More than two-thirds (69%) of Australian 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, including more than 200 in Australia, examines the challenges that enterprises are facing when scaling AI. It shows that while investment in AI continues to grow, Australian organisations are having to manage a more complex set of data challenges, from real-time infrastructure and processing to lineage, quality and oversight.
According to the research, 70% of IT leaders have encountered at least three challenges when scaling AI initiatives. Among the most common are ambiguity surrounding data lineage, timeliness, and quality assurance (72%), insufficient infrastructure for real-time data processing (69%), fragmented ownership of data (66%) and insufficient skills and expertise in managing AI projects and workflows (66%). Together, these findings suggest many organisations are grappling not only with the technical demands of scaling AI, but also with keeping the data behind those systems current, trustworthy and properly controlled.
These challenges are also slowing the deployment of agentic AI. Over two-thirds (70%) of IT leaders cite the reliability and non-determinism of LLMs as a key barrier, while three in five (60%) cite data infrastructure and data quality issues as barriers to agentic adoption. Only 36% of organisations report having agentic AI in production, with the majority experiencing delays.
Greg Taylor, SVP APAC at Confluent said: “Australian organisations understand that AI governance cannot stop at the model. As AI systems become more embedded in business processes, leaders need confidence in the data behind every output, decision and action. That means knowing where the data came from, whether it is current, how it has been governed, and who has access to it. Data streaming platforms are critical for enabling organisations to govern live data as it moves, not after the fact. That real-time foundation determines which businesses can scale AI safely and create meaningful business value.”
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. Four in five (80%) Australian IT leaders say using enterprise data to drive AI-based systems is 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. Nearly nine in 10 (89%) Australian IT leaders say effective data sovereignty is important, while the same proportion (89%) say effective data provenance and tracking capability is important.
Many see data streaming as a key part of the solution. Nearly nine in 10 (89%) say data streaming platforms can help address governance, risk and compliance issues in agentic AI by enforcing data access and usage policies upstream. A majority (93%) 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 90% say it makes data more trustworthy, contextualised and discoverable. More broadly, 94% say data streaming has increased or is expected to increase the impact of their AI investments, and 91% say it helps ease the path to AI adoption.
Data streaming investment overtakes AI
The report finds that as AI investments increase, investments into data streaming also increase, with 88% of IT leaders ranking data streaming as a key priority, alongside 80% citing AI and machine learning technologies. The findings suggest IT leaders increasingly recognise that maximising the value of AI depends on access to trusted, real-time data. As organisations move AI initiatives into production, attention is shifting from models alone to the infrastructure needed to deliver the right data at the right time.
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.
“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.
Methodology
For the fifth installment of our annual Data Streaming Report, we teamed up with Freeform Dynamics and Radma Research to gather responses from 4,625 IT leaders who are familiar with data streaming and whose experience with the technology ranges from little to significant, including 225 respondents in Australia. Survey respondents hold various strategic and leadership positions. They include those in C-suite roles, directors, vice presidents, managers, senior contributors, and senior consultants in companies with 500 or more employees. The pool of respondents spans 14 countries, including the United States, Canada, Australia, France, Germany, India, Indonesia, Japan, Singapore, Spain, the United Arab Emirates, the United Kingdom, Saudi Arabia, and Thailand.
