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Cloudera, the only company bringing AI to data anywhere, 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. A similar pattern is emerging across Asia Pacific, where 68% say their existing architecture requires significant change, suggesting that 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, with the challenge also evident across Asia Pacific at 61%. These pressures are already affecting deployment: more than half (55%) of the organizations across the world report delaying or canceling more than six AI projects over the past 12 months due to governance, compliance, or regulatory challenges, while 92% of APAC organizations have delayed or canceled at least one project. In the region, data security, governance and compliance requirements are also the leading driver of AI infrastructure change, cited by 46% of respondents.
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 globally and 64% across APAC 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 by flexibility rather than a single infrastructure strategy.
Enterprises in Singapore are focusing on high-performance AI environments, matching their infrastructure strategies with sophisticated AI adoption and corporate innovation. Highlighting a dynamic review of optimal runtime environments, 43% of Singaporean organizations have transitioned AI workloads out of the public cloud, and an additional 36% are evaluating this shift. Investment patterns also highlight edge infrastructure as a primary target, with 35% of local respondents projecting higher edge spending over the coming two years. Concurrently, balancing processing capabilities with operational oversight remains crucial as AI initiatives expand, with 41% identifying data security, governance, and compliance as the primary catalysts for modifying AI infrastructure.
“Across the Asia Pacific region, the conversation is transitioning from merely selecting an AI host to determining which environment yields the superior business results for specific workloads,” observed Remus Lim, Senior Vice President, Asia Pacific & Japan at Cloudera. “Singapore reflects this evolution as enterprises collectively evaluate latency, performance, expenditure, and governance when defining their AI deployment strategies. To scale AI breakthroughs effectively, businesses must possess the agility to transition data and AI seamlessly among cloud, edge, and on-premises setups while maintaining strict governance and operational command.”
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 best positioned to deliver scalable, secure AI and lasting business value.
As these themes take center stage at Cloudera EVOLVE Singapore next week, read the full report to learn how organizations are preparing their data foundations for enterprise AI.
About Cloudera
Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, sovereign clouds, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives.
