- Confluent study reveals that companies are not worried about the scale of investments in AI: the ambitions are there
- Instead, businesses are struggling with legacy data systems
- At the time, we just didn’t know that we needed continuous intelligence support.
Companies continue to invest heavily in AI while they determine where it can best be used, but Confluent believes that the volume of investment is no longer a barrier. Instead, it is the quality of the data that AI systems rely on that is letting them down.
Three in four IT leaders (72%) say poor real-time data infrastructure prevents them from scaling properly.
Real-time data processing (72%), uncertainty over data lineage (66%), and fragmented data ownership (65%) are among the biggest challenges businesses face when trying to implement AI.
The biggest blocker of AI is data
These challenges ultimately led to lower-than-expected AI deployments and low ROI: only 32% report having agentic AI in production, and the majority instead experience delays.
To address this, 80% say they are now prioritizing the use of business data to drive AI-based systems, with data streaming platforms cited as one of the top supports by 88% of IT leaders. In fact, it is a greater priority than AI and ML (82%), indicating that leaders are increasingly aware of how they could solve the problem.
“Models need to be connected to the systems, events and signals that reflect what’s happening in the business,” wrote Shaun Clowes, chief product officer, referring to currently fragmented data systems. But Clowes acknowledged that it’s not necessarily the fault of organizations that AI systems fail.
Clowes explained that current infrastructures were not designed for continuous intelligence, which is why all companies, regardless of industry or size, face the same problems.
“The companies that are making the most progress are investing not only in AI itself, but also in the databases needed to support it,” he concluded.
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