Singapore – Singapore organisations are making rapid progress in adopting artificial intelligence (AI), but many are struggling to scale their initiatives because of inadequate data infrastructure, according to a new report by Confluent.
The company’s 2026 Data Streaming Report, which surveyed 4,625 IT leaders across 14 countries including Singapore, found that 78% of Singapore IT leaders said a lack of real-time data infrastructure is hindering efforts to scale AI. The report also showed that 75% of organisations in Singapore are already deploying or piloting agentic AI solutions, increasing the need for stronger real-time infrastructure, data governance and organisational readiness.
According to the study, 78% of Singapore respondents have encountered at least three obstacles when scaling AI initiatives. The most commonly cited challenges were insufficient infrastructure for real-time data processing (78%), fragmented ownership of data (73%), and a lack of AI management skills and expertise (73%).
The findings also highlighted barriers to deploying agentic AI. Around 95% of Singapore IT leaders said they either experience or expect challenges related to data infrastructure and quality, while another 95% cited legacy system integration. Meanwhile, 93% identified large language model (LLM) reliability as a concern. More than 73% said agentic AI projects had stalled, with half reporting that projects had been abandoned altogether. Comparable figures across Asia-Pacific stood at 74% and 53%, respectively.
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.”
He added, “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.”
The report found that 86% of Singapore IT leaders consider continuous and up-to-date business visibility a top business priority, compared with 91% across Asia-Pacific. Data sovereignty was also identified as a key concern, with 86% of respondents saying effective management of data sovereignty is important, while 82% highlighted the importance of data provenance and tracking capabilities.
Many respondents also identified data streaming platforms as a key enabler for scaling AI. Around 90% said such platforms could help address governance, risk and compliance requirements for agentic AI by enforcing data access and usage policies. Meanwhile, 91% said data streaming platforms improve LLM reliability by ensuring data remains current and complete, while 92% believed they make data more trustworthy, contextualised and easier to discover. Another 91% said data streaming provides the contextual data needed for AI adoption, and 88% said it supports data provenance.
The report also found that investment priorities are shifting towards the underlying data infrastructure needed to support AI. While 90% of Singapore IT leaders ranked data management and governance as an investment priority, 86% prioritised data streaming, slightly ahead of AI and machine learning solutions at 85%.
Among organisations already investing in these capabilities, 65% reported delivering richer and more responsive customer experiences, while 61% said they had improved automation and responsiveness across internal business processes.
Shaun Clowes, Chief Product Officer at Confluent, said, “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.”
He added, “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.”
