]. The agency said that the ability to bridge the gap between business operations and AI application is essential for maintaining competitiveness in the current market.
As these roles expand, the pressure on the education system and corporate training programs to produce "bilingual" workers increases. The IMDA's findings highlight a structural change in the economy where technical literacy is becoming a baseline requirement for leadership and operational roles across all sectors [1].
“Tech roles in non‑tech industries are growing three to four times faster than those in the tech sector”
The rapid expansion of tech roles in non-tech sectors signals a transition from 'digital transformation' as a project to 'digital fluency' as a core business requirement. By prioritizing AI-bilingualism, Singapore is attempting to avoid a talent bottleneck where technical experts lack business context and business leaders lack technical understanding, which could otherwise stall the adoption of AI in critical infrastructure and services.
