The Ryder Cup golf tournament served as a real-world demonstration of how AI-ready networking is becoming crucial for managing large-scale events and enabling real-time intelligence. The event’s IT infrastructure, supporting nearly a quarter of a million attendees, leveraged a high-performance network and private cloud to aggregate and analyze data from sources like 67 AI-powered cameras.
Jon Green, CTO of HPE Networking, points out that robust networking is the bedrock for successful AI implementations. Businesses are increasingly adopting distributed, real-time AI, and their networks must be able to handle the resulting data deluge with speed and efficiency. The Ryder Cup deployment exemplified a network specifically designed for AI, requiring ultra-low latency, lossless throughput, specialized equipment, and scalability. The event utilized over 650 WiFi 6E access points and 170 network switches in a two-tiered architecture to ensure continuous connectivity and feed a private cloud AI cluster.
This trend extends far beyond event management. Enterprises are re-evaluating their architectures to support physical AI applications like autonomous vehicles and AI-driven factories. Edge-based AI clusters are gaining momentum, processing information closer to the source to improve speed and security, and enabling a shift of operations from the cloud back to on-premise environments.
Moreover, AI is revolutionizing network management itself. AIOps (AI-driven IT operations) uses AI models to analyze vast amounts of network telemetry data, identify patterns, optimize performance, and automate tasks. This paves the way for ‘self-driving networks’ that can handle repetitive tasks, freeing up network engineers to focus on more strategic initiatives. As data movement becomes ever more critical for businesses, AI-powered networking is emerging as a fundamental building block for scaling AI initiatives effectively.