78% of Singapore IT Leaders Say Poor Data Infrastructure Is Stalling AI Scale-Up
Source: Frontier Enterprise
Nearly four in five (78%) Singapore IT leaders say a lack of real-time data infrastructure is stalling their efforts to scale AI, according to a new report from Confluent. The survey of 4,625 IT leaders across 14 countries found that while 75% of Singapore organisations are already deploying or piloting agentic AI, infrastructure gaps are forcing many to abandon projects mid-stream.
Singapore has been positioning itself as a global AI hub with considerable success — GIC is pouring capital into frontier AI, government agencies are launching data guidelines, and the city-state's GDP is riding an AI-driven manufacturing wave. But beneath the headlines, a quieter story is unfolding: the infrastructure underneath all this ambition may not be ready for what's coming.
According to Confluent's 2026 Data Streaming Report, which surveyed 4,625 IT leaders across 14 countries including Singapore, 78% of Singapore IT leaders say a lack of real-time data infrastructure is holding back their AI scale-up efforts. Singapore organisations are moving fast — 75% are already deploying or piloting agentic AI solutions — but the foundations aren't keeping pace. 95% of leaders reported struggling with data infrastructure and quality for agentic AI, the same share that cited legacy system integration as a blocker.
The data reveals a pattern familiar to anyone watching enterprise AI adoption: companies are eager to deploy, but the practical realities of getting production-ready data pipelines in place are far harder than the demos suggest. Over 73% of Singapore IT leaders reported stalled agentic AI projects, and half have completely abandoned the work. Three-quarters face at least three major scaling challenges simultaneously — insufficient real-time data infrastructure (78%), fragmented data ownership (73%), and insufficient AI skills and expertise (73%).
Greg Taylor, Confluent's SVP for Asia-Pacific, noted that while Singapore leads in AI governance globally, trust cannot come from regulation alone. The report suggests that data streaming platforms are emerging as a critical missing piece — 91% of leaders said such platforms help unblock agentic AI by improving LLM reliability and reducing hallucinations, while 86% now rank data streaming as an investment priority alongside AI itself.
Why it matters for Singapore: The city-state's AI strategy has focused heavily on governance, talent, and investment — but this report highlights a less glamorous but equally critical gap: the data plumbing. If Singapore wants to maintain its edge as an AI hub, enterprises will need to invest as heavily in real-time data infrastructure as they do in GPUs and model training. The good news is that 65% of organisations already report richer customer experiences from better data streaming, and 61% see more responsive internal processes. The foundations are there — they just need to scale as fast as the ambition.
