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- Confluent research reveals firms aren't worried about the scale of AI investments – the ambitions are there
- Instead, companies are struggling with legacy data systems
- We just didn't know that we needed support for continuous intelligence back then
Businesses are still investing heavily in AI while they figure out where it can be used best, but Confluent believes the volume of investment isn't a blocker anymore. Instead, it's the quality of the data AI systems rely on that's letting them down.
Three in four (72%) IT leaders say poor real-time data infrastructure is preventing them from being able to scale properly.
Real-time data processing (72%), data lineage uncertainty (66%) and fragmented data ownership (65%) are among the biggest challenges that companies face when trying to implement AI.
AI's biggest blocker is data
These challenges have ultimately led to lower-than-expected AI deployments and poor ROI – only 32% say they have agentic AI in production, and the majority instead experience delays.
To fix it, 80% say they're now prioritizing using enterprise data to drive AI-based systems, with data streaming platforms cited as one of the biggest supports by 88% of IT leaders. In fact, it's more of a priority than AI and ML (82%), indicating that leaders are increasingly aware of how they could fix the problem.
"Models need to be connected to the systems, events and signals that reflect what is happening across the business," Chief Product Officer Shaun Clowes wrote, referencing the currently fragmented data systems. But Clowes acknowledged that it's not necessarily organizations' faults that AI systems are failing.
Clowes explained that current infrastructures weren't designed for continuous intelligence, which is why all companies regardless of sector or size are facing the same issues.
"The companies making the most progress are investing not only in AI itself, but in the data foundations needed to support it," he concluded.
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