Confluent’s 2026 Data Streaming Report finds that the biggest barrier to AI growth isn’t investment, but the infrastructure and governance foundations needed to support it.

75% of APAC IT leaders are already deploying or piloting agentic AI solutions, but 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, examines the challenges enterprises face when scaling AI. 

In APAC, infrastructure challenges are slowing the deployment of agentic AI. Key APAC findings from the Report include:

  • 76% cite insufficient real-time data-processing infrastructure as a challenge to scaling AI.
  • 71% cite fragmented data ownership, while another 71% point to insufficient AI and data skills.
  • 94% experience or anticipate challenges with LLM reliability and non-deterministic outputs.
  • 93% experience or anticipate challenges with data infrastructure and quality, while 93% cite legacy-system integration.
  • 74% report agentic AI projects stalling, while 53% have completely abandoned projects.

Greg Taylor, Senior Vice President, APAC, Confluent, said: “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. 

91% of APAC 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.

That push is also bringing data sovereignty and provenance into sharper focus. 90% say effective management of data sovereignty is important, with 86% valuing effective data provenance and tracking capabilities.

As organisations in APAC 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:

  • 94% believe data streaming platforms can improve LLM reliability by injecting up-to-date data.
  • 91% believe data streaming platforms can make data more trustworthy, contextualized and discoverable.
  • 92% believe data streaming platforms can support data provenance and lineage tracking.

Data streaming investment overtakes AI

The report also finds that as AI investments increase, investments in data streaming also increase:

  • 90% of APAC organizations prioritize investment in data streaming technologies and platforms.
  • 60% say data streaming has already improved the 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, 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.”