“The Great AI Re-Architecture,” a Fundamental Shift in Enterprise Computing
Cloudera reports that 95% of companies have postponed their AI projects due to infrastructure limitations. A data re-architecture is essential. The current architecture was not designed to meet the needs of modern AI.
72% of companies believe their current data architecture requires a thorough overhaul to meet the future demands of AI, suggesting that the current infrastructure was not designed for the needs of modern AI.
Cloudera’s The Great AI Re-Architecture, based on responses from 1,500 enterprise architects, cloud infrastructure managers, and data architects worldwide, highlights the massive shift from traditional data architectures to hybrid environments, enabling organizations to integrate reliable AI with reliable data, regardless of its location.
“The current era of AI is forcing organizations to rethink the foundations of their technology infrastructure,” says Sergio Gago, CTO, Cloudera. “Many companies are finding that architectures designed for traditional analytics were not suited to the scale, governance, and flexibility that AI demands today. Success will depend on establishing a data infrastructure that gives organizations the freedom to deploy AI where it is most relevant, without compromising control or security.”
AI Is Disrupting Enterprise Infrastructure
AI has long since moved beyond isolated pilot projects and is now integrated into business operations. As organizations deploy AI across all levels of the enterprise, they are placing increasing pressure on infrastructures that were never designed for such scale.
Three-quarters (75%) of respondents say that AI integrations have changed their organization’s data storage and architecture practices, while 84% report an increase in infrastructure costs due to AI-related workloads. These findings indicate that organizations are rethinking not only where their data is stored, but also how it is managed, governed, and made available to AI systems.
Governance: An Essential Infrastructure
With the large-scale deployment of AI, governance is becoming a fundamental component of business success. Earlier this year, Cloudera’s Data Readiness Index revealed that 75% of organizations believed that AI was highlighting the limitations of their existing governance processes. This latest study suggests that these challenges are only intensifying as AI adoption progresses.
Nearly three-quarters (73%) of respondents say that AI has made data governance more complex, and more than half (55%) report having delayed or canceled more than six AI projects in the past 12 months due to challenges related to governance, compliance, or regulation.
This challenge is exacerbated by the growing distribution of data. Nearly all respondents (97%) report moving data between environments at least once a month, making consistent governance across the cloud, private cloud, on-premises infrastructure, and edge environments essential for the secure deployment of AI.
Hybrid architectures are becoming the new standard for businesses
Organizations are increasingly adopting hybrid architectures to optimize performance, governance, costs, and flexibility—all of which are essential to the success of modern AI.
Two-thirds (66%) of respondents reported having moved AI workloads from public cloud environments to a private cloud or on-premises infrastructure over the past year, reflecting a broader trend toward hybrid architectures. These architectures enable organizations to run AI workloads where they perform best.
This rearchitecture will take shape across cloud, on-premises, edge, and hybrid environments rather than relying on a single deployment model. One-quarter (25%) of them plan to prioritize a hybrid architecture over the next two years, confirming that the future of AI in the enterprise will be defined in part by flexibility rather than a single infrastructure strategy.
