- Confluent research reveals that companies are not worried about the scale of AI investments: the ambitions are there
- Instead, companies are struggling with legacy data systems.
- We just didn’t know we needed support for continuous intelligence back then.
Companies are still investing heavily in AI as they figure out where it can best be used, but Confluent believes the volume of investment is no longer an obstacle. Instead, it is the quality of the data that AI systems rely on that lets them down.
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 over data lineage (66%), and ownership of fragmented data (65%) are among the biggest challenges companies face when trying to implement AI.
The biggest obstacle for AI is data
Ultimately, these challenges have led to lower-than-expected AI deployments and poor ROI, with only 32% saying they have agent AI in production, and the majority instead experiencing delays.
To address this, 80% say they are now prioritizing using enterprise data to power AI-based systems, and 88% of IT leaders cite data streaming platforms as one of the biggest supports. In fact, it is more of a priority than AI and ML (82%), indicating that leaders are increasingly aware of how they could solve the problem.
“Models must be connected to systems, events and signals that reflect what is happening across the enterprise,” wrote chief product officer Shaun Clowes, referencing currently fragmented data systems. But Clowes acknowledged that it’s not necessarily organizations’ fault that AI systems are failing.
Clowes explained that current infrastructures were not designed for continuous intelligence, so all companies, regardless of sector or size, face the same problems.
“Companies that are making the most progress are investing not only in AI itself, but also in the databases necessary to support it,” he concluded.
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