Why insufficient data streaming infrastructure crumbles AI ambitions
Confluent says nearly three-quarters of global IT leaders can identify a lack of real-time data infrastructure in their business… and, consequently, this reality is stalling their efforts to scale AI.
According to a new 2026 Data Streaming Report from Confluent (which surveyed 4,625 IT leaders worldwide), IT leaders have encountered at least three challenges when scaling AI initiatives.
Among the most common are insufficient infrastructure for real-time data processing (72%), uncertainty around data lineage, timeliness and quality (66%) and fragmented ownership of data (65%).
Around two-thirds of IT leaders cite data infrastructure and data quality issues as barriers to agentic adoption, while only 32% report having agentic AI in production, with the majority experiencing delays.
Confluent says that the findings may suggest that many organisations see data streaming as a key part of the solution.
“Nearly nine in 10 (88%) say data streaming platforms help unblock agentic AI progress by making data more trustworthy, contextualised and discoverable. Meanwhile, 94% say data streaming increases or is expected to increase the impact of their AI investments, and 90% say it helps ease the path to AI adoption,” said the company, in a press statement.
Infrastructure investment
Shaun Clowes, chief product officer at Confluent thinks that IT leaders increasingly recognise that maximising the value of AI depends on access to trusted, real-time data. He says that 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.
“Most organisations do not have an AI investment problem; they have a data problem,” said Clowes. “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 tells us that 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,” added Clowes.
Methodology
For the fifth instalment of its annual Data Streaming Report, Confluent teamed up with Freeform Dynamics and Radma Research to gather responses from IT leaders who are familiar with data streaming and whose experience with the technology ranges from little to significant.