Cloudera released its latest global survey, The Great AI Re-Architecture, revealing a fundamental shift in enterprise IT as organizations redesign their data architectures to meet the demands of AI. Based on responses from 1,500 Enterprise Architects, Cloud Infrastructure Leads, and Data Architects worldwide, the report finds that while AI adoption has become mainstream, legacy data architectures are increasingly limiting organizations’ ability to scale AI securely, efficiently, and cost-effectively.

The findings point to a fundamental shift in enterprise IT architecture. While 77% of organizations are actively using AI, nearly all (95%) have delayed or canceled AI initiatives over the past year because of data governance, compliance, or regulatory challenges. To overcome these challenges, 72% say their current data architecture requires a significant overhaul to meet future AI requirements, suggesting today’s infrastructure was not built for the demands of modern AI.

Together, these findings highlight what Cloudera calls “The Great AI Re-Architecture”—the mass transition from legacy data architectures toward hybrid environments that enable organizations to bring trusted AI to trusted data, wherever it resides.

“This current era of AI is forcing organizations to rethink the foundations of their technology infrastructure,” said Sergio Gago, Chief Technology Officer at Cloudera. “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.”

AI Is Driving an Enterprise Infrastructure Reset
AI has moved well beyond isolated pilot projects and is now embedded across enterprise operations. As organizations expand AI across the business, they’re placing mounting pressure on infrastructure that was never designed for AI at scale.

Three-quarters (75%) of respondents say AI integrations have changed their organization’s data storage and architecture practices, while 84% report increased infrastructure costs driven by AI workloads. Together, these findings suggest organizations are rethinking not only where data lives, but how it is managed, governed, and delivered to AI systems.

Governance Is Critical Infrastructure
As organizations scale AI, governance is becoming foundational to enterprise AI success. Earlier this year, Cloudera’s Data Readiness Index found that 75% of organizations said AI is exposing the limitations of their legacy governance processes. This latest research suggests those challenges are only intensifying as AI adoption grows.

Nearly three-quarters (73%) of respondents say AI has made data governance more complex, and more than half (55%) report delaying or canceling more than six AI projects over the past 12 months due to governance, compliance, or regulatory challenges.

The challenge is compounded by increasingly distributed data. Nearly every respondent (97%) reports moving data between environments at least monthly, making consistent governance across cloud, private cloud, on-premises, and edge environments essential for scaling AI securely.

Hybrid Architectures Become the New Enterprise Standard
Organizations are increasingly adopting hybrid architectures to balance performance, governance, cost, and flexibility, all of which are critical to modern AI success.

Two-thirds (66%) of respondents say they have moved AI workloads from public cloud environments back to private cloud or on-premises infrastructure during the past year, signaling a broader shift toward hybrid architectures that allow organizations to run AI workloads where they perform best.

Looking ahead, organizations are investing across cloud, on-premises, edge, and hybrid environments rather than relying on a single deployment model. One-quarter (25%) say they plan to prioritize a hybrid-first architecture over the next two years, reinforcing that the future of enterprise AI will be defined, in part, by flexibility rather than a single infrastructure strategy.

The Future of Enterprise AI Depends on Hybrid Data Architectures
AI adoption is no longer the differentiator; AI optimization is. Organizations that modernize their data architectures to govern data consistently and run AI wherever it makes the most sense will be well positioned to deliver scalable, secure AI and lasting business value.