Artificial intelligence (AI) is reshaping industries at a breathtaking pace. From predictive analytics in healthcare to autonomous operations in manufacturing, AI is rapidly becoming an integral part of modern business operations. While innovations in data centers, GPU clusters, and algorithms are often in the spotlight, there’s a critical foundation that enables AI to reach its full potential: the network infrastructure, especially the Wide Area Network (WAN).
AI’s effectiveness relies not just on processing power or data science, but on the ability to reliably, securely, and efficiently connect data, users, and applications—wherever they reside. Traditional WAN architectures, however, were never designed to handle the distinct demands of AI workloads—such as real-time inference, massive data transfers, ultra-low latency, and distributed processing across the globe. As organizations accelerate their adoption of AI, they face new network challenges that must be addressed to unlock the full promise of intelligent operations.
In this blog post, we’ll explore five emerging WAN trends that are building the foundation for the AI era. These trends are critical for ensuring optimal performance, security, and agility in AI-driven environments. NetOps teams, IT leaders, and business stakeholders alike need to understand these trends to future-proof their networks and maximize the value of AI investments.
Trend 1: The Internet is Your New WAN—SASE Secures the Frontier
Enterprise WAN architectures are now built with the Internet as the primary transport for business-critical and AI-driven workloads. This isn’t just a shift from MPLS or private circuits—it’s about enabling a WAN fabric that is elastic, cloud-centric, and optimized for distributed, latency-sensitive AI applications. The Internet’s global reach and flexibility make it possible to connect users, devices, and workloads across diverse environments with the agility modern organizations require.
SD-WAN has evolved into a policy-driven overlay that dynamically adapts to real-time network conditions. For AI workloads—which demand unpredictable bandwidth, low latency, and distributed data access—SD-WAN’s intelligent path selection and application-aware routing are key to maintaining performance.
Relying on the Internet for WAN transport also introduces new security considerations. Secure Access Service Edge (SASE) meets this challenge by converging SD-WAN, next-generation firewall, CASB, and zero-trust network access (ZTNA) into a unified, cloud-delivered solution at the WAN edge. This integration ensures consistent security, seamless hybrid access, and optimal AI workload performance no matter where users or resources are located.
For AI operations, SASE provides the necessary foundation to keep distributed workloads secure and performant—aligning network and security policies with the demands of intelligent applications.
Visibility Where SLAs Don’t Exist
While using the Internet brings agility, it also introduces unpredictability. The Internet lacks traditional SLAs, and SASE deployments often involve multiple vendors and complex paths, making it difficult to pinpoint performance issues or validate security policies. Cisco ThousandEyes delivers end-to-end visibility across the Internet, monitoring ISP performance, cloud connectivity, and SaaS application reachability. The platform validates SASE effectiveness by showing performance from the edge to the cloud/application, so you can see if your unified network and security stack is truly delivering the experience you expect.
Trend 2: Zero-tolerance for Lag—Building the AI-Optimized WAN
AI workloads are fundamentally different from typical enterprise applications—they are "performance-critical" and unforgiving of even minor delays. Key metrics include ultra-low latency, minimal jitter, bursty traffic flows, and high throughput. Whether it’s edge inference, real-time video analytics, or rapid data fetches for complex API calls, the margin for error is slim.
Unlike traditional applications, where a second or two of delay might be acceptable, AI workloads can suffer severely from even subtle lags or glitches. The consequences? Degraded model accuracy, sluggish real-time responses, and ultimately, poor user and business outcomes.
To meet these stringent requirements, WANs must be engineered or optimized for deterministic performance. This means deploying advanced Quality of Service (QoS), intelligent traffic steering, AI-optimized network paths, and sometimes even specialized hardware at the edge.
