Global study of 500 senior data and technology leaders finds reliability gaps tied to DIY and legacy integration, diverting budget and focus from AI innovation
Fivetran, the data foundation for AI, released “The enterprise data infrastructure benchmark report 2026,” revealing that fragile data pipelines are delaying analytics and AI initiatives while exposing large enterprises to significant operational lost revenue potential. Nearly 97% of senior data and technology leaders surveyed said pipeline failures have slowed analytics or AI programs, underscoring reliability as a growing constraint on enterprise AI success.
Nearly 97% of senior data and technology leaders surveyed said pipeline failures have slowed analytics or AI programs, underscoring reliability as a growing constraint on enterprise AI success.
The global study surveyed 500 senior data and technology leaders at organizations with more than 5,000 employees.
The findings suggest the primary challenge facing enterprises is not underinvestment in data, but rather the architecture supporting it. Enterprises now spend an average of $29.3 million annually on data programs, making data one of the largest line items in enterprise technology budgets. Yet despite this investment, reliability challenges continue to erode business value.
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Enterprises allocate approximately 14% of total data budgets to integration, representing an average annual spend of about $4.2 million. Many organizations continue to operate a fragmented mix of DIY pipelines, legacy ETL systems, and partially automated tools, which become increasingly difficult to manage and maintain as data volumes and pipeline counts grow. The benchmark found that pipeline downtime and operational disruption create an estimated $3 million in average monthly business exposure, highlighting a widening gap between data investment and measurable return.
“Enterprises are investing millions in data integration, yet too much of that spend is still going toward maintaining fragile pipelines built for a pre-AI era,” said George Fraser, CEO of Fivetran. “As reliability challenges grow, data architecture is becoming a defining factor in how quickly organizations can scale AI. The leaders pulling ahead are beginning to rethink their foundations around more open data infrastructure approaches that support speed, resilience, and long-term flexibility.”
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Pipeline reliability risks intensify as data environments scale:
- 97% of senior data leaders say pipeline failures have slowed analytics or AI initiatives.
- $3 million in average monthly business exposure is attributed to downtime and operational disruption.
- 4.7 pipeline failures per month occur on average in large enterprises, with incidents taking nearly 13 hours to resolve.
- More than 60 hours of monthly downtime delays analytics delivery and slows AI deployment timelines.
- Over 300 pipelines on average are now managed in enterprise environments, increasing operational complexity.
- Up to $1.4 million in impact per incident can result from a single pipeline failure in large-scale ecosystems.
- 53% of engineering capacity is devoted to maintaining and troubleshooting pipelines, limiting resources for innovation and AI initiatives.
- Organizations operating automated integration platforms are nearly 2x as likely to exceed ROI expectations from data investments.
The findings suggest that as AI adoption accelerates, enterprises will increasingly shift toward open data infrastructure architectures, which emphasize automated data movement, interoperability across platforms, and greater operational resilience at scale. These approaches are emerging as a critical foundation for organizations seeking to reduce engineering overhead while supporting more complex analytics and AI workloads.