Enterprise network operations teams are increasingly struggling to keep pace with emerging demands as AI workloads enter their environment. A recent study by Enterprise Management Associates (EMA) revealed that only 31% of IT professionals feel their organization’s network operations strategy is completely successful, a decline from 42% just two years ago. This trend underscores the significant challenges facing network teams, including talent shortages, an overload of tools, and the complexity of hybrid and multi-cloud environments.
Shamus McGillicuddy, vice president of research at EMA, highlighted that network operators recognize the need for improvement but lack the necessary support from their organizations. Budget constraints, inadequate tools, and insufficient automation are key factors hampering their efforts to adapt to the growing demands of AI integration.
The current state of network operation centers (NOCs) shows persistent tool sprawl, with many organizations relying on multiple monitoring and troubleshooting tools without significant improvement in operational success. While 58% of network issues are detected before impacting users, only 37% of alerts indicate actual problems. A considerable amount of time—29% of professionals’ workdays—is spent troubleshooting issues, while staffing shortages exacerbate the situation.
Furthermore, the talent crisis in network technology is worsening, with the proportion of organizations finding it difficult to hire skilled experts rising from 26% in 2022 to 52% today. This skills gap is particularly pronounced in senior and mid-level roles, where demand for expertise in cloud, security, and automation is highest.
Amid these challenges, the push to automate day-two operations—such as ongoing problem detection, diagnosis, and remediation—has emerged as a top priority. Seventy-nine percent of respondents to the EMA study rated the automation of such tasks as critical, with a strong preference for AI-driven tools capable of autonomous decision-making.
Organizations are also navigating the complexities of hybrid and multi-cloud networks, with 69% of surveyed teams operating in such environments. However, only 36% consider their management strategies effective, primarily due to a lack of cohesive visibility across distinct cloud providers and on-prem environments.
Interestingly, about 47.7% of respondents indicated the presence of AI workloads on their networks. Yet, only 35% believe their current network observability tools are equipped to manage these workloads effectively. Enhancements sought by network teams include AI-powered troubleshooting and proactive alert systems, indicating a strong desire to optimize performance amid rising AI demands.
Successful teams distinguish themselves by holding observability data to high accuracy standards and using AI-driven management tools. They tend to emphasize integration and interoperability across tools, rather than merely aiming for consolidation. As AI networking evolves, McGillicuddy encourages organizations to engage with vendors about their readiness to support these transformative needs.
For further insights on AI transformation in enterprise connectivity, refer to the EMA’s Network Management Megatrends 2026.
