AI's role in network management Artificial intelligence, particularly, large language models (LLMs), have become the terms in vogue over the past few years--and with good reason. AI can revolutionize many industries and functions, including network operations. The extent to which AI can transform IT networking may seem vague, but there are definitely compelling use cases once we get past AI hallucinations and data privacy concerns. When we say use cases are strong, we need to look at a couple of things AI is really good at:
The rate of change in the industry is so dynamic that the complexity of networks evolves over a shorter period of time, like 2–3 years, thanks to the different sizes, scales, strategies, industries, and so on that an organization decides to operate in. Hybrid networks have made troubleshooting increasingly complex; an IT admin has to worry about on-premises infrastructure composed of different vendors, versions of code, and different outputs, along with software-defined data center solutions and a multi-cloud presence that depend on the underlying infrastructure. The result is very difficult troubleshooting, where IT personnel are expected to have proficiency across many vendors, stay up-to-date with code versions, and always have precise know-how to extract the exact information from the infrastructure to troubleshoot effectively.
Some of the characteristics of traditional network management that set it apart from the modern approach are the ones that become challenges when faced with a modern, vast, and complex IT infrastructure.
Manual device configuration: Traditional network management demands a much higher degree of manual intervention across different stages starting from device discovery. The processes are time-consuming, adding to more operational complexity and costs. Need for continuous manual intervention combined with a sprawling network increases the likelihood of human errors and creates inconsistencies in management.
Tedious troubleshooting: Troubleshooting has to be performed on a device-by-device basis, making it challenging to diagnose issues from a birds-eye perspective. This results in longer resolution times and increased downtime.
Compartmentalized network visibility: Individual device management limits panoramic visibility into the overall availability, health, and performance of the entire network. This challenge further eats into the operational costs as organizations will be forced to subscribe to multiple tools to unlock network visibility and deep analysis.
Predictive maintenance: AI's ability to read vast amounts of data, recognize patterns, and detect anomalies can unlock proactive steps in network management. This can be in the form of replacing or repairing existing network components or investing in more capacity because the forecast indicates that current network capacity is at a tipping point. Being proactive and identifying issues early enables scheduling routine maintenance and planned downtime—priceless in enterprise network management, where unforeseen downtimes can bring business operations to their knees.
Network optimization: Real-time insights into network performance and usage with the power of AI can direct IT admins to areas in the network where there is an urgent capacity need or routing adjustment need. Sometimes the traffic shaping and QoS configurations and prioritizations can be out of balance, causing performance issues. AI-powered tools can monitor these metrics in real-time and tweak settings to prevent the issues proactively.
Network automation: Artificial intelligence and machine learning bring exciting possibilities in the form of automating mundane and repetitive tasks including configuration management, device management, and network monitoring. This frees up precious time for IT personnel, which can be utilized for strategic and mission-critical initiatives.
Closed-loop remediation: A network that can detect issues, drill down to the root cause, and remediate without intensive manual work is a blessing for IT admins and organizations alike. The ability to learn from past performance issues, interpret performance and anomaly patterns, and make proactive adjustments to configuration and network performance ensures optimal network efficiency.
Network security: Predictive analytics, pattern recognition, and adaptive learning capabilities of AI facilitate improved threat detection and resolution.
Start with specific use cases: Identify and address manageable, high-impact areas such as predictive maintenance or anomaly detection. Gradually expand AI applications as confidence and expertise grow within the organization.
Adopt a collaborative approach: Engage network experts and AI specialists to align AI models with operational goals. This ensures AI solutions integrate seamlessly with the network’s architecture and requirements.
Encourage a culture of learning: Promote continuous training for IT teams on AI tools and technologies. Staying informed on emerging trends ensures the organization adapts effectively in a fast-changing AI landscape.
Plan gradual implementation: Deploy AI in stages to test scalability and efficacy. This approach allows for troubleshooting, learning, and refining models to maximize value without disrupting existing operations.
Focus on data quality: Ensure clean, relevant, and comprehensive data for AI training. High-quality data drives accurate predictions and actionable insights, enhancing overall network management efficiency.
Monitor and refine models regularly: Implement periodic checks and updates for AI models to adapt to evolving network conditions and new requirements, ensuring sustained performance and relevance.
OpManager Plus excels at processing network telemetry, filtering out irrelevant data to focus on actionable insights. With advanced noise-reduction algorithms, IT teams can efficiently detect incidents and respond to critical alerts, enhancing decision-making and operational workflows.
OpManager Plus uses real-time and historical data to set adaptive thresholds for monitoring performance metrics. Alerts are categorized by severity—Attention, Trouble, or Critical—enabling network admins to proactively address issues and prevent downtime.
Seamlessly integrated with tools like Slack, Microsoft Teams, and Telegram, OpManager Plus ensures real-time notifications via email, SMS, or chat. Alarms are customizable and actionable, empowering IT teams to resolve issues swiftly and efficiently.
By correlating application and network performance, OpManager Plus uncovers interdependencies and visualizes device relationships with organization maps. This improves troubleshooting and prioritizes critical alarms for faster issue resolution.
Root Cause Analysis (RCA) in OpManager Plus simplifies troubleshooting by correlating performance metrics and alarms. Its visual RCA profile helps IT teams quickly identify bottlenecks and underlying issues, reducing mean time to repair (MTTR).
OpManager Plus employs closed-loop workflows for autonomous remediation. Combined with real-time topology mapping, it provides a clear view of device health and dependencies, enabling IT teams to resolve issues efficiently and maintain reliability.
AI-driven capacity planning analyzes resource usage trends, offering precise forecasts for memory, CPU, and disk space needs. This proactive approach helps avoid resource bottlenecks, optimize costs, and schedule expansions effectively.
Leverage ML-powered trend analysis to forecast network performance based on historical data. OpManager Plus anticipates shifts, dynamically adjusts baselines, and enables proactive measures, ensuring peak network efficiency during high-demand periods.
