IT Teams Lose 11 Hours a Week to AI Networking

IT teams now spend 11 hours a week fixing cloud connectivity problems, a new study finds, driven by surging AI workloads.

A new survey highlights a growing pain point for IT teams: the hidden networking costs of artificial intelligence. According to research from DE-CIX reported by Computer Weekly, enterprise IT departments are now spending an average of 11 hours per week troubleshooting cloud connectivity issues. The study, which surveyed over 400 IT and infrastructure decision-makers in the US and UK, points to the explosive growth of AI workloads as the primary cause. This finding creates a stark contrast with the confidence of the respondents, as 96% stated they believe their enterprise networks are fully prepared to handle the demands of AI and cloud-based applications.

The problem isn't simply about needing more bandwidth. AI workloads, particularly for training large models and running real-time inference, generate unique and demanding traffic patterns. Unlike predictable, steady streams of data from traditional enterprise applications, AI traffic is often bursty, high-volume, and extremely sensitive to latency. Data needs to move rapidly and reliably between on-premise data centers, various cloud providers, and edge locations. When legacy network architectures, designed for simpler north-south traffic, are hit with these complex east-west and hybrid patterns, they can create bottlenecks. This leads to packet loss, high jitter, and connection timeouts that disrupt AI processes and force engineers into hours of manual diagnosis and repair.

For CTOs and IT leaders, this 11-hour weekly time sink represents a significant operational drag and a hidden tax on innovation. It translates to more than one full workday per week, per team, dedicated to firefighting instead of strategic projects. This directly impacts team productivity, increases the risk of employee burnout, and can delay the deployment of critical AI-driven products and services. The disconnect between the 96% who feel prepared and the reality of constant troubleshooting is a major blind spot. It suggests that many organizations have underestimated the foundational infrastructure requirements for their AI strategies, leaving their technical teams to manage the consequences.

The key takeaway for businesses is that network infrastructure cannot be an afterthought in the race to adopt AI. The study underscores that simply procuring cloud compute and AI software is not enough; the underlying connectivity is a critical component for success. Companies must now proactively evaluate and invest in modern networking solutions capable of handling AI's demands, such as direct cloud interconnects, software-defined networking, and enhanced observability platforms. Failing to align infrastructure capabilities with AI ambitions will result in a poor return on investment, as the gains from AI are eroded by operational friction, project delays, and escalating support costs. This report serves as a warning that true AI-readiness extends deep into the network stack.

The 11-hour weekly loss in productivity is a significant hidden cost of AI adoption. It reveals a critical gap between how prepared IT leaders think their networks are and the reality of supporting demanding new workloads, impacting project timelines and team morale.

This operational drag directly affects the ROI of expensive AI initiatives. Businesses that fail to modernize their network infrastructure alongside their AI strategy will face escalating costs, project delays, and a competitive disadvantage as they struggle to deploy and scale new technologies.