AI-driven network management is becoming a crucial component for enterprises as their networks grow increasingly complex alongside a surge in AI initiatives. By integrating generative AI with traditional automation methods such as scripting, machine learning, and robotic process automation, companies can streamline operations and enhance deployment effectiveness.
At Aflac, AI agents now play a vital role in managing security policies. Previously, updating and reviewing these policies could take hours and required detailed analysis by multiple personnel. Thanks to AI-powered policy automation agents from Airrived, Aflac can now implement rule changes significantly faster, achieving a 90% reduction in the time taken to alter policies, alongside a threefold cut in configuration errors.
Such automation doesn’t necessarily lead to job losses; rather, it allows companies to optimize existing resources. For instance, the efficiency gained at Aflac frees up approximately half of a full-time position, enabling existing staff to tackle other responsibilities.
Networking dynamics are shifting due to AI integration. A survey revealed that 93% of networking professionals view network automation as essential for managing change, with many seeking capabilities that can proactively prevent outages and automate vulnerability checks.
Even though many organizations have not fully automated their network management processes—around 30% have only partial automation—there is a widespread acknowledgment of generative AI’s potential benefits, particularly in improving compliance with security protocols.
As organizations adopt AI technologies, they face challenges such as accuracy and trust in AI functionalities. Research indicates that over 70% of professionals are cautious regarding AI’s reliability for network operations. This concern limits the types of tasks being considered for automation and pushes many companies to focus on less critical functions.
Notably, larger enterprises are progressing in AI adoption more swiftly. Research highlighted that around 22% of medium to large-sized companies are already using AI for network automation, compared to just 3% of smaller companies.
Companies like Zscaler, which operates a vast network and employs around 100 people for network management, have started leveraging generative AI to decrease outage detection and response times dramatically.
N-able, another proactive user of AI in network automation, has significantly improved its incident response times, reducing root cause identification from hours to seconds by utilizing AI technologies, including large language models (LLMs).
While the industry progresses, the integration of AI in network management is not without barriers. Multiple tools and vendors complicate network performance management, and many smaller organizations struggle without dedicated automation staff or the necessary skills to implement AI effectively.
Despite these challenges, the call for enhanced network automation solutions is growing, with analysts predicting a continued rise as businesses increasingly rely on agile methods and automation to streamline infrastructure delivery. As AI technologies evolve, the potential for widespread application in network management continues to expand, promising improvements in performance, compliance, and operational efficiency.
