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Singapore’s AI Push Hits a Real-Time Data Bottleneck
In Singapore, 75% of IT leaders say their organisations are deploying or piloting agentic AI, but 78% say inadequate real time data infrastructure is stalling AI scaling.
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Key takeaways
- In Singapore, 75% of IT leaders say their organisations are deploying or piloting agentic AI, but 78% say inadequate real time data infrastructure is stalling AI scaling.
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openai.comCreate a landscape editorial hero image for this Studio Global article: What does Confluent’s 2026 Data Streaming Report, based on a survey of 4,625 IT leaders across 14 countries including Singapore, reveal abou. Article summary: The report points to a gap between Singapore organisations’ AI ambitions and their readiness to run AI reliably in production: 75% are deploying or piloting agentic AI, yet 78% say inadequate real-time data infrastructur. Topic tags: general, government, 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, ch
Singapore organisations are moving into agentic AI, but reported adoption is outpacing readiness to scale. In Confluent’s 2026 Data Streaming Report, 75% of Singapore IT leaders said their organisations were deploying or piloting agentic AI, while 78% said inadequate real-time data infrastructure was stalling efforts to scale AI. 2
What the survey says about Singapore
The report draws on responses from 4,625 IT leaders across 14 countries. That is the overall survey sample; the available reporting does not give Singapore’s sample size. 2
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The Singapore figures point to a gap between experimenting with agentic AI and supporting it at scale. The 75% adoption-or-pilot figure describes organisational activity, not how many projects are in production or delivering sustained results. Meanwhile, the 78% infrastructure figure identifies a barrier respondents associate with scaling AI. 2
Respondents also reported difficulties with the operational foundations around agentic AI: 95% said they experience or anticipate challenges with data infrastructure and quality, another 95% with integrating legacy systems, and 93% with LLM reliability. Because these figures combine experienced and anticipated difficulties, they should not be read as failure rates. 2
Why pilots can be easier than production
A pilot can work within a limited setting. Scaling requires AI systems to connect with business data and existing technology in ways that remain reliable as conditions change. The survey’s reported concerns about data quality, legacy integration and LLM reliability suggest that the transition is not only a question of selecting a model. It also depends on whether organisations can provide usable data and connect AI to the systems where that data lives. 2
That is an interpretation of the reported barriers, not proof that any single technical approach will solve them. The survey identifies areas of difficulty; it does not establish that real-time data infrastructure alone is sufficient for production readiness.
What the findings do—and don’t—show
The headline figures describe what surveyed IT leaders reported. They do not establish that every Singapore organisation faces the same constraints, nor do they show that the survey’s results represent all businesses in the country. The sample covered multiple countries, and the available sources do not specify the Singapore respondent count. 2
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The findings also come from Confluent’s own data-streaming report. They are useful as a snapshot of respondents’ stated priorities and challenges, but should be considered in that context when evaluating claims about data-streaming platforms or their benefits. 9
The clearest takeaway is that Singapore’s agentic AI activity is substantial, while respondents see real-time data infrastructure and integration as important obstacles to scaling. For organisations moving beyond pilots, the reported challenges make data quality, system connections and reliability central questions—not afterthoughts.
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In Singapore, 75% of IT leaders say their organisations are deploying or piloting agentic AI, but 78% say inadequate real time data infrastructure is stalling AI scaling.