The hidden backbone of artificial intelligence success isn’t algorithms or datasets — it’s connectivity. Whilst organisations race to implement sophisticated AI models, many overlook the foundational network infrastructure required to support these compute-intensive workloads.
Recent HPE research reveals that 93% of IT leaders believe their networks are ready for AI, yet fewer than half understand the nuanced networking requirements across different phases of the AI lifecycle.
The great confidence disconnect
Despite substantial investment in AI technologies, networks rank just fifth on IT leaders’ priority lists. That positioning reflects a concerning disconnect between perceived readiness and technical reality.
Across each stage of the AI journey — from data acquisition to model training and inference deployment — different network capabilities become critical.
Without purposeful network design, even the most ambitious AI initiatives risk stalling before delivering meaningful business value.
Three distinct phases, three distinct requirements
Data acquisition represents the foundation of any AI initiative, requiring networks that function as reliable on-ramps for capturing high-quality information.
Only 48% of surveyed IT leaders demonstrated a full understanding of networking needs during this phase, leaving a majority potentially unprepared.
Edge connectivity becomes particularly important here, as organisational data increasingly originates and often needs processing at peripheral locations without first traversing centralised infrastructure.
Model training introduces entirely different demands, requiring low-latency, high-performance connections between massive GPU clusters.
The network must deliver optimised, predictable performance whilst supporting multiple simultaneous workloads. Bottlenecks at this stage directly impact training time, accuracy, and cost — yet barely 39% of leaders fully comprehend these specialised needs.
Inference deployment, where AI models deliver actual business value, requires networks that seamlessly connect edge devices, on-premises systems and cloud resources.
Models may run anywhere based on business requirements, with networks needing to ensure proper connectivity performance or risk compromising the efficacy of inference results through latency issues or packet loss.
Beyond connectivity: essential network imperatives
Forward-looking organisations recognise four key imperatives for successful AI networks. Firstly, broad-based infrastructure supporting diverse connectivity options allows data collection from varied sources.
Beyond traditional wired connections, integrated Wi-Fi (including advanced standards like Wi-Fi 6E) and private 5G create comprehensive coverage for IoT devices and remote locations.
Secondly, unified visibility eliminates infrastructure silos through edge-to-cloud management integration. That enables consistent security policies, centralised control, and comprehensive monitoring across all network segments.
Automation represents the third imperative, employing zero-touch service management to simplify network operations.
Standardising configuration and management through intelligent tools helps organisations reduce operational overhead whilst maintaining high service levels.
Finally, security must be integral rather than bolted on — especially concerning given 94% of leaders believe AI will worsen the threat landscape.
Zero Trust principles and SASE (Secure Access Service Edge) frameworks help restrict access to sensitive systems whilst ensuring data integrity throughout the AI lifecycle.
