Top 5 AI Network Monitoring Use Cases and Real Life Examples

The integration of artificial intelligence (AI) into network monitoring enhances availability and performance. Here are options for you:

• If you’re searching for an AI network monitoring tool, compare the top options and pricing with our curated selections.
• Are you interested in discovering how AI is enhancing network monitoring? Explore how AI technologies streamline operations, boost efficiency, and drive smarter network management.

Real-life Examples

Case study #1 from Juniper Networks

Source: AI-Native Networking Diagram¹

Juniper Networks is a networking technology company offering solutions for enterprise and service provider networks. Its AI-native networking platform offers artificial intelligence and machine learning to optimize network performance, enhance security, and automate network management tasks² .

Challenge: Expert needed to optimize network management and user experience across its operations, dealing with traditional Wi-Fi connectivity and performance issues in its automated warehouse environment.

AI Solution:

• Deployed Juniper’s AI-Native Networking Platform with Mist AI
• Marvis Virtual Network Assistant identified and resolved issues such as VLAN misconfigurations and DHCP errors efficiently
• The Marvis AI engine saves operators time, effort, and money with up to 90% fewer trouble tickets, up to 50% faster problem resolution, and up to 85% fewer on-site visits.

Results:

• Streamlined and responsive network environment
• “With Juniper and Mist AI, we can quickly find the root cause and often prove that the problem is not the network infrastructure”.
• Automated troubleshooting and operations
• Enhanced operational insights and automation

Case study #2: DataDog

Watchdog, Datadog’s AI engine, offers automated notifications, insights, and root cause analyses derived from observability data spanning the entire Datadog platform.

Challenge: Toyota’s Automated Guided Vehicles (AGVs) were disconnecting from the network and disrupting plant operations, with the AGV vendor suggesting the network was the source of the issue.³ .

AI Solution:

• Datadog’s Watchdog feature, which utilizes AI and ML on the backend to help the company forecast and prevent future outages
• Real-time monitoring and analysis of network and infrastructure data
• Watchdog, Datadog’s AI engine, offers automated notifications, insights, and root cause analyses derived from observability data spanning the entire Datadog platform

Quantified Results:

• Datadog enabled his team to reduce MTTR by 80 percent
• Datadog allowed Toyota to solve a problem in hours that they hadn’t been able to solve in weeks, saving them thousands of dollars in lost production time
• TMTR is reduced from about six hours to 15 minutes in a large-scale system
• In another example, TMNA also used Datadog’s services to help reduce the mean time to resolution (MTTR) from seven days to two hours in one of its manufacturing plants, avoiding hundreds of thousands of dollars of cost from downtime

Case study #3 from Dynatrace Davis AI

Dynatrace offers an AI network monitoring engine called Davis, which is integral to its software intelligence platform. Davis analyzes data across the digital ecosystem, including clouds, applications, and infrastructure.

Challenge: BARBRI decided to move to a full cloud environment after working in a hybrid environment. BARBRI began migrating to Azure in 2016 and worked with Dynatrace because of the company’s automation and intelligence offerings.

AI Solution:

• Davis, Dynatrace’s powerful AI engine, learned BARBRI’s environment, providing problem analysis and root-cause analysis
• With Dynatrace automatically detecting and monitoring BARBRI’s topology, BARBRI was able to scale its Azure environment during peak times of the year to ensure a positive customer experience
• Integration with Azure Monitor for expanded visibility

Results:

• Successful migration to full Azure cloud environment
• Dynatrace provided BARBRI with real-time insights into its Azure environment and the information to communicate with other departments and report to the executive team.
• “By bringing in metrics from Azure Monitor, the Dynatrace AI engine now provides better answers, to give us a deeper view into service behavior and root cause,” said Mark Kaplan, Senior Director of IT at BARBRI⁴ .

Source: Dynatrace Davis AI User Interface⁵

Case Study #4 from Cisco AI Network Analytics

Cisco’s AI Network Analytics, part of its DNA Center, uses machine learning to provide insights into network performance. It helps network managers predict issues, optimize network performance based on predictive analytics, and fine-tune the network proactively.

Cisco AI Network Analytics has been utilized in various scenarios, including detecting unusual patterns that may indicate security threats or operational issues, thereby enabling quicker remedial action.

Source: Cisco AI Network Analytics Features ⁶ .

REWE Group implemented Cisco AI Network Analytics to enhance their network management capabilities. This collaboration has reduced the time needed to resolve network issues, allowing the IT team to allocate more time to new projects and innovations crucial to business operations.

The application of AI/ML has simplified the handling of network workloads, making daily management tasks less time-consuming and highlighting critical alerts that indicate connectivity or performance issues.⁷ .

Case study #5 from Anadot

Anodot is a provider of AI-powered analytics solutions designed to detect anomalies in real-time data. Its platform utilizes machine learning algorithms to identify deviations from expected patterns, enabling businesses to address issues and capitalize on opportunities.

LivePerson, a conversational AI platform, implemented Anodot’s real-time analytics to monitor a complex array of nearly 2 million metrics every 30 seconds across its global data centers. This deployment was crucial to ensuring 24/7 service uptime and the continuous availability of customer data.

Anodot’s AI capabilities enable LivePerson to detect and respond to anomalies in real-time, thereby maintaining high customer satisfaction and operational efficiency. ⁸

Based on these case studies, AIMultiple identified AI use cases in network monitoring:

AI Use Cases In Network Monitoring

By using AI capabilities, businesses can enhance their network monitoring practices in various ways. Here are some use cases of AI in network monitoring:

• Anomaly detection: AI network monitoring tools can quickly identify unusual patterns or deviations from normal network behavior, which might indicate a security breach or system failure.
• Predictive analytics: By analyzing historical data, AI can predict potential network failures or performance degradations before they occur.
• Automated configuration and optimization: AI can automate routine network configuration tasks and optimize network settings based on current traffic patterns and demands.
• Security enhancement: AI enhances network security by detecting and responding to threats in real time. It can quickly identify malware, ransomware, and other malicious activities, minimizing potential damage.
• Root cause analysis: When problems occur, AI can help diagnose the root cause more quickly than traditional methods. By correlating various data points and identifying patterns, AI reduces the time needed to troubleshoot and resolve issues.
• Capacity planning: AI can forecast future network needs based on trend analysis, helping organizations plan upgrades and expansions more effectively.

AI Network Monitoring Tools

** Reviews are based on Capterra and G2. Vendors are ranked according to their number of reviews

*** Free trial periods and pricing are included if the content is publicly shared.

1. Dynatrace

Dynatrace offers its AI engine, Davis, which significantly enhances network and application monitoring capabilities. Davis automates complex processes, such as root cause analysis, anomaly detection, and predictive insights, making it a powerful tool for proactive monitoring.

Dynatrace isn’t limited to just network monitoring it excels in Application Performance Monitoring (APM), cloud infrastructure management, and digital experience monitoring, offering a comprehensive solution for managing the entire digital ecosystem.

2.LogicMonitor

With its AI-driven insights, LogicMonitor can automate anomaly detection, enabling it to identify unusual network behaviors before they escalate into critical issues. The platform