AI has become the driving force behind Europe’s digital ambitions.
Across industries, from healthcare and manufacturing to finance, energy, retail, and logistics, organizations are racing to deploy new models, automate processes, and secure a competitive edge. Yet a critical challenge persists: many are trying to build advanced AI systems on infrastructures that don’t meet the requirements of these complex workloads.
Vice President at Western Digital.
Discussions about AI often center on GPUs, training methods, model performance, and talents. But it is another consideration that lies at the heart of any digital system – organizations need enough storage capacity to keep up with the sheer volume of data that AI requires.
At the same time, Europe’s push for digital sovereignty is reshaping its regulatory landscape, prompting enterprises to rethink how and where their data is stored.
These dynamics, AI’s exploding data generation and demand as well as Europe’s increasingly stringent data governance requirements (e.g., Data Governance Act (DGA), GDPR, and the EU AI Act) touch different parts of the new AI data economy and can create significant complexity.
So what should organizations consider as they try to navigate both? And how to turn this emerging environment into an advantage?
How the sovereignty drive is reshaping technical architectures
Digital transformation has driven organizations to rely more heavily on interconnected, distributed and multi-domain IT architectures. The ‘five Vs’ of data – velocity, volume, value, variety and veracity – should also shape an organizations’ core tiered infrastructure.
The demands of managing this complex architecture continue to span across legal, compliance, tax, audit and risk management.
For any business, data sovereignty and governance should not be an isolated compliance task; it should ultimately shape how and where an organization’s core systems operate.
Regulatory pressure has also transformed the scope of data obligations. GDPR laid the foundation, but the EU Data Act, AI Act, NIS2, and DORA all affect data obligations, including operational infrastructure.
AI exposes hidden infrastructure gaps
Modern AI workloads need far more than computational strength. They can demand petabyte-scale storage, high-performance access to data and ultra-low-latency pipelines to feed GPUs efficiently.
When storage sits too far from compute, training cycles slow, costs rise, and model performance suffers – factors that directly influence an organization’s ability to deploy AI at scale.
This is where on-premises and hybrid cloud IT strategies intersect directly with compliance and AI optimization. From a policy requirement, keeping data local can become a technical advantage.
Increasingly, organizations need to ensure co-location of data and compute within jurisdictions that align with EU-compliance requirements, both to satisfy data protection and sovereignty concerns and to achieve the speed necessary for real-time analytics, iterative model development and high-performance inference.
As a result, EMEA is seeing rapid growth in metro-edge data centers – localized, highly connected facilities located near major populations or industrial hubs. Just recently, three huge new data center schemes for London worth £10bn were revealed.
Cushman & Wakefield reports that the operational capacity of EMEA data center markets rose by 21% between H1 2024 and H1 2025, with emerging metro-edge markets outside traditional hubs (Frankfurt, London, Amsterdam, Paris, Dublin) reshaping the landscape.
Cities like Oslo, Dubai, Berlin, and Lisbon are also seeing rapid increases in new data center facilities.
These metro-edge centers can offer high-density storage, local compliance, and the proximity needed to help eliminate latency bottlenecks. For organizations building or scaling AI systems, this local-first architecture can be essential.
It can help ensure that data stays in certain boundaries while delivering the throughput required for modern AI.
Infrastructure modernization in practice
For all the focus on AI tools and computation hardware, the backbone of any AI strategy is the storage architecture supporting it. Without storage, there’s no AI. So, in addition to optimizing compute power, organizations across Europe should be putting importance on “where is my data, and how quickly can I access it.”
This shift involves three major architectural principles:
Design for data locality – Move computational technology closer to the data instead of moving vast datasets to remote clouds. This can cut latency, improve model performance and lower data egress costs.
Invest in scalable, high-capacity storage – AI workflows generate, store and need huge amounts of data. To keep up with demands for clean, consistent and resilient datasets, AI needs expansive, cost-effective storage systems – especially for unstructured data like video, logs and sensor data.
High-capacity HDDs and AI-optimized storage arrays (JBODs) are becoming central to long-term AI sustainability.
Build hybrid ecosystems that balance sovereignty and scale – Sensitive datasets can remain in local or metro-edge environments while public clouds handle burst compute, global collaboration and non-sensitive training workloads.
AI and data sovereignty are putting regional data centers at the heart of Europe’s AI evolution. Local data centers and storage providers are creating real value for businesses by offering deeper local expertise and aligning closely with regional governance requirements.
The result is a new reality: scalable, high-performance infrastructure located closer to home.
Organizations that make these architectural choices today will shape the next era of Europe’s digital advantages.
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