- Confluent research reveals that companies aren’t worried about the scale of AI investment – the ambition is there
- Instead, companies struggle with legacy data systems
- We just didn’t know we needed continuous intelligence support back then
Companies are still investing heavily in AI while figuring out where it can best be used, but Confluent believes that investment volume is no longer a barrier. Instead, it is the quality of the data AI systems rely on that is failing them.
Three in four (72%) IT leaders say poor real-time data infrastructure prevents them from being able to scale properly.
Real-time data processing (72%), uncertainty about data lineage (66%) and fragmented data ownership (65%) are among the biggest challenges companies face when trying to implement AI.
AI’s biggest bottleneck is data
These challenges have ultimately led to lower-than-expected AI deployments and poor ROI—only 32% say they have agent AI in production, and the majority instead experience delays.
To remedy that, 80% say they now prioritize using enterprise data to power AI-based systems, with data streaming platforms cited as one of the biggest supporters by 88% of IT leaders. In fact, it is more of a priority than AI and ML (82%), indicating that managers are increasingly aware of how to solve the problem.
“Models must be connected to the systems, events and signals that reflect what is happening across the enterprise,” wrote Chief Product Officer Shaun Clowes, referring to the currently fragmented data systems. But Clowes acknowledged that it is not necessarily organizations’ fault that AI systems fail.
Clowes explained that current infrastructures were not designed for continuous intelligence, which is why all companies, regardless of sector or size, face the same problems.
“The companies making the most progress are investing not only in AI itself, but in the data foundation needed to support it,” he concluded.
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