As the conversation and developments around AI rage on, its application in network performance management has also become a key focus area. With more enterprises than ever- and growing complexities to match- AI offers the promise of reducing the burden and stress of managing vast networks. By automating what was once manual, organizations can be more confident in perfecting every aspect of their digital touchpoints.
AI in network performance management centers on automating and enhancing network operations. At the core of this is Machine Learning (ML), a subset of AI. Network management generates vast datasets, and ML leverages this data with algorithms to predict anomalies- often surpassing human capabilities.
This empowers a network monitoring system to learn and adapt to new threats and changes in network behavior, strengthening the key pillars of network monitoring and management:
Before we dive into what AI enables, it’s worth looking back at how things have been in the traditional network monitoring world- where IT teams have often had to wrestle with limitations on a daily basis. Each of those pillars we just touched upon carried its own set of headaches.
Drowning in false positives: In traditional setups, spotting anomalies meant relying on static thresholds and endless alerts. Teams often ended up drowning in false positives, with genuine issues hiding in plain sight. By the time someone noticed, the damage was already done.
Fault detection without clarity in direction: Locating the actual fault was like finding a needle in a haystack. Logs were scattered, device alerts weren’t always consistent, and “where do we even start?” was a question no admin wanted to ask at 2 a.m.
Reactive approach in the absence of "predictive" capabilities: For many IT teams, prediction simply wasn’t in the picture. Monitoring was reactive- responding to breakdowns rather than anticipating them. Downtime was often the first “signal” that something was wrong.
RCA involved piecing together issues manually: Without intelligent correlation, root cause analysis was largely a manual affair. Multiple admins pieced together events, cross-checked logs, and relied on experience or guesswork. Resolution times stretched while SLAs loomed overhead.
Over-provisioning or poor allocation due to poor capacity planning: Growth was usually managed by over-provisioning. IT teams added more hardware “just in case,” since there weren’t reliable ways to model demand or forecast usage trends. This meant unnecessary costs or, worse, running out of resources at the wrong moment.
Configuration management was fully manual: Configuration tasks- backups, changes, compliance checks- were heavily manual. One small oversight, like a missed update, could cascade into vulnerabilities or inconsistencies across the network.
Struggles with rigid/reactive resource allocation: Traditional monitoring tools were often rigid. Resources weren’t allocated based on shifting demands; they were statically provisioned. This lack of agility meant IT teams had to scramble when workloads spiked unexpectedly.
Missed or delayed anomalies often let small glitches turn into outages. Even a short service disruption can cost anywhere from thousands of dollars for smaller businesses to hundreds of thousands for large enterprises.
Unnoticed anomalies spread across systems, leading to prolonged downtime and bigger troubleshooting efforts.
Slow detection meant teams wasted hours digging through logs and alerts. That kind of lost productivity can quickly add up to tens of thousands of dollars in wasted labor and delayed recovery per incident.
Each delay drags out downtime, frustrating end-users and piling up SLA penalties.
Without prediction, downtime was the first alert. The cost of downtime is well-documented- ranging from a few hundred dollars per minute for SMBs to several million dollars per hour in large enterprises.
Revenue loss is only the beginning- customer trust and IT team morale erode over time.
Manual RCA slowed resolution, often stretching incidents into hours or days. The hidden costs- lost business opportunities, SLA credits, and productivity dips- can easily reach hundreds of thousands of dollars a year.
Without timely RCA, the same issues resurface, multiplying long-term costs.
Over-provisioning locked away budget in unused resources, while under-provisioning caused costly bottlenecks. Either way, businesses risk losing 5–10% of IT spend annually to inefficient planning.
Poor forecasting eventually leads to service disruptions at scale, forcing emergency fixes and capital spends.
Manual configuration tasks introduced drift and compliance gaps. A single misconfiguration can lead to downtime or, worse, a breach. The financial fallout ranges from minor operational losses to regulatory fines in the millions.
Small inconsistencies compound, undermining network reliability and security posture.
Rigid systems meant services often buckled under sudden spikes. The immediate cost was downtime, but the longer-term loss was customer dissatisfaction and churn. Financially, this can mean thousands per incident for SMBs, or millions in lost business at enterprise scale.
One bad experience can ripple across brand reputation, client trust, and competitive positioning.
OpManager uses AI/ML-driven adaptive thresholds to dynamically learn normal network behavior and detect real-time anomalies. This reduces false positives compared to static threshold alerts and enables proactive incident detection.
OpManager’s fault management combines AI-powered alerting, event correlation, SNMP trap processing, and syslog monitoring to automatically detect network faults and suppress noisy alerts. This accelerates fault identification and prevents alert storms by focusing on root faults.
OpManager leverages machine learning-based trend forecasting from historical and real-time data to predict potential performance or capacity issues. This lets IT teams get advance warnings about issues to mitigate them proactively.
AI-enabled dependency-aware alerting in OpManager correlates and suppresses secondary alarms, helping IT teams quickly identify the primary cause of network issues. This reduces troubleshooting time and improves incident response.
Using historical data, OpManager’s predictive analytics assist in capacity planning by forecasting future network resource needs, enabling better long-term resource allocation and avoiding network bottlenecks.
Automation via OpManager’s Network Configuration Manager (NCM) add-on is substantial. The NCM add-on automates the entire lifecycle of device configuration management by enabling:
Integration with OpManager workflows to automate remediation steps like backups and configuration pushes. This empowers IT teams to reduce downtime, minimize errors, and enhance security through AI-assisted automation of configuration tasks.
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