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Singapore’s official surveys point to a practical gap: many firms are trying or planning AI, but fewer have integrated it into core processes. The reported obstacles include implementation costs and limited in-house expertise; smaller firms also cite strategy and trust. These are survey responses, not proof of why any particular company’s pilot stalls.
What does “stuck at the pilot stage” mean?
A pilot tests whether an AI tool can help with a bounded task. Integration goes further: the tool becomes part of a routine business process, with defined ownership, data access, staff responsibilities, and a way to monitor results. Trying a generative AI tool is therefore not the same as embedding AI in core operations.
That distinction matters when interpreting adoption figures. A company can report using AI without having redesigned a workflow or made the system part of day-to-day operations.
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In a 2026 survey of private-sector establishments with at least 10 employees, Singapore’s Ministry of Manpower (MOM) found that 71.5% had not adopted AI. Among all surveyed establishments, 7.4% were planning adoption, 6.0% were piloting AI, and 3.8% were integrating it into core processes. The stages indicate that fewer establishments reported core-process integration than planning or piloting; they do not show that every pilot failed or explain why any one firm did not advance. MOM’s 30 April 2026 release gives the survey results and population definition.
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Adoption also differed sharply by establishment size in that survey: 23.9% among establishments with fewer than 25 employees, compared with 76.4% among those with more than 500 employees. These are MOM establishment figures, not a count of all Singapore SMEs, and the 2026 survey covers establishments with at least 10 employees.
A separate measure from IMDA shows momentum among SMEs: AI adoption rose from 4.2% in 2023 to 14.5% in 2024. IMDA says the increase was driven mainly by off-the-shelf generative AI tools. That indicates more reported AI use, not necessarily integration into core workflows. IMDA’s Singapore Digital Economy Report 2025 reports the adoption trend.
IMDA’s 2025 pulse survey also found that AI-using SMEs used AI across an average of three business functions, compared with five among AI-using non-SMEs. IT, customer service, and finance and accounting were common areas. Breadth across functions is useful context, but it does not measure whether an individual pilot succeeded or became operational.
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Why the move from experiment to operations can be difficult
Implementation costs compete with uncertain scope
High implementation costs were the most commonly reported constraint in MOM’s 2026 survey, cited by 44.9% of firms. The figure identifies a reported obstacle; it does not establish that return on investment is the leading reason pilots stall. Costs can involve more than the initial tool: a business may need to prepare data, connect systems, check outputs, train staff, and assign someone to maintain the process.
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A pilot can be cheap to start yet expensive to operationalize if the production workflow requires custom integration, ongoing review, or safeguards the test did not include. Before expanding, leaders should estimate the full cost of the intended routine process, not extrapolate from a small trial.
Expertise is needed to make the tool fit the work
Lack of in-house expertise was cited by 42.4% of firms in the same MOM survey. A useful pilot needs people who can identify a suitable task, judge output quality, handle data appropriately, and decide how the work changes when AI is introduced. Without that capability, a promising demonstration may not translate into a dependable process.
MOM’s reported next steps varied by firm size. Among AI-adopting smaller firms, training (46.6%) and providing AI tools (41.1%) were prominent; larger firms more often reported developing governance frameworks (37.5%) and redesigning workflows (22.5%). These are reported actions, not evidence that one approach causes adoption. The MOM release describes these as shares of AI-adopting firms; consult the release for subgroup wording and denominator details.
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Among smaller firms, MOM recorded lack of strategy (32.4%) and low trust in AI (30.8%) as additional reported constraints. A pilot can expose these questions without resolving them: Which business problem is important enough to address? What level of error is acceptable? Who checks outputs, and what happens when the system is wrong?
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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These concerns are related but distinct. A clear business objective helps determine whether a tool is relevant; trust depends on evidence that its outputs are safe and dependable for the specific use.
Larger firms face integration and security challenges
For larger firms, integration complexity (56.1%) and data-security concerns (55.4%) were frequently reported constraints in MOM’s survey. A successful isolated test may still need to connect with existing systems and meet organizational requirements for data access and security before it can become routine.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What reported benefits can—and cannot—tell a business
MOM found that 70.7% of AI-using firms reported worker productivity improvements. The survey also recorded role redesign at 18.9%, creation of AI-related jobs at 13.9%, and headcount reduction at 6.2% after AI adoption. These are reported outcomes, not guaranteed effects or predictions for a particular company.
IMDA reported average cost savings of 52% in 2024 among SMEs using AI-enabled solutions under the Productivity Solutions Grant (PSG). That result is specific to the grant context and participating SMEs; it should not be treated as the expected return from a typical pilot. IMDA’s 2025 announcement of the report summarizes the findings.
Questions to answer before a pilot becomes business-as-usual
These are practical decision questions for a leadership team, not survey findings. They help distinguish a useful experiment from a process ready to scale:
- Business value: What specific task or bottleneck is the pilot meant to improve, and what baseline will you compare against?
- End-to-end cost: What will data preparation, integration, oversight, training, and ongoing maintenance cost once the process is in regular use?
- Data and security: What information will the system access, where will it be processed, and who is authorized to review or approve its use?
- Workflow ownership: Who is accountable for the process, who checks outputs, and how will staff handle errors or exceptions?
- Staff capability: What training or specialist support is needed for employees to use the system reliably?
- Scale criteria: What measurable result and risk threshold would justify expanding, revising, or stopping the pilot?
Writing down these answers makes it easier to decide whether the obstacle is the tool, the process around it, or the case for using AI at all.
Singapore support for moving beyond experimentation
In March 2026, IMDA described the National AI Impact Programme as supporting 10,000 enterprises over three years, with measures including curated, pre-approved solutions and grant support. Programme scope, eligibility, and terms can change, so confirm the current details directly with IMDA’s National AI Impact Programme page before relying on a particular form of support.
What the available evidence cannot establish
The official figures describe aggregate survey responses and adoption stages. They do not track individual Singapore SME pilots over time, identify which factor most often causes a particular pilot to be abandoned, or establish a universal causal explanation. The safest conclusion is narrower: reported cost, capability, strategy, trust, integration, and security constraints help explain why moving from experimentation to routine use can require substantial organizational work.
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