AIOps

What is AIOps?

AIOps detects issues affecting performance, security, or user experiences, and responds with recommendations or autonomous fixes. It automates complex workflows, accelerating efficiency and minimizing human-induced delays.

AIOps empowers IT teams with predictive analytics, anomaly detection, and event correlation. With these capabilities, operators can proactively identify and resolve issues before they impact user experiences, application performance, or system availability, delivering seamless experiences with advanced intelligence and automation.

Beyond automating complex workflows and reducing manual effort, its real strength lies in its ability to scale across diverse environments, from campus networks to cloud infrastructure, adapting to evolving business needs. This adaptability, combined with real-time intelligence, drives greater efficiency across the organization.

AIOps works by ingesting and consolidating billions of data points from diverse sources, (applications, logs, events, alerts, patterns, and more). It then processes that data via machine learning or deep learning (DL) algorithms with agentic AI orchestration to deliver real-time insights, such as quality of experience (QoE), root cause analysis, and anomaly detection.

AIOps continuously scans for patterns, correlations, and anomalies that may indicate emerging issues or performance deviations. It applies techniques like clustering, classification, and predictive analytics to automatically group related events, filter out noise, and identify root causes.

AIOps platforms often leverage natural language processing (NLP) to interpret unstructured data (like incident tickets or chat messages) and use automation engines to trigger remediation workflows or alert IT teams.

Good AIOps will reduce false positives, eliminating alarm fatigue so operators can proactively detect issues and resolve them before they impact end user experiences.

AIOps helps address many of the most common challenges IT teams face today when it comes to operating their networks. These include:

• Maintaining network configuration compliance: Static device settings do not keep up with changing business needs. AIOps continuously monitors network operations and recommends or automatically makes optimization changes.
• Addressing changing business needs: Manually configuring Service Level Expectations (SLEs) is costly and time-consuming. With AIOps, important network thresholds are automatically defined, monitored, and adjusted based on environmental changes.
• Resolving network issues quickly: In most IT organizations, help desk calls are the primary form of identifying problems, which is expensive and inefficient. The preemptive insights provided by AIOps help identify issues before they impact users or IoT devices for a reduction in help desk calls.
• Replicating intermittent issues: Many IT teams spend hours or days tracking down intermittent problems because they are difficult to replicate. Automated, always-on monitoring via AIOps pinpoints persistent versus obvious problems, with built-in data capture.
• Increasing network complexity: Troubleshooting and optimization tasks consume over 50% of IT’s time. AIOps solves this challenge by providing key insights such as the reasons for failures, root cause analysis, and repair recommendations.
• Lacking resources and skills: Lack of resources and training are a constant point of contention in many IT organizations. AIOps-driven insights, such as GenAI-powered search features, are designed to assist and enhance the team’s knowledge base.

• Faster troubleshooting and resolution: AIOps automates root cause analysis and incident correlation, reducing manual troubleshooting and mean time to resolution.
• Proactive issue detection: AIOps identifies anomalies and potential problems before they impact users, allowing admins to address issues proactively and reduce trouble tickets.
• Optimized network performance: AIOps analyzes traffic patterns and configuration data, providing actionable recommendations that can increase network utilization, optimize bandwidth and enable IT teams to focus on higher-value strategic initiatives.
• Reduced alert noise: Machine learning filters out irrelevant alerts and false positives, enabling admins to focus on genuine issues and improve operational efficiency.
• Automated remediation and enhanced user experience: AIOps automates routine tasks and workflows, minimizing downtime and outages. This allows IT teams to maintain higher service availability and meet network uptime requirements without increasing operational overhead.

AIOps for networking, or AI for networking, is an approach to networking that integrates security, digital experience monitoring, and support for Zero Trust architectures, ensuring networks are efficient and resilient against evolving threats.

Networks must have universal connectivity, constant uptime, high speed, and low latency, all while being secure and reliable. With innovations like GenAI search, agentic mesh, and autonomous remediation, organizations can set a new standard for intelligent networking.

Using the power of AI in a networking platform to help you optimize and manage your network is like having a team of experts with you at the poker table. Harnessing the power of AIOps helps you run your network seamlessly.

Organizations have an exciting opportunity to elevate their network operations by embracing AIOps. Adopting new technology opens the door to greater efficiency, smarter decision-making and improved service quality. Here are some important aspects to consider as you explore AI adoption for your organization:

• Security and ethics: Determine what data the AI engine is using and how that data is secured. Ensure that the vendor follows ethical AI principals and guidelines.
• Integration: An effective AIOps solution should streamline operations rather than add complexity. Look for a solution that can integrate with existing infrastructure or is built into the IT solution.
• Efficacy: Evaluate how AIOps has evolved over time, beginning with the point of its initial integration. An effective AIOps system should deliver accurate, real-time insights and proactively alert operators to high-priority issues, without contributing to alert fatigue. Its performance should continuously improve through a closed-loop cycle of feedback and development.
• Real-world examples: Look for situations where the AIOps solution has provided real results for customers.