Network operations teams are increasingly looking towards artificial intelligence (AI) as a solution to automate their Day 2 operations. This need arises from persistent challenges such as workforce shortages and limited tools for operational efficiency in network monitoring, troubleshooting, and change management.
Recent insights from Enterprise Management Associates (EMA) reveal that 52% of IT organizations face difficulties in hiring qualified networking professionals. This talent shortage is compounded by cautious business strategies that emerged post-pandemic, where companies prefer to maintain lean teams rather than over-hire and face layoffs. Network operations centers (NOCs) are particularly affected, with many opting to enhance automation efforts to offset the lack of staffing. Notably, 79% of IT professionals surveyed regard automating Day 2 operations as a top priority.
The push for automation is driven not only by staffing issues but also by the desire to improve key performance metrics, such as reducing mean time to repair and enhancing the overall network experience. Traditionally, network teams have utilized scripted processes and playbooks to address recurring issues, but the advent of AI has presented significant opportunities to further elevate automation capabilities.
According to the findings, 62% of IT organizations intend to leverage AI for network management, enabling higher levels of automation. Major infrastructure vendors are already integrating AI-driven automation features into their platforms, and many network observability vendors are following suit.
Despite the promising landscape, there are significant barriers to success in automation endeavors. A staggering 46% of IT professionals cite skill gaps within their teams as the primary obstacle. This reliance on scripts and predefined processes necessitates resources who can develop these solutions. Additionally, organizations often struggle with adopting vendor automation tools due to limited internal resources to evaluate and implement new tools effectively.
Other challenges include technical limitations of existing tools, data quality and visibility issues, and risk aversion that prevents teams from entrusting AI to manage critical changes. Often, manual verification is preferred, leading to missed opportunities for efficiency despite the availability of advanced technology.
To foster success in automating Day 2 operations, network teams must prioritize these initiatives within their overall operational strategy. Leadership engagement and budget allocation will be crucial for sustaining automation efforts and achieving lasting improvements in network operations.
