98% of EMEA organisations delayed or cancelled an AI project in the past year due to data governance, compliance or regulatory challenges, a Cloudera survey finds.
Companies across Europe, the Middle East and Africa (EMEA) are reworking their data infrastructure as artificial intelligence (AI) moves deeper into enterprise operations.
According to Cloudera’s latest report, The Great AI Re-Architecture, 98% of EMEA organisations delayed or cancelled an AI project in the past year because of data governance, compliance or regulatory hurdles.
The survey covered 400 IT and business leaders in EMEA. Globally, it gathered responses from 1,500 enterprise architects, cloud infrastructure leads and data architects.
EMEA Firms Plan Major Data Architecture Changes
The findings point to a major shift in how companies build and manage their technology infrastructure.
Around 81% of EMEA enterprises said their existing data architecture needs a significant overhaul. Meanwhile, 74% have already made major changes to their data storage and architecture practices to support AI.
AI is also changing where companies want to store and process data. About 30% of EMEA organisations plan to move from public cloud-only models to hybrid-first infrastructure over the next two years.
This shift reflects growing demand for stronger control over data, especially as companies use AI with sensitive business information.
AI Raises Data Governance and Compliance Pressure
Data governance has become a key challenge as AI adoption grows.
Around 65% of EMEA IT leaders said feeding sensitive corporate data into AI models has made data governance more difficult.
Furthermore, 73% of respondents globally said AI has made data governance more complex. More than half, or 55%, said they delayed or cancelled more than six AI projects in the past 12 months because of governance, compliance or regulatory issues.
The trend shows that AI adoption is no longer only a technology challenge. Companies must also decide how to protect, manage and control the data that powers AI systems.
AI Workloads Push Up Infrastructure Costs
AI is also putting pressure on enterprise infrastructure budgets.
About 75% of respondents said AI integrations have changed their organisation’s data storage and architecture practices. At the same time, 84% reported higher infrastructure costs because of AI workloads.
As a result, businesses are reviewing both their technology setup and their data strategies.
The report suggests that companies are moving away from older data architectures that were built mainly for traditional analytics. Instead, they are looking for systems that can support AI at scale while maintaining security and governance.
Cloudera Calls for a New Data Foundation
Sergio Gago, Chief Technology Officer at Cloudera, said AI is forcing organisations to rethink the foundations of their technology infrastructure.
“Many enterprises are discovering that the architectures built for traditional analytics weren’t designed for the scale, governance, and flexibility AI demands today.”
He added that businesses will need a data foundation that allows them to run AI where it makes the most sense without giving up control or security.
Cloudera said its approach combines public cloud, on-premise data centres, sovereign clouds and edge environments.
Hybrid Infrastructure Gains Momentum
The growing interest in hybrid infrastructure reflects a wider shift in enterprise IT.
Companies are looking for greater flexibility over where data is stored and where AI workloads run. At the same time, they need to meet regulatory and security requirements.
For EMEA organisations, these concerns are particularly important because data rules and sovereignty requirements vary across markets.
Therefore, the rise of AI is creating a broader infrastructure reset. Businesses are not simply adding AI tools to existing systems. They are increasingly redesigning the data architecture that supports their digital operations.
AI Re-Architecture Becomes an Enterprise Priority
Cloudera’s findings suggest that the next phase of enterprise AI will depend heavily on data readiness.
As AI moves from pilot programmes into everyday business operations, organisations will need data systems that are secure, flexible and easier to govern.
The report describes this shift as “The Great AI Re-Architecture” — a move from legacy data systems towards hybrid environments designed to connect trusted data with AI.
