Core Insights - AI is transitioning from theory to practice in the UK, significantly impacting employee work and economic growth, but its full potential relies on robust network infrastructure as well as high-performing AI models [1] - Over half of global organizations are attempting to run advanced AI on legacy networks, leading to bottlenecks, increased costs, and limited AI effectiveness [2] - A survey indicates that 94% of enterprises experience network limitations affecting their AI projects, highlighting the need for businesses to identify and upgrade network shortcomings [3] Network Requirements for AI - An AI-ready network must support high bandwidth to handle large data volumes effectively, avoiding queues [6] - Low latency is essential for AI to provide quick and accurate responses, ensuring timely decision-making [7] - Resilience is crucial for networks to withstand stress and recover quickly from issues, providing a steady and predictable experience [7] Building AI-Ready Infrastructure - Companies can enhance their existing infrastructure to become AI-ready by focusing on high ROI use cases, such as employee assistants and fraud detection [8] - It is important to trace the data journey from the user or device to the application, measuring round trip times and identifying slow points [8]