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 2026 Data Streaming Report from Confluent.
The report, which surveyed 4,625 IT leaders worldwide across 14 countries, including Singapore, examines the challenges enterprises face when scaling AI. Singapore organisations are rapidly advancing their AI ambitions, with 75% are already deploying or piloting agentic AI solutions. This momentum is also placing greater urgency on organisations to address the real-time infrastructure, data governance and organisational readiness needed to support AI at scale.
According to the research, 78% of Singapore IT leaders have encountered at least three challenges when scaling AI initiatives. Among the most common are insufficient infrastructure for real-time data processing (78%), fragmented ownership of data (73%) and insufficient skills and expertise in managing AI (73%).
These infrastructure challenges are also slowing the deployment of agentic AI. The majority of Singapore’s leaders experience or anticipate struggles with the data infrastructure and quality (95%), legacy system integration (95%) and LLM reliability (93%). As a result, over 73% report stalled agentic AI projects, with half completely abandoning the work – APAC leaders responded similarly at 74% and 53%.
Greg Taylor, Senior Vice President APAC at Confluent said, “Businesses across Singapore are rapidly embracing AI, strengthening the country’s position as a global leader in AI governance. But as AI systems become more embedded in business processes, trust cannot come from regulation alone, especially given the different regulatory approaches across APAC.”
“Organisations need the confidence in their data to power every output, decision and action – so the onus is on business leaders to assess whether their data infrastructure is ready to support AI at scale.”
Unlocking AI in real time
As organisations look to move AI from pilot projects into production, attention is increasingly turning to the data that powers it. More than four in five (86%) Singapore IT leaders rate continuous and up-to-date business visibility as a top business priority, highlighting the growing importance of real-time access to trusted information – a sentiment echoed by 91% of APAC IT leaders.
That push is also bringing data sovereignty and provenance into sharper focus. 86% say effective management of data sovereignty is important, alongside 82% valuing effective data provenance and tracking capabilities. This mirrors the wider APAC trend, where the figures stand at 90% and 86% respectively.
As organisations move AI initiatives into production, attention is shifting from LLM models alone to the infrastructure needed to deliver the right data at the right time. Many view data streaming as a key part of that infrastructure. 90% say data streaming platforms (DSPs) can help address governance, risk and compliance issues in agentic AI, by enforcing data access and usage policies upstream.
A majority (91%) also say data streaming platforms help unblock agentic AI progress by improving LLM reliability and non-determinism, ensuring data is complete and up-to-date to potentially reduce hallucinations, while 92% believe DSPs make data more trustworthy, contextualised and discoverable.
Ultimately, Singapore IT leaders acknowledge that data streaming helps ease the path to AI adoption, providing necessary context sourced data (91%) and enabling data provenance (88%).
Data streaming investment overtakes AI
The report also finds that as AI investments increase, investments in data streaming also increase. 86% of Singapore leaders rank data streaming as an investment priority, alongside AI and machine learning solutions (85%) and data management and governance (90%). The findings suggest Singapore IT leaders increasingly recognise that maximising the value of AI depends on access to trusted, real-time data.
Many are already seeing the benefits: 65% of organisations have created richer, more responsive customer experiences, while 61% report greater automation and responsiveness of internal processes.
“Most organisations do not have an AI investment problem, they have a data problem. AI systems depend on fresh, accurate and contextual information, but too many are still being built on fragmented data, batch processes, and infrastructure that was not designed for continuous intelligence,” said Shaun Clowes, Chief Product Officer at Confluent.
“As organisations move beyond experimentation and start deploying AI across critical business processes, those gaps become harder to ignore. Models need to be connected to the systems, events and signals that reflect what is happening across the business. The companies making the most progress are investing not only in AI itself, but in the data foundations needed to support it. Those foundations will determine which organisations can turn AI investment into business value at scale.”
Download the full 2026 Data Streaming Report here.
Legal Disclaimer: The Editor provides this news content "as is," without any warranty of any kind. We disclaim all responsibility and liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. For any complaints or copyright concerns regarding this article, please contact the author mentioned above.
