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Singapore’s Agentic AI Adoption Doubled, but Workflow Redesign Lags
Singapore’s agentic AI adoption rose from 22% in 2025 to 51% in 2026, but just 10% of enterprises redesigned processes for AI to complete work end to end. Singapore’s AI maturity score rebounded to 53 out of 100 in 2026, above the global average of 51, while AI spending reached 15.4% of IT budgets.
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Key takeaways
- Singapore’s agentic AI adoption rose from 22% in 2025 to 51% in 2026, but just 10% of enterprises redesigned processes for AI to complete work end to end.
- Singapore’s AI maturity score rebounded to 53 out of 100 in 2026, above the global average of 51, while AI spending reached 15.4% of IT budgets.
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openai.comCreate a landscape editorial hero image for this Studio Global article: What does ServiceNow’s 2026 Enterprise AI Maturity Index, based on a survey of 4,500 senior leaders across 19 countries including 200 in Sin. Article summary: ServiceNow’s findings show a sharp rise in Singapore’s agentic AI adoption, but a much smaller shift in how businesses actually operate: adoption increased from 22% in 2025 to 51% in 2026, while only 10% of enterprises h. Topic tags: general, general web, user generated. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts with fa
ServiceNow’s 2026 Enterprise AI Maturity Index captures two different stages of Singapore’s enterprise AI push: adoption is accelerating, but workflow transformation is not keeping pace. Agentic AI use rose from 22% in 2025 to 51% in 2026; only 10% of enterprises had redesigned processes so AI could complete multi-step work end to end. 26
3 The distinction matters: adopting an AI tool does not, by itself, change how a business operates.
Adoption is spreading faster than process change
The index surveyed 4,500 senior leaders across 19 countries, including 200 in Singapore. 26 In Singapore, 33% of enterprises were using AI mainly to assist employees with day-to-day tasks, while 18% had made no progress on advanced AI adoption.
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Taken together, those figures show why a headline adoption rate can overstate how deeply AI is embedded in work. Many organisations are using AI to support individual tasks; far fewer have redesigned complete workflows around it. The 10% figure refers specifically to processes where AI can complete multi-step business tasks end to end. 3
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Maturity has rebounded, with gaps between dimensions
Singapore’s AI maturity score fell from 45 out of 100 in 2024 to 34 in 2025, then rebounded to 53 in 2026. That put it above the 2026 global average of 51. 5
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The improvement is not uniform. Singapore scored above global averages on six of the seven dimensions, with talent readiness its widest lead, but lagged on AI-enabled workflows—the dimension associated with putting AI into operational processes. 20
37 The index’s overall score therefore sits alongside a more specific weakness: turning AI capability into redesigned work.
There are signs of investment in foundations. In Singapore, 23% of enterprises had replaced legacy technology with modern platforms, compared with 16% globally. Formal processes for testing, auditing and managing AI risk were reported by 28%, versus 20% globally. 19 Those measures indicate progress in systems and governance, but they are not the same as end-to-end workflow redesign.
Spending is rising, but spending alone is not the outcome
Singapore organisations allocated 15.4% of their IT budgets to AI, close to the global share of 15.8%. AI spending in Singapore was up 108% year on year, and the reported projection was for AI to reach about 20% of IT budgets by 2027. 13
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The spending figures show commitment; they do not establish that investments are producing productivity gains. CK Tan of ServiceNow describes the contrast between faster agentic AI adoption and slower operational redesign as Singapore’s “AI adoption paradox.” 5 The index data support a practical way to assess progress: look beyond how many tools are in use and ask whether complete workflows, responsibilities and outcomes have changed.
What enterprises can do to close the execution gap
The findings point to several practical priorities. These are implications of the adoption–transformation gap, not verified quotations from every executive named in the original research question.
- Start with an end-to-end process. Define the business outcome, map the existing workflow and identify where AI can take action, where people need to review decisions, and how exceptions are handled.
- Design around employees’ work. Involve the people who carry out the process, clarify responsibilities and escalation paths, and identify what training or support a changed workflow requires.
- Connect data and systems across the process. AI working on isolated tools or fragmented information may not be able to support a multi-step workflow. Treat reliable, appropriately permissioned data and system integration as part of the redesign.
- Build governance into deployment. Establish ownership, testing, auditability and controls for privacy and security before expanding AI’s ability to act.
- Manage investment as a portfolio. Prioritise projects against measurable outcomes, cost and risk; account for supporting infrastructure and organisational change; and use results to decide whether to scale, revise or stop a project.
These steps shift the measure of success from AI usage or budget share to whether the work itself has improved—and whether that improvement can be demonstrated.
What the available evidence does not establish
The supplied Singapore findings do not provide enough detail to quantify privacy and security concerns or determine whether they persisted year over year. They also do not substantiate a quantified productivity benefit from AI-led process redesign, or the details and expected gains of the Standard Chartered onboarding example. Those outcomes should not be treated as established from the evidence cited here.
The available excerpts identify CK Tan’s adoption-paradox framing, but do not give enough detail to reliably attribute specific recommendations or findings to Melinda McKinley, StarHub’s Tan Kit Yong or Councilio’s Peter Carr. 5 The execution priorities above are therefore presented as analysis of the reported adoption and workflow figures, not as verified statements from those individuals.
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Singapore’s agentic AI adoption rose from 22% in 2025 to 51% in 2026, but just 10% of enterprises redesigned processes for AI to complete work end to end.
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Singapore’s agentic AI adoption rose from 22% in 2025 to 51% in 2026, but just 10% of enterprises redesigned processes for AI to complete work end to end. Singapore’s AI maturity score rebounded to 53 out of 100 in 2026, above the global average of 51, while AI spending reached 15.4% of IT budgets.
Sources
newsroom.servicenow.comSingapore agentic AI adoption doubled in 2026, with 10% of ...
sbr.com.sgBusinesses double agentic AI adoption, but few transform ...
marketscreener.comServiceNow : Singapore agentic AI adoption doubled in 2026 ...
theedgesingapore.comOnly 1 in 10 Singapore firms has redesigned work around AI