SUPERINTELLIGENCE SG .AIA closer look at how Singapore's public AI agenda is being translated into enterprise workflows, practical skills and accountable deployment.
The public record is moving beyond “who has access to an AI tool?” and toward “which work changes, who can change it, and what support makes the change durable?” This note follows that shift across enterprise data, national programmes and applied experiments.
Singapore's adoption layer has three connected parts: measure where firms are using AI, build the capabilities to redesign work, and give organizations a practical route from a problem statement to a tested system.
These parts do not make adoption automatic. They make the path more visible. The open question is whether the resulting systems improve work for the people who use and are affected by them.
Each row names a public signal, the structure it reveals and the question that remains open.
IMDA reports that 14.5% of SMEs adopted AI in 2024, up from 4.2% in 2023; among non-SMEs, adoption rose from 44% to 62.5%. The same report describes firms using AI across several business functions, from IT and customer service to finance and accounting.
Read IMDA's adoption evidence ↗Why it mattersA headline adoption rate does not show whether AI is a pilot, a workflow layer or a system that changes accountability.
IMDA's National AI Impact Programme aims to support 10,000 enterprises over three years and 100,000 workers to become AI-bilingual. Its mechanisms pair enterprise support with leadership training, pre-approved AI solutions and domain-specific fluency programmes.
Read the NAIIP factsheet ↗Why it mattersThe target is not only tool distribution. It links implementation to the people who must redesign and govern the work.
AI Singapore's 100 Experiments programme asks whether a proposed solution creates substantial value for an organization and its stakeholders, and whether the organization has the Singapore-based technical team needed to work on it.
Read the 100E application criteria ↗Why it mattersA problem-led route helps separate a useful deployment from a generic demonstration. It also makes the organization's own capacity part of the design.
IMDA's TeSA work under NAIIP describes AI fluency for tech and non-tech workers, with the aim of combining domain expertise and practical AI skills so people can transform role-specific workflows.
Read IMDA's NAIIP skills pathway ↗Why it matters“AI-ready” is more useful when it names the work a person can do, the judgment they retain and the risks they can recognize.
Public targets and programme descriptions show direction, not outcomes. They do not yet tell us which workflows changed well, which workers gained or lost agency, or how results differ by sector and organization size. Those are the next questions for the Navigator and the Commons to document.