Singapore’s enterprise AI adoption is moving faster than the data infrastructure required to support it, according to a new Confluent report that points to a widening gap between experimentation and production readiness.
The company’s “2026 Data Streaming Report” found that 78 per cent of the city-state’s IT leaders say a lack of real-time data infrastructure is stalling their ability to scale AI.
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The finding is notable because Singapore is among Southeast Asia’s most aggressive adopters of AI policy, enterprise digitisation, and data governance frameworks. Yet the survey suggests that the next phase of AI adoption may depend less on model access or boardroom appetite, and more on whether companies can modernise their underlying data systems.
Confluent, an IBM company, surveyed 4,625 IT leaders across 14 markets, including Singapore, Indonesia, Thailand, India, Japan, Australia, the US, Canada, the UK, Germany, France, Spain, Saudi Arabia, and the UAE. Respondents worked in companies with at least 500 employees and held roles ranging from C-suite executives to senior contributors and consultants.
The report was conducted with Freeform Dynamics and Radma Research.
The survey comes as companies across Southeast Asia are moving beyond generative AI pilots into more operational use cases, including customer support automation, fraud detection, logistics optimisation, financial risk analysis, and software development. Singapore, in particular, has positioned itself as a regional AI hub through initiatives such as the National AI Strategy 2.0 and its Model AI Governance Framework. But enterprise adoption remains uneven, especially among companies operating on legacy infrastructure or fragmented data estates.
From model hype to data constraints
According to Confluent, 75 per cent of Singapore organisations are already deploying or piloting agentic AI solutions. Agentic AI refers to systems that can take actions or complete multi-step tasks with limited human intervention, rather than simply generate text or images in response to prompts.
That shift raises the stakes for data reliability. Unlike standalone chatbots, agentic systems need access to timely, accurate, and contextual business data. If the data is stale, incomplete, poorly governed, or locked in silos, the risks move beyond inaccurate answers to faulty actions.
The report found that 78 per cent of Singapore IT leaders have encountered at least three challenges when scaling AI. The most common barriers include insufficient infrastructure for real-time data processing, cited by 78 per cent of respondents; fragmented data ownership, cited by 73 per cent; and insufficient skills in managing AI, also cited by 73 per cent.
These figures broadly reflect what many technology leaders in Southeast Asia are encountering as AI pilots collide with production realities. Large banks, telcos, retailers, and logistics operators in Singapore, Indonesia, Malaysia, Thailand, and Vietnam have accumulated years of customer, transaction, and operational data. But much of it sits across separate systems, cloud environments, on-premise databases, and departmental platforms.
That makes it difficult to feed AI applications with consistent and governed data streams. It also complicates compliance in a region where data protection rules vary significantly, from Singapore’s Personal Data Protection Act to Indonesia’s Personal Data Protection Law and Thailand’s PDPA.
Greg Taylor, Senior Vice President for APAC at Confluent, said Singapore’s AI momentum needs to be matched by stronger data foundations.
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“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 said.
Agentic AI exposes legacy weaknesses
The report suggests that agentic AI is where infrastructure weaknesses become most visible. About 95 per cent of Singapore IT leaders said they experience or expect struggles with data infrastructure and quality, while the same proportion pointed to legacy system integration. Another 93 per cent cited large language model reliability as a concern.
These constraints are already affecting projects. More than 73 per cent of Singapore respondents said agentic AI initiatives had stalled, with half saying projects had been completely abandoned. Across APAC, the figures were similar: 74 per cent reported stalled projects and 53 per cent said work had been abandoned.
The findings should be read with some caution. Confluent is a data streaming company, and the report naturally frames the problem through the lens of streaming infrastructure. Still, the broader diagnosis is consistent with enterprise technology trends in the region. AI adoption is increasingly constrained by the quality, latency, and governance of the data layer.
This is also why infrastructure vendors have been repositioning around AI. Confluent competes in a market that includes open-source Apache Kafka deployments, Redpanda, StreamNative, Aiven, and cloud-native services such as Amazon Kinesis, Google Cloud Pub/Sub, and Azure Event Hubs. Broader data infrastructure players, including Databricks and Snowflake, are also pushing AI-oriented data platforms as enterprises look to unify analytics, governance, and machine learning workloads.
In Southeast Asia, the competitive context is shaped by both cloud adoption and regulatory caution. Banks and insurers in Singapore and Malaysia, for example, face stricter requirements around data lineage, explainability, and outsourcing risk. Digital banks, e-commerce platforms, and ride-hailing companies need low-latency data flows to support fraud monitoring, personalisation, and real-time pricing. These use cases make batch processing increasingly inadequate.
Governance becomes part of AI infrastructure
Confluent’s report found that 86 per cent of Singapore IT leaders rate continuous and up-to-date business visibility as a top priority. The same proportion said effective data sovereignty management is important, while 82 per cent valued data provenance and tracking capabilities. Across APAC, those figures stood at 91 per cent, 90 per cent, and 86 per cent respectively.
That emphasis reflects a shift in how enterprises think about AI governance. Earlier debates focused heavily on model behaviour, bias, and regulatory compliance. Those issues remain important, but companies are increasingly recognising that governance must start upstream, at the point where data is created, moved, transformed, and accessed.
In the report, 90 per cent of Singapore respondents said data streaming platforms can help address governance, risk, and compliance issues in agentic AI by enforcing data access and usage policies upstream. Another 91 per cent said these platforms can improve large language model (LLM) reliability by ensuring data is more complete and current, while 92 per cent said they make data more trustworthy, contextualised, and discoverable.
Shaun Clowes, Chief Product Officer at Confluent, framed the issue as a data problem rather than an AI spending problem. “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 said.
Investment follows the infrastructure layer
The report found that 86 per cent of Singapore leaders rank data streaming as an investment priority, close to AI and machine learning solutions at 85 per cent and data management and governance at 90 per cent.
That pattern matters because technology budgets are beginning to move from experimentation into implementation. Enterprises that spent 2023 and 2024 testing generative AI tools are now asking whether those tools can be embedded into core operations. In Singapore and the wider region, the answer will depend on whether companies can connect AI systems to live operational data without compromising security, compliance, or reliability.
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For Confluent, the commercial implication is clear: AI adoption creates demand for the infrastructure that moves and governs data in real time. For enterprises, the message is more sobering. Access to advanced models is becoming commoditised. The harder work lies in cleaning up data ownership, modernising legacy systems, and building governance into the flow of information.
Singapore may remain ahead of much of Southeast Asia in AI policy and enterprise readiness. But the report suggests that even in the region’s most mature digital economy, AI scale is now running into the same unglamorous constraint that has slowed many technology transformations before it: the plumbing.
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