Pinpointing Performance Bottlenecks
Pinpointing the source of subtle latency or intermittent jitter across complex, multi-domain paths is challenging. ThousandEyes precisely measures latency, jitter, and packet loss at every hop, including over the public Internet and cloud environments. This granular visibility enables NetOps to identify and address the exact points of performance degradation, and can help you ensure the low-latency, low-jitter environment that AI workloads demand.
Trend 3: AI for NetOps—Smarter WAN Management Through Automation
As WANs become more complex and critical, traditional reactive monitoring and manual troubleshooting are no longer sufficient. The shift is on—from reactive monitoring to proactive, automated assurance powered by AI.
AI-augmented WAN operations use artificial intelligence for diagnosis, root-cause analysis, anomaly detection, predictive insights, and automated recommendations, all with the goal of reducing mean time to resolution (MTTR) for complex issues. The next evolution is agentic operations, where AI systems move beyond diagnosis to initiate automated remediation, paving the way for self-healing networks and freeing NetOps teams to focus on strategic initiatives.
Feeding AI with Actionable Network Intelligence
For AI-powered operations to deliver value, they need high-fidelity, real-time data from across the entire network path, including all external dependencies. ThousandEyes provides the foundational, high-quality data—performance metrics, path visibility, dependency mapping, and BGP routing insights—that AI/ML engines require for accurate diagnosis and prediction. This data can flow directly into automated workflows and AI platforms, enabling intelligent remediation and helping detect problems before they impact users or AI workloads.
Trend 4: Distributed Intelligence—The Branch WAN Reimagined for Edge AI
AI is no longer confined to data centers—it’s increasingly being deployed at branch offices and the very edge of the network. Use cases like localized AI for security cameras, IoT analytics, smart retail, and edge compute are becoming the norm.
However, most branch office WANs were never designed to support such demanding workloads. Common issues include insufficient bandwidth, high latency, limited local processing power, and security vulnerabilities.
To support distributed AI, branch WANs must evolve to provide enhanced local processing, direct Internet access, robust security, and efficient synchronization with central AI platforms.
Unifying Edge Visibility
Gaining visibility into the performance of AI workloads at hundreds or thousands of branch locations—each with its own local ISP and network conditions—is a daunting task. ThousandEyes monitors performance and connectivity from each branch to the cloud, SaaS, and internal resources, simulating user and application paths to quickly identify issues affecting distributed AI workloads. This enables high performance and quality user experience, no matter where the branch or edge is located, and provides much-needed visibility into the critical "last mile."
Trend 5: Digital Experience as the Ultimate Metric
In the AI era, enhanced WAN visibility is an imperative—not a luxury. AI applications often involve complex chains of dependencies that stretch across on-prem, cloud, SaaS, and the public Internet. This makes end-to-end visibility across the LAN, WAN, Internet, and cloud absolutely essential.
Traditional network metrics like uptime and bandwidth are no longer enough. The true measure of WAN performance is the quality of the digital experience—for both users and AI applications. NetOps must proactively identify and resolve issues that may impact application performance, even when there’s no outright outage, to ensure seamless operation of AI-driven services.
Experience-centric Assurance
Achieving true end-to-end visibility and measuring the actual digital experience across multi-cloud, multi-ISP environments is where ThousandEyes excels. By collecting data from multiple vantage points—including agents, endpoints, and cloud services—ThousandEyes delivers direct, actionable insight into digital experience. NetOps teams can see precisely how network performance impacts users and AI applications, and quickly pinpoint the root cause of any degradation, wherever it lies in the chain.
Charting Your Path to an AI-ready Network
The AI era is redefining what’s required from WAN architectures. The five trends we’ve explored—embracing the Internet as the new WAN, optimizing for ultra-low latency, using AI for smarter NetOps, reimagining the branch for edge AI, and measuring success by digital experience—are all essential to building a network foundation capable of supporting intelligent operations.
For NetOps teams, the call to action is clear: Adapt your strategies, invest in modern WAN technologies, and embrace AI-driven management approaches. The future is now—and the right WAN foundation will ensure you’re ready for whatever comes next.