Singapore AI Adoption Statistics 2026: Enterprise and Workplace Use
Singapore AI adoption statistics 2026 at a glance
Singapore AI adoption statistics 2026 show that 23.5% of enterprises had adopted AI in 2025, up from 14.7% in 2024, according to the Infocomm Media Development Authority (IMDA). A separate worker measure in the same report found that 85.7% of surveyed workers used AI tools at work in 2026. The enterprise figure describes organisations and the worker figure describes respondents. They come from different samples and reference periods.
IMDA’s enterprise breakdown gives 23.4% adoption for small and medium-sized enterprises (SMEs) and 70.4% for non-SMEs in 2025. The difference is useful when assessing which peers to compare a business with. An overall enterprise figure close to the SME rate does not mean larger businesses are behaving in the same way.
How many enterprises in Singapore have adopted AI?
The Singapore Digital Economy Report 2026 puts enterprise adoption at 23.5% in 2025, an increase of 8.8 percentage points from 14.7%. The report tracks AI as one area of enterprise digitalisation. A company may already use cloud services or data analytics and still be deciding whether it needs AI; broader digital adoption figures do not say how many businesses use AI.
SME adoption rose from 4.2% in 2023 to 14.5% in 2024 and 23.4% in 2025. Over the same three points, non-SME adoption was 44.0%, 62.5% and 70.4%. These sequences show that the groups started in different places and grew at different speeds. The 2025 gap was 47.0 percentage points. Compare each group with its own previous result if your question is how quickly it moved.
The distinction affects practical decisions. A small business adding an AI tool for customer enquiries can benchmark itself against other SMEs, but it should still ask whether the tool suits its languages, customer volume and staff capacity. A large business evaluating a wider deployment may already have data and procurement teams. The non-SME rate can provide market context for that decision without telling the company which tool to buy.
What Singapore’s worker survey says
In the report’s worker survey, 85.7% of respondents reported using AI tools at work in 2026, compared with 77.6% in 2025. The current total combines 84.8% who used AI in daily life and at work with 0.9% who used it only at work. The report also shows 6.5% using AI only in daily life and 7.8% never using it. These figures apply to the surveyed workers.
That distribution highlights the crossover between home and work. An employee who has used an assistant to organise a personal task may have enough familiarity to try it at work. Whether their employer authorises that use, provides training or tracks results is a question for their organisation. The survey percentages describe what workers report doing. Their popularity offers no shortcut around reviewing an individual tool.
When deciding what to measure in a staff survey, ask about purpose and frequency separately. “Have you tried AI for work?” and “Do you use it every week?” can produce very different results. Ask whether a feature is embedded in employer software or used through a personal account. That distinction can matter as much as the headline share.
Why the worker rate and business rate can both be true
A worker-level survey counts people. An enterprise survey counts organisations. A large enterprise where AI is used by many employees is still one enterprise. Thousands of small businesses with little AI adoption can dominate a count of firms while accounting for a different share of jobs. The surveys also refer to different dates and can ask different questions, so a direct subtraction does not show a gap in one consistent measure.
Say a large employer rolls out an assistant to several teams while dozens of small firms in the same area do little with AI. The employee and enterprise surveys would reflect different units, even if fieldwork covered the same dates. The national figures are valuable together because they point to different aspects of adoption. They should be separately labelled in any report.
What we can say about the general public
The worker survey separates daily-life and work use among the people it surveyed. It does not represent the share of everyone in Singapore who used generative AI, including people outside employment. A public-use estimate would need its own survey, with a stated age range, reference period and definition of AI. The IMDA business and worker figures can be cited without inventing a public estimate.
For consumer research, look for questions about use away from work, who is included and whether the survey counts built-in AI features. A survey recruiting people already familiar with AI could be useful for testing a feature but would not show what fraction of the country has tried it.
Programme targets and adoption statistics
IMDA’s National AI Impact Programme announcement describes support for 10,000 enterprises over three years. A programme target tells you what organisers intend to support. Reaching the target would still be separate from measuring adoption across the economy. For reporting, keep the target alongside its stated timeframe, and use the Digital Economy Report for measured enterprise adoption.
A UK technology supplier assessing Singapore can use these sources to prepare questions for local partners. Ask which processes are already digital, where teams see a need and how a pilot would be assessed. If the local team uses an assistant for document drafting, ask whether there is a reporting or review process before recommending a wider deployment. An enterprise rate is most helpful when it leads to that kind of specific enquiry.
Comparing Singapore with other markets
The international AI adoption hub links to guides that explain their country-specific methods. Compare Singapore’s enterprise measures with the coverage of the France guide before putting both on a chart. France Num focuses on very small businesses and SMEs, so its result and Singapore’s all-enterprise figure may not describe equivalent populations. For related reading, visit the What Is ChatGPT? homepage.
What workers use AI for
IMDA’s report offers more detail than the overall workplace rate. Among AI-using workers, 76.7% reported at least one content-creation use case and 63.9% reported a use case in process automation. At the level of individual tasks, 47.0% reported generating or editing written content, 38.5% document processing or data extraction and 36.1% desk research or information gathering. The categories can overlap, and each number applies to the AI-using group in the report.
These figures make a distinction that matters for organisations choosing what to test. Writing assistance can help one person prepare a draft, with review still taking place in the same team. Automating a document step can affect several roles and depend on how reliably the system reads forms. A report that combines both under “using AI” may hide the different effort needed to set up, supervise and evaluate those activities.
The report separately asks workers about perceived benefits. Reported improvements tell you what people felt changed. To assess actual productivity, a team would need a baseline and should count the time spent reviewing outputs and fixing errors. Decide what constitutes acceptable quality before measuring speed. If staff finish first drafts faster but reviewers spend more time correcting them, the final result can look different from the time saved at the drafting stage.
Which figure belongs in a business presentation?
For an audience of small-business owners, put 23.4% next to the SME label and include 2025 on the slide. Keep the 70.4% non-SME figure in a separate column. If the audience is deciding what to teach staff, show the work-task results and state that the denominator is AI-using workers. That steers the conversation towards relevant tasks instead of implying the firm is behind a national average.
If your organisation already collects technology metrics, choose one use-case question and retain it across review periods. It could ask whether staff used an approved assistant for research in the previous month. The narrower question makes the answer easier to interpret and gives you a way to track change independently of IMDA’s national research.
For a quick fact check, write the year beside every result before you combine them in a summary. A sentence about 2025 enterprise adoption and 2026 worker behaviour can be accurate if each time period is visible. A single label such as “Singapore AI adoption in 2026” would obscure the different dates.
Frequently asked questions
Is 85.7% the percentage of all Singapore residents who use AI?
It is the rate of AI use at work among workers surveyed for the report in 2026. It should not be extended to all residents.
When did 23.4% of SMEs adopt AI?
The 23.4% statistic refers to 2025. It was published in the 2026 edition of the Singapore Digital Economy Report.
Why is the overall enterprise rate so close to the SME rate?
The overall figure reflects the composition of the enterprise population. A simple average of the SME and non-SME rates would give the two groups equal weight and would not reproduce the report.
Are the figures specifically about ChatGPT?
The published rates concern AI tools generally within their survey definitions. They are not a measure of ChatGPT usage or paid subscriptions.
