10 Steps to Future Proof Network Infrastructure for AI Workloads Artificial intelligence is transforming industries, but are our networks ready for this seismic shift? AI's growing demands are pushing network performance to its limits. From massive data volumes to unpredictable traffic patterns, our infrastructure is being tested like never before. Take Meta, for example. In 2022, they faced an unexpected tsunami of AI data traffic, driven by their expanding GPU infrastructure. Their network strategists recognized that their networks weren't ready. The real question is, are you?

Figure 1: AI’s demands on today’s networks

Let’s explore 10 steps to optimize network performance across datacenter, ISP and cloud environments to ensure the business gets the ROI it expects from AI’s promises:

Fault isolation and alert suppression. Employing analytics that suppress the noise from downstream infrastructure events and alarms is a proven method to simplify troubleshooting and accelerate root cause analysis, particularly when AI workloads are disrupted.

Alarm noise reduction. When it comes to isolating the root cause of issues in modern networks, AI and advanced analytics undoubtedly hold promise—but this promise will only be realized when advanced capabilities are effectively tailored to the networking domain. When done successfully, these features can significantly enhance operational efficiency, speed up remediation, and support network team effectiveness.

Root cause analysis (powered by contextual data). Building on the previous steps, applying

analytics to the right data means you can expect intelligent answers. When implemented correctly, expect minimal troubleshooting efforts or even a ‘one-click alarm to root cause diagnosis’.

Baseline Thresholding always leads to predictive intelligence. When planning AI projects, these proactive measures should not be overlooked, especially since many enterprises implementing artificial intelligence for enhanced customer experiences may anticipate seasonal workload peaks.

Configuration compliance and automated roll back policies. Unplanned or unauthorized network device configuration changes are among the leading causes of network outages.

By correlating outages with configuration changes, you can expect to minimize the impact of unexpected downtime on operations, customers, or AI projects, and even prevent issues before they occur.

Topology analytics. Visualizing the entire global network landscape is always beneficial to operations teams; even when it’s a mix of legacy and modern multi-vendor technologies, ISP and cloud network paths, and hybrid Wi-Fi setups. With ‘one-click’ visibility into the health and

performance of your entire global network, you can confidently identify the root cause of managed or unmanaged network issues and assess their impact on branch sites, remote user productivity, or even the reliability of Gen-AI-driven prompt responses.

Site-to-site network deliver SLA visibility. Teams should be looking to enable active monitoring of the complete end-to-end network path now. This includes visibility

into transit and last-mile ISP performance to help IT quickly understand the root cause of performance issues. If that root cause is with the ISP, you can easily accelerate your mean time to insights and prove your innocence with granular performance metrics from inside the ISP network.

Real-time, end-to-end, hop-by-hop network path analysis. With end-to-end views into site-to-site, site-to-cloud, and cloud-to-cloud network paths for business critical services, teams can improve problem isolation and remediation times, and confidently network delivery performance by leveraging intelligent insights into unmanaged network devices.

App-aware network synthetic testing. While apps rarely go down these days, a deeper understanding of network delivery’s impact on application performance is required to support the demands of AI workloads. Synthetic transaction monitoring provides details for any

application by sending tests via scripts to mimic user interaction. App-aware network synthetic testing can help teams quickly answer the question “is it the app or is it the network?”

Active performance validation of ISP/cloud networks. With the widespread use of public networks now, network teams should adopt a “trust but verify” approach. Unlike in private data centers, we typically don’t have full administrative access to public ISPs or cloud infrastructure. AI workloads will undoubtedly be disruptive for some use cases and it will always be better to adopt capabilities to visualize, analyze and validate every hop in your AI workload network path to ensure success.

AI is here, and it's revolutionizing the way we work. However, its success depends on building resilient networks today to meet tomorrow's demands. In the world of AI, there is no room for buffering.