Briefing: What Singapore’s SME AI adoption gap means for operators

IMDA reports that 23.4% of Singapore small and medium-sized enterprises used AI in 2025, compared with 70.4% of larger firms. For an SME deciding whether to buy an AI tool, the gap is a prompt to examine a specific workflow and its value. It does not measure the return that any one firm can expect.

The figures come from the Singapore Digital Economy Report 2026, but describe adoption in 2025. The SME rate rose from 14.5% in 2024. The report's issue year and a later update to its listing do not establish that it first became public on 5 October 2026. This Briefing treats the evidence as a durable operating question, not a fresh programme announcement.

What the gap can and cannot show

IMDA found that perceived lack of relevance at small-business scale was the leading barrier among firms that had not adopted AI. That helps explain why a broad sales pitch may fail: a small operator needs to see where a tool fits the work, what staff must check, and whether the benefit covers its cost. The survey does not establish that company size alone caused the adoption gap.

Adoption also varies by industry. Information and communications, professional services, and finance and insurance showed higher rates than retail trade, transportation and storage, construction, and manufacturing. Those comparisons can help a provider decide which workflows to investigate, but a sector average is not a buying recommendation for a particular shop or supplier.

A separate Enterprise Singapore and IMDA Retail Industry Digital Plan, announced in May 2026, offers a practical example of function-first guidance for SME retailers. It organises solutions around business functions rather than expecting every retailer to choose from the same technology menu. That plan is context for how an operator might frame a decision; it is not evidence that the firms in IMDA's adoption survey gained a particular return from AI.

The retail plan makes the mechanism more concrete. It separates front-of-house customer work, back-of-house operations and corporate functions. Its examples include customer engagement, demand forecasting for inventory, marketing content and digital training. These are different jobs with different data, staff and review needs. A retailer considering demand forecasting would have to inspect stock and sales records; one considering customer responses would have to assess service quality and escalation. The plan does not say that each tool is appropriate for every retailer.

Enterprise Singapore and IMDA also described a cybersecurity and data-protection roadmap alongside the retail plan, as well as the need for job redesign and worker skills. That is relevant to the SME adoption gap because a tool purchase is only one part of an operating change. A small firm must know what information the tool can use, which decisions remain with staff and who can intervene when the output is wrong. The fact sheet is guidance for retail operators, not an independent measurement of the national AI survey's business returns.

Test a process before buying

An SME can choose one repeated task, such as stock reconciliation or customer-query handling, and record its current time, error rate, staff effort and review controls. A limited trial can compare those measures with the same task after introducing a tool. The owner should also decide who can see customer or business data and who is accountable for checking the output. These are proposed decision steps, not outcomes measured by the IMDA report.

For an inventory trial, the owner could begin with a fixed set of products and a comparable period of sales. Record stock discrepancies, time spent reconciling them and any lost sales before introducing forecasting. In the trial, keep a person responsible for checking unusual recommendations, document overrides and review whether the result is useful after enough trading days to cover normal variation. A customer-query trial would need different measures: resolution time, accuracy, escalation and customer complaints. Neither example promises savings; each turns a broad adoption statistic into a falsifiable local test.

A supplier pitching an AI system should state which existing records and integrations are needed, how much setup work falls on the customer, what a failure looks like and who owns correction. It should show a price and review point matched to the business function. If the SME lacks usable data or staff time to supervise the process, deferring purchase may be rational even when national adoption rises. Conversely, a narrow trial that reliably solves an expensive recurring task can justify expansion without waiting for every peer to adopt. These are decision criteria, not conclusions drawn by IMDA about any named company.

Among firms already using AI, 87.6% reported productivity and process improvement as a benefit in 2025. That is the share of AI adopters reporting a type of benefit, not an independently measured 87.6% productivity gain and not a share of all SMEs. A buyer should ask a supplier for a task-level baseline and a clear review date instead of treating that survey response as a performance promise.

At the review date, the buyer can compare the trial with its original baseline and decide whether to stop, adjust or expand. A result that depends on constant manual correction may be less valuable than a slower but dependable process. The decision should include staff time, data access, service quality and recurring cost as well as the tool price. This keeps the trial tied to an operating need rather than a national adoption benchmark.

For a B2B provider, the opportunity is to show a narrow use case, implementation effort and evidence of value at a scale the customer can manage. For the SME, the decision is whether one trial improves a real process enough to justify further investment. The national adoption gap identifies a question to investigate; it does not settle that decision.

Source: IMDA Singapore Digital Economy Report 2026; Enterprise Singapore and IMDA retail digital plan factsheet.