Generative AI applications are spreading quickly across enterprise environments, and traditional network architectures, originally built for predictable, asymmetric traffic, are under strain. These new workloads introduce highly dynamic, bursty, and unpredictable traffic patterns that legacy systems were not designed to handle. This shift presents challenges for network performance, stability, and resource allocation, especially as more AI-enhanced and AI-generated services come online.
As a result, enterprises now face a dual imperative: ensure consistent application performance while adapting to a rising volume of AI-driven data flows. According to Omdia’s data published by Network World, AI-related traffic, including both new AI applications and AI-enhanced tools, accounted for 39 exabytes of network traffic in 2024 alone. That figure is expected to grow as companies continue to expand their use of automation, analytics, and AI-powered solutions.
Solving this problem requires a smarter infrastructure that can sense what’s happening in the network, make decisions instantly, and adjust before network performance issues arise.
This article explores how VeloCloud’s AI-powered network architecture helps enterprises meet today’s network performance demands while preparing for tomorrow’s complexity.
Dynamic Multipath Optimization with AI at the Core
Network traffic no longer moves in predictable patterns in today’s hybrid and multicloud environments. For instance, in a typical enterprise setting, applications span public and private clouds, users connect from anywhere, and traffic conditions shift by the second. Add to that the rising volume of AI-generated and AI-powered data flows, and it becomes clear that static routing rules or fixed failover plans just don’t cut it anymore.
VeloCloud’s Dynamic Multipath Optimization ™ is built to handle this type of enterprise network environment. It works by continuously monitoring every available WAN link — broadband, 5G, MPLS, satellite and more — and analyzing their performance across key metrics like latency, packet loss, jitter, and throughput. Then, it uses AI to make routing decisions in real time. This means, if one path starts to lag, the system quietly reroutes traffic to a better-performing option without waiting for thresholds or outages. It happens automatically, based on live network conditions and application needs. It’s as if your vehicle’s GPS constantly updates your route, not just when there’s an accident, but the second the flow of traffic starts to change.
Smarter Traffic, Better Prioritization
As generative AI, agentic AI models and latency-sensitive applications become a larger part of workloads, it becomes critical for organizations to ensure that the most critical workloads get the right type of traffic at any given time without needing IT teams to constantly step in.
In response, many organizations are launching AI-focused networking initiatives, as highlighted in VeloCloud’s State of the Enterprise Edge report.
These efforts align with new research by Opengear, which found that 57% of network engineers expect their organizations to increase investment in AI for network management by more than 25% over the next two to three years. This surge reflects a growing push to meet the performance, bandwidth, and security demands of agentic AI workloads operating outside the traditional data center.
VeloCloud understands this yawning gap and addresses it through its AI-powered network architecture, which combines machine learning-based application recognition with automated, real-time policy adjustments. It continuously monitors traffic, identifies thousands of applications — even when encrypted — and dynamically allocates bandwidth based on performance demands and business priorities.
Say there’s a company-wide video meeting happening across global offices. VeloCloud automatically detects the traffic, understands its importance, and gives it top priority. At the same time, background tasks like system updates or file syncs are quietly given low priority. The interesting thing here is that these decisions happen in real time, without anyone needing to update policies or change settings manually.
That approach scales easily across thousands of users and locations. Whether you’re working in a branch office, accessing a model hosted in the cloud, or joining a call from home, the network responds to your needs without missing a beat.
IT teams benefit too because when you have fewer manual quality of service (QoS) adjustments to make, you can focus on higher-level tasks instead of chasing bottlenecks.
Security and Control of AI-Driven Networking
Enterprises deploying AI workloads need not only performance but also a strong security posture and precise control across their networks.
VeloCloud’s VeloRAIN architecture brings advanced AI networking features to VeloCloud SD-WAN, optimizing performance, security, and scalability for distributed and AI-driven workloads. It goes beyond simply combining SD-WAN, cloud security, and observability by adding capabilities like AI-driven application profiling, dynamic application-based slicing, and automated network operations. These features give IT teams real-time visibility into application usage and performance, so they can adjust policies as needed.
Security is further strengthened through Symantec SSE for VeloCloud, a cloud-based service designed to protect distributed AI applications. It uses machine learning to detect and block emerging threats, inspects encrypted traffic at scale, and includes inline CASB to manage risks from shadow AI.
Ready for Networks that Think Ahead?
VeloCloud is redefining network performance with AI at its core. Through intelligent traffic classification, predictive bandwidth allocation, and dynamic path optimization, it gives enterprise networks the tools to think and adapt in real time.
Explore how we use AI to optimize network performance to ensure your enterprise workloads run smoothly.
