‘The architectures built for traditional analytics weren't designed for the scale, governance, and flexibility AI demands today’
Nearly all (95%) enterprises have delayed or cancelled artificial intelligence (AI) projects over the past year because of data governance and infrastructure shortfalls, impacting rollout timelines of AI-powered HR work, according to a report.
While 77% of organizations are actively using AI, the vast majority have still hit delays tied to governance, compliance, or regulatory challenges, notes Cloudera.
Over 7 in 10 (72%) of respondents believe their current data architecture requires a significant overhaul to meet future AI requirements.
About the same number (73%) said AI has made data governance more complex, while 55% reported delaying or cancelling more than six AI projects over the past year for governance-related reasons.
Cloudera noted that 97% of respondents move data between environments at least monthly, a pattern the company said makes consistent governance difficult to maintain.
“This current era of AI is forcing organizations to rethink the foundations of their technology infrastructure,” said Sergio Gago, chief technology officer at Cloudera, in the report. “Many enterprises are discovering that the architectures built for traditional analytics weren't designed for the scale, governance, and flexibility AI demands today. Success will depend on building a data foundation that gives organizations the freedom to run AI wherever it makes the most sense, without compromising control or security.”
The best way to get the utmost benefit out of AI is to deploy it deeply across operations rather than confining it to pilot projects, according to a previous global report.
Infrastructure and cost pressures
More than 8 in 10 (84%) organizations have seen infrastructure costs rise because of AI workloads, while 75% said AI has changed their organization's approach to data storage and architecture, according to Cloudera. The firm said these findings point to organizations rethinking not only where data resides, but how it is managed and delivered to AI systems.
Two-thirds (66%) of organizations reported moving AI workloads from public cloud back to private cloud or on-premises infrastructure over the past year. A quarter of respondents said they plan to prioritize a hybrid-first architecture over the next two years, according to the survey of 1,500 enterprise architects, cloud infrastructure leads, and data architects at companies with at least 1,000 employees.
Cloudera described the shift as part of a broader move toward hybrid environments that allow organizations to run AI workloads wherever they perform best while maintaining governance and security standards across cloud, on-premises, and edge systems.
Research firm IDC projects that global spending on big data and analytics will reach approximately US$420 billion in 2026, while analyst firm Gartner forecasts that by 2027, 60% of repetitive data management tasks will be automated.
A separate 2026 survey of nearly 280 IT leaders by DataStrike found that 74% expect their infrastructure budgets to increase this year, even as more than half report lacking the internal resources needed to act on that investment — pointing to a persistent gap between funding and capacity.
