AI Ambitions Are Growing Fast – But Is The Infrastructure Ready?
by Gavin Sutton, Head of Marketing
Artificial Intelligence (AI) is here. Whether it’s coming for your job remains to be seen, but one thing that’s certain, is that its adoption is growing at a rapid rate. Finance, healthcare, education….regardless of industry, AI is helping businesses accelerate automation, provide predictive analytics, and enhance real-time decision making. While investment in AI is expected to exceed $632 billion by 20281, there is still an enormous void that needs to be filled in order for businesses to make AI work at scale.
The AI Infrastructure Gap
AI workloads place an enormous demand on infrastructure in terms of compute power, data throughput and storage capacity. Without the right solutions in place, customers will be unable to leverage the true potential of AI.
At the heart of every successful AI operation lies high-performance computing (HPC) infrastructure, underpinned by:
- Advanced servers, such as GPU-accelerated or high-density CPU configurations
- High-throughput, scalable storage capable of managing massive datasets
- Fast, low-latency networking for data movement and model training
- Specialist software and frameworks to orchestrate data processing and model execution
Many organisations today lack the foundational infrastructure to support these requirements, leading to bottlenecks, escalating costs, and underwhelming outcomes.
Servers Built for AI Workloads
AI workloads like those driven by large language models (LLMs) and deep learning applications place extraordinary demands on infrastructure, requiring immense parallel processing power, speed, and efficiency. Without the right server architecture in place, businesses risk falling short of AI’s full potential.
GPU-optimised servers are specifically engineered to meet these requirements. Designed to handle complex inference tasks and deliver ultra-fast performance, these systems support multi-GPU configurations for high-throughput parallel processing, incorporate advanced cooling and power management technologies to maintain energy efficiency, and leverage high-bandwidth interconnects like NVLink and PCIe Gen5 to eliminate bottlenecks.
Combined with orchestration tools and container platforms such as Kubernetes, these servers form the foundation of scalable, high-performance AI environments. Simply put, without this level of infrastructure readiness, organisations will struggle to realise the transformative benefits of AI.
Storage That Keeps Up with AI Data
What’s that saying? Garbage in, garbage out? The same applies to AI. Its effectiveness hinges on the quality and accessibility of the data it can process. That’s why storage is such a critical component in the AI era. Your customers not only need to ensure long-term retention of historical data but also prepare for future scalability as data volumes continue to surge. And let’s be clear: slow, traditional storage simply doesn’t cut it anymore. To meet the performance demands of AI, companies need ultra-low latency access powered by NVMe flash, along with scale-out storage architectures that can handle exponential growth. The priority is finding storage solutions that are not only fast, but also flexible and easy to scale, ensuring they can support AI workloads now and as they evolve.
Beyond Hardware: The Software Layer That Brings AI to Life
While powerful servers and high-performance storage form the backbone of AI infrastructure, it’s the software stack that can really unlock value from data. For AI to deliver meaningful results, businesses need more than just compute and capacity, they need intelligent platforms that manage, move, and prepare data for advanced analytics and machine learning workflows.
This is especially true when dealing with vast amounts of unstructured data spread across edge, core, and cloud environments. Without the right data orchestration tools, organisations struggle to make this information accessible and usable for AI training and inference. By automating the process of discovering, organising, and delivering distributed datasets, solutions can enable teams to put all their data to work, regardless of where it lives.
Similarly, infrastructure platforms designed for edge environments are becoming essential, as AI moves closer to where data is generated. These tools provide the agility and resilience required for real-time processing at the edge, while still maintaining integration with centralised AI pipelines.
And then there are software-defined architectures that help organisations tap into the 90% of data that typically remains dark and unused. With the right approach, businesses can automate iterative learning, streamline model training, and fuel a continuous cycle of innovation, all while building a sustainable competitive advantage.
In short, the path to successful AI adoption runs through software. It’s the layer that transforms infrastructure into intelligence.
The Time is Now
The AI revolution is happening now, but without the right infrastructure in place, your customers risk falling behind. By investing in AI-ready servers, scalable storage, and integrated software stacks, companies can unlock the full potential of their data and position themselves to lead in the era of intelligent transformation.
Explore our AI-ready infrastructure portfolio and discover how we’re helping organisations turn ambition into execution.