Network readiness is becoming the next barrier to AI adoption

AI barriers

United Kingdom, Apr 23, 2026

As organisations accelerate AI deployment, many are discovering their networks were never designed to support the scale and performance these technologies require.

Authored by Richard Simmons, Networking Solutions Director, Logicalis UKI.

The challenge of adopting AI is no longer proving that the technology works, but ensuring that the infrastructure supporting it can keep pace with the scale and speed of these new workloads.

Recent research among UK CIOs suggests that nearly four in five organisations believe infrastructure limitations are already slowing their ability to adopt and scale AI, yet as organisations begin integrating AI into core business processes, many are discovering that their networks were never designed to support the data volumes, performance demands and connectivity requirements these technologies create.

AI workloads depend on the rapid movement of large volumes of data between data centres, cloud platforms and increasingly distributed edge environments, placing significant pressure on connectivity, latency and network performance.

For UK CIOs, this highlights a growing priority that AI success will depend not only on the tools organisations deploy, but on the strength of the network foundations supporting them.

Why AI workloads are reshaping network demands

AI workloads are fundamentally different from traditional enterprise applications. They are data-intensive, distributed and dynamic, requiring real-time processing across multiple environments. Training models, running inference and supporting AI-driven services depend on continuous data exchange between systems, often across cloud, data centres and edge locations, creating sustained pressure on bandwidth and increasing sensitivity to latency.

Unlike traditional workloads, AI environments generate unpredictable traffic patterns and require consistent high performance to maintain accuracy and responsiveness. This places additional strain on networks that were not designed to support constant, large-scale data movement or fluctuating demand.

Even small delays can impact model performance or user experience, particularly in real-time analytics or customer-facing AI services, making network performance a direct contributor to business outcomes and overall service reliability.

The limitations of legacy infrastructure

Legacy infrastructure presents several limitations in this context. Many enterprise networks were built for predictable traffic patterns and centralised architectures, not for the east-west data flows that AI workloads generate at scale. Capacity constraints, fragmented architectures and limited visibility can all become significant bottlenecks over time, particularly as data volumes continue to grow and become more complex.

Older networking models also lack the flexibility required to support hybrid and multi-cloud environments, which are now central to most AI strategies and digital transformation initiatives. As organisations attempt to scale AI into core operations, these limitations can quickly translate into increased costs, complexity, and operational risk, while also slowing innovation, agility, and overall time to value.

This is reflected in broader CIO concerns, as many are balancing innovation with operational responsibility and risk, often without fully developed frameworks, governance models or internal capabilities to support that transition effectively, consistently and at scale across the organisation, while maintaining resilience and performance standards.

Why connectivity performance, observability and modernisation matter

Connectivity performance and observability are becoming critical components of AI readiness, but they cannot be considered in isolation from network modernisation. Organisations need to understand not only whether their networks are functioning, but also how they perform under AI workload demands and whether they are built for scale.

Real-time visibility into traffic flows, latency and application performance across environments enables organisations to identify bottlenecks and optimise performance. In AI environments, where workloads shift rapidly, this insight is essential for maintaining consistent performance and user experience.

Observability also supports stronger governance and control, helping CIOs ensure AI deployments remain reliable, secure and aligned with business objectives as these systems become embedded in operations. To help with this, network modernisation requires a strategic approach and organisations should begin by assessing infrastructure against AI demands, identifying gaps in capacity, performance and visibility, and prioritising investments.

This includes high-performance connectivity, software-defined networking and integrated observability platforms that provide end-to-end insight. Supporting hybrid and multicloud architectures is important, as AI workloads rarely operate in one location, requiring networks that enable consistent performance and secure connectivity across cloud platforms, data centres and edge environments without introducing latency or complexity.

Building the foundation for scalable AI

Ultimately, the organisations that succeed with AI will be those that recognise infrastructure as a critical enabler of value rather than a secondary consideration within their wider digital strategy. The technology itself may be proven, but without the right network foundation, its full potential cannot be realised at scale across the enterprise.

As CIOs continue to navigate the pressures of rapid adoption, rising expectations and limited resources, the focus must shift towards building networks that are resilient, observable and designed for the demands of AI and future innovation.

Only then can organisations move from experimentation to enterprise-wide impact with confidence and long-term success.

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