Artificial intelligence is driving new innovation opportunities in companies across EMEA, but many organizations are finding that their projects are progressing more slowly than expected because their technology infrastructure was not designed to support these types of intensive workloads.
The investment impulse is significant. According to Statista, the AI market in Europe could grow from around $105 billion in 2024 to more than $640 billion in 2031, with a compound annual growth rate of 35%. However, legacy infrastructure remains one of the main obstacles to realizing its full potential and allowing digital infrastructure to adapt to the pace of innovation.
To help identify these challenges, Dell Technologies points out five indicators that can show that an organization’s infrastructure is limiting the development of AI initiatives and slowing the modernization of its technology infrastructure.
1. Data access becomes a bottleneck
AI models depend on large volumes of quality data. If data teams spend more time waiting for data sets to load or integrate than developing models, the infrastructure is likely not ready for these workloads. Additionally, in regions such as Europe, regulations such as the General Data Protection Regulation (GDPR) require that data access and management be carried out with strict security and compliance criteria, which requires a robust and well-governed data infrastructure. Modern data management platforms allow you to unify access, accelerate analysis and ensure that information is managed securely and in compliance with regulations.
2. Server infrastructure doesn’t scale for new AI loads
Although few organizations train large models from scratch, more and more companies are using AI for advanced analytics, computer vision, automation, or real-time decision making. These applications require considerable computing power and can overwhelm general-purpose servers that are already operating near their limits within existing infrastructure. When business applications and AI loads compete for the same infrastructure resources, performance suffers. Infrastructures designed specifically for AI, with accelerated computing capabilities, help manage these loads efficiently and maintain stable performance within the technology infrastructure.
3. The network becomes a data jam
AI not only requires computing power and storage, but also networks capable of moving large volumes of information between systems. If data transfers are slow, congestion occurs, or training tasks are interrupted, the network is likely limiting the performance of AI applications and showing gaps in the network infrastructure. A high-speed, low-latency network infrastructure is essential to ensure data flows seamlessly between storage, servers, and end users.
4. AI deployment and management are too complex
Moving an AI model from the lab to production should be an agile process. However, many organizations face rigid technology environments, with manual configurations and complex dependencies that make it difficult to deploy and scale new applications within their infrastructure. This lack of agility in infrastructure can slow down innovation, especially in a competitive environment where speed in bringing new solutions to market is key. Modern infrastructures integrate automation tools and software platforms that simplify management and facilitate the deployment of AI applications.
5. There is no clear strategy to scale
Many AI projects start as pilots, but the true value comes when they scale across the entire organization. If expanding them requires redesigning the infrastructure or making large investments, their adoption becomes complicated. Modular and scalable architectures allow the infrastructure to be expanded progressively, adjusting the investment to the real needs of the business.
Infrastructures specifically designed for AI help manage loads efficiently
Preparing infrastructure for AI is not just about adopting new technologies, but about building a flexible foundation ready for growth. Modernizing infrastructure allows organizations to simplify complexity, accelerate innovation, and create the conditions for AI to deliver real value to the business.
Investing in modern infrastructure designed specifically for these workloads allows companies to overcome the limitations of legacy systems and move towards more agile, efficient and future-ready environments for artificial intelligence.
