Enterprise networks are undergoing a significant transformation due to artificial intelligence (AI). The shift is characterized by continuous, high-volume traffic generated during AI model training, which demands high bandwidth, ultra-low latency, and minimal packet loss. AI inference brings challenges as real-time data exchanges require immediate responses, where millisecond delays can negatively impact performance.
Gartner forecasts that global AI spending will grow by 47% by 2026. Meanwhile, a report from McKinsey & Company indicates that 88% of organizations currently utilize AI in at least one business function, although nearly two-thirds remain in pilot projects or experimentation stages.
A 2026 report from Cisco Systems and Foundry anticipates AI will triple enterprise network traffic within three years. However, only 15% of organizations possess networks flexible enough to support AI at scale, according to Cisco’s AI Readiness Index 2025. Taranvir Singh, research manager for network infrastructure and services at IDC, emphasizes that the network must evolve from a basic connectivity role to an intelligent fabric capable of supporting identity-based authorization, policy enforcement, and optimization.
Deepu Komati, Lead Engineer at HCL America, notes that AI has altered the perception of enterprise networking. She states that AI assistants and copilots have shifted discussions from merely providing reliable connectivity to delivering consistent low-latency access to distributed AI services. AI workloads, which produce bursty traffic and depend on cloud APIs, are resulting in increased network bottlenecks caused by latency, congestion, and inefficient routing.
IT teams face the challenge of gaining visibility and control over AI traffic, which often merges with standard cloud activity. Komati points out that traditional network monitoring might identify the availability of a connection but fail to address why AI responses can be slow or incomplete. IDC’s 2026 Worldwide AI in Networking Special Report highlights security, automation, and networking skills as significant barriers to the successful implementation of AI projects.
Shamus McGillicuddy, Vice President of Research for Network Infrastructure at EMA, argues that robust network infrastructure will be critical for enterprises making investments in AI technology. He stresses the need for organizations to modernize data center and wide-area networks to accommodate AI workloads that span public clouds and data centers.
Organizations are encouraged to modernize their networks to align with AI advancements. CIOs should invest in unified, programmable networking platforms offering high performance and built-in security. IT teams should also collaborate across departments, including network, security, data, and AI teams, to improve infrastructure management.
Komati recommends three priorities for IT teams over the next two to three years: develop end-to-end observability connecting users, networks, cloud platforms, APIs, and AI applications; modernize architecture for intelligent traffic management and resilient cloud connectivity; and promote collaboration among teams to avoid operating in silos. She concludes, “The goal should not be to increase bandwidth blindly. It should be to build an adaptive network that can prioritize critical AI traffic, detect performance degradation, enforce data-governance policies, and scale as AI usage becomes embedded across the organization.”
