AI is rapidly transforming businesses across Europe, the Middle East, and Africa (EMEA), unlocking innovation and potential in vital areas from retail personalisation to medical research. But Irish organisations in particular are feeling both the excitement and the strain. Many businesses find their AI ambitions stalling – as no one expected they'd need to support AI workloads when designing their infrastructure strategy.

The investment momentum is strong. Projections show the AI market in Europe alone is experiencing robust growth, projected to expand from approximately $105B in 2024 to over $640B by 2031, at a CAGR of 35% (Statista). But in Ireland, the legacy systems remain one of the biggest barriers to progress, with almost 28% of businesses saying their servers need upgrading to support AI workloads and 34% saying the same for their storage systems, according to the Dell Technologies Innovation Catalyst Study.

And as data volumes surge, 97% organisations that are planning to increase their storage capacity expect to face challenges of some sort when doing so, underscoring the scale of the infrastructure gap.

To truly unlock AI's potential, leaders must first look inward and assess if their infrastructure is a launchpad for innovation or a barrier to progress. Here are five indicators that your infrastructure might be holding you back.

1. Data Access is a Bottleneck, Not an Enabler

AI models are fueled by data. The more high-quality data they can process, the more accurate and insightful they become. However, many local businesses still struggle with fragmented or slow-moving data. If data scientists spend more time waiting for datasets to load than they do building models, that is a problem. Legacy storage systems often struggle to deliver the high-speed, parallel throughput required for training complex algorithms.

The challenge is further amplified by Ireland's strict regulatory environment, as seen 40% of the organisations say they face challenges when it comes to meeting regulatory data requirements, when it comes to increasing storage capacity, and 37% cite data security and privacy concerns as barriers when planning to scale their storage infrastructure.

The need for strong data management in the EMEA region is further amplified by stringent regulatory requirements. Regulations like the General Data Protection Regulation (GDPR) in Europe set high standards for data privacy, consent, and localisation. Businesses need to ensure that data used for AI is not only accessible and timely but also managed and transferred in compliance with these legal mandates.

Consider a financial institution in London aiming to use AI for fraud detection. Real-time analysis is essential, but a fragmented or slow data landscape not only risks missed threats but can also lead to breaches of privacy mandates. Modern, compliant data platforms help unify, streamline, and accelerate access – enabling safe, rapid innovation, while meeting the complex requirements for privacy and governance.

2. Scaling Server Infrastructure for the Next Wave of AI

Running AI in production is still a highly compute-intensive challenge for most businesses. While few enterprises are training large language models from scratch, many are deploying AI to support real-time decision making, analytics, computer vision, and increasingly autonomous workflows alongside existing business applications.

Almost 28% of Irish organisations say their servers need upgrading to support AI workloads, as it places sustained pressure on server infrastructure, particularly when general-purpose servers are already operating close to capacity. When AI inference, data processing and core applications compete for the same resources, performance suffers, and the value of AI is harder to realise. Purpose-built infrastructure, including accelerated compute, helps businesses support these mixed workloads efficiently while maintaining reliability and predictable performance.

3. The Network Is...