'Most organizations do not have an AI investment problem, they have a data problem': New study warns infrastructure demands could be what's really holding AI back

Date:
Thu, 18 Jun 2026 16:05:00 +0000

Description:
Existing infrastructure "was not designed for continuous intelligence" all companies, big or small, need to reinvest in their data platforms.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter 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. Latest Videos From Watch full video here: 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. You may like How AI will collide with data readiness Enterprises dont have an AI problem, they have a data problem Why building AI applications still means building infrastructure-first

"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. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners
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"The companies making the most progress are investing not only in AI itself, but in the data foundations needed to support it," he concluded. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.



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