Network management has been growing more challenging for several years, as networks grow more complex and uptime demands become more insistent, and the pace of change is only accelerating. Research has shown that 80% of data center managers have experienced downtime of some kind in the past three years, with networking issues proving the single biggest cause.
Artificial intelligence (AI) could be the solution. AI tools are already transforming productivity and resilience in numerous industries, and could bring similar improvements to network management, as Gartner is beginning to acknowledge. According to a survey by Comcast Business, 85% of IT leaders believe that AI networking tools can meet their organization’s needs, with 82% agreeing that human intervention will be needed less as time goes by.
AI network management tools promise a number of benefits for overworked IT teams and the organizations that rely on them, including increasing network uptime and performance, reducing costs, and improving operational efficiency. Read on to learn the 7 top benefits of AI network monitoring and operations.
Network management teams are under pressure to ensure constant uptime without any interruptions, but increasingly complex networks make it harder to deliver on this demand. AI network monitoring tools can crunch historical performance data to understand the true baseline of each individual network, and use algorithms to detect the early signs of potential network failures.
This allows NetOps teams to work proactively to address the issue before it crashes or degrades the network, thereby reducing downtime and performance issues. NetOps personnel also use these alerts, together with insights from AI data analytics, to schedule vital maintenance or corrective activity for off-peak hours, when it causes minimal disruption to users and reduces costs to the organization.
AI network monitoring and operations goes beyond increasing uptime to optimizing the entire user experience. Comcast Business reports that IT leaders believe network performance optimization is the IT function that would most benefit from automation, alongside security threat detection.
AI-powered systems can continuously monitor network traffic and detect anomalies, intrusions, or unusual patterns in real time. This enables network managers to identify and resolve bottlenecks and slowdowns before they affect network performance.
By tracking patterns in real-time demand and conditions, AI can dynamically adjust routing, bandwidth allocation, and quality of service (QoS) parameters, leading to more efficient resource utilization and improved user Quality of Experience (QoE). AI tools that monitor end user experience on the application level can track every KPI, to detect and predict issues for all types of application performance. AI can even tailor network services to individual user preferences and needs, enhancing user satisfaction and engagement.
IT teams can also use AI tools to explore different network models and investigate the impact of different scenarios and circumstances on their networks. With AI, it’s possible to simulate situations like rapidly scaling up availability or reacting to cyberattacks. This empowers network managers to evaluate different options and configurations, and improve network resiliency under all conditions.
Automated monitoring and analysis tools also use the masses of data produced by network sensors and devices to predict future network capacity requirements, allowing IT teams to plan the necessary network upgrades, expansions, or optimizations to future-proof the network.
IT teams are often swamped by customer support tickets. They need to read and respond to each one, even if they receive dozens relating to the same issue, which holds them back from tasks like planning for future needs or improving network robustness.
AI monitoring and operations tools that detect the first signs of impending network issues or failures also help stem the flow of tickets. When IT teams receive early alerts, they’re often able to resolve the issue before users notice any resulting change in network performance, thereby removing the need for them to open a support ticket.
As networks become more complex, it becomes more difficult for network management teams to gain visibility into the networks and understand the causes of network issues. This is particularly problematic for evasive issues that are not easy to track.
AI monitoring and operational tools give IT teams the ability to pinpoint root causes, supporting faster troubleshooting and reducing the resources needed to resolve issues such as slow video meetings etc . More advanced tools can even propose solutions faster and more accurately than human IT teams, and some can resolve them autonomously without requiring manual intervention.
AI can analyze and diagnose network problems, often resolving them autonomously or providing actionable insights to IT teams. This reduces the need for manual intervention, leading to faster issue resolution and improved uptime
Using traditional network monitoring solutions, false positives often make up a significant percentage of alerts, causing IT teams to waste time and resources on unnecessary activities. AI can significantly reduce false positives or non-important notifications and prioritize alerts according to their urgency, helping IT teams use their capabilities more efficiently. Moreover, correlating alerts stemming from a single root cause both reduces noise as well as provides more accurate information on how to resolve the issue.
According to TechTarget’s Energy Strategy Group, 61% of security leaders anticipate that AI will improve issue detection accuracy.
Some AI tools take things a step further and automate alert responses, for example, closing an open port, adjusting access permissions, or isolating devices or systems as part of threat mitigation activities. Other AI platforms automate tasks using procedures like intent-based configuration to mass configure multiple networks according to intent, even if they include equipment from different vendors.
It’s estimated that in the last three years, close to 40% of organizations have had a major downtime incident that was caused by human mistakes. Automating tasks can help slash the risk of manual error, as well as easing the workload for IT teams, leading to reduced operational costs and increased efficiency in network management.
It’s tough to find skilled network management employees, and as networks constantly evolve, it’s becoming a never-ending challenge for IT teams to maintain knowledge and expertise. AI networking automation can carry out basic tasks without manual instruction, making it possible to reduce the burden on teams and field personnel that do not have such deep network knowledge.
It’s not practical for IT teams to continue to grow in size to support the growing demands on their time and capabilities. AI automation can step into the breach by simplifying and streamlining the workload. With fewer support calls, prioritized alerts, more resilient networks, and greater operational efficiency, it becomes possible for network management teams to do more with less – or the same – resources.
Network complexity is still increasing, uptime and user experience demands are still growing, and IT teams can’t keep up. AI network monitoring and operational tools make it possible for network managers to meet all their responsibilities and satisfy all their customers, without burning out or running out of budget.