Enterprise networks are undergoing a significant transformation driven by artificial intelligence (AI), which alters how data flows and creates new networking demands. AI traffic requires high bandwidth, ultra-low latency, and minimal packet loss for effective model training. Real-time information exchanges pose additional challenges, where even millisecond delays can hinder performance.
Gartner forecasts global AI spending will increase by 47% by 2026. McKinsey & Company reports that 88% of organizations utilize AI in at least one function, though nearly two-thirds remain in pilot testing or experimentation phases.
AI tools like Microsoft Copilot and ChatGPT Enterprise are reshaping data movement across enterprise networks. A report from Cisco Systems and Foundry predicts that AI will triple enterprise network traffic within three years, but only 15% of organizations have networks flexible enough to support AI at scale, based on Cisco’s AI Readiness Index 2025.
Taranvir Singh, a research manager at IDC, states that networks are evolving from basic connectivity to crucial components of the AI stack. “It needs to be seen not as a basic connectivity pipe but as an intelligent fabric ready to support identity-based authorization, policy enforcement, and optimization at scale,” he said.
AI workloads encompass a variety of sources across hybrid and multicloud environments, necessitating networks that maintain security, low latency, and performance visibility. Deepu Komati, Lead Engineer at HCL America, emphasizes that AI has refocused IT teams on delivering low-latency access to distributed AI services instead of merely providing connectivity.
Komati points out that AI workloads create bursty traffic and depend heavily on cloud APIs, leading to network bottlenecks primarily caused by latency and congestion. The challenge extends beyond bandwidth, as many IT teams struggle with establishing visibility and control over AI traffic. “AI tools are often introduced through browser applications, SaaS platforms, embedded copilots, and third-party APIs, which makes their traffic difficult to distinguish from ordinary cloud activity,” she says.
According to IDC’s 2026 Worldwide AI in Networking Special Report, significant barriers to AI projects include security, automation, and networking skills. “Business-critical agentic AI interactions traverse different applications, APIs, and variable data sources hosted across cloud and on-premises environments. Enterprises require end-to-end visibility and control,” Singh stated.
EMA’s study highlights security risks and budget constraints as key networking challenges for AI. Shamus McGillicuddy, Vice President of Research for Network Infrastructure at EMA, asserts that successful enterprise AI technology investments depend on robust network infrastructure.
CIOs and network leaders are urged to modernize networks in conjunction with compute and data infrastructure to prepare for AI. Singh suggests prioritizing unified, programmable networking platforms designed for high-performance and low-latency connectivity. He encourages networking teams to collaborate more closely with platform engineering, DevOps, and DevSecOps.
Komati identifies three priorities for IT teams in the next two to three years: enhancing end-to-end observability, modernizing network architecture with intelligent traffic management, and fostering collaboration among networking, security, data, and AI teams. “The goal should not be to increase bandwidth blindly,” she said. “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.” As AI evolves from experimental use to production, network readiness will hinge on visibility, resilience, and intelligent traffic management, rather than solely bandwidth.
