New industry research reveals that while data streaming is becoming essential for AI workloads, quality and scalability gaps are creating serious friction for large enterprises.
A new study from Conduktor highlights a growing paradox at the heart of enterprise AI: data streaming is rapidly becoming indispensable, yet its weaknesses increasingly threaten the performance and reliability of AI systems. In a survey of 200 senior IT and data leaders at companies earning over $50 million annually, 43% said they already rely on data streaming to train or run AI models. It’s a clear sign of how crucial live data pipelines have become.
Respondents ranked integration with AI and machine learning as the most important capability for modern streaming platforms, surpassing traditional priorities such as application connectors and even security and governance. But as reliance rises, so does frustration. Companies report recurring issues with inconsistent data formats, duplicated events that distort AI outputs, and missing or incomplete data: problems that strike at the core of model accuracy.
Scaling AI initiatives introduces even sharper challenges. Data privacy and security concerns were cited by 72% of respondents, making compliance a primary barrier to AI expansion. High infrastructure costs (59%) and insufficient real-time processing capabilities (58%) further constrain how far enterprises can push their AI ambitions.
The payment and fintech sectors, where millisecond decisions shape fraud prevention, risk scoring, and transaction routing, are particularly sensitive to these findings. Industry analysts note that poor-quality streaming data can inflate false positives, distort customer insights, and slow real-time transaction flows. With global digital payments expected to surpass $14 trillion by 2026, even minor data delays or inconsistencies translate into meaningful financial and operational risk.
Despite the hurdles, confidence remains strong. Eight in 10 executives rated their current streaming-to-AI integration as “good,” reflecting optimism that the technology is directionally aligned with enterprise needs. With the streaming data processing market expected to grow from $9.5 billion in 2023 to $23.8 billion by 2032, demand for more unified, secure, AI-ready pipelines will only intensify.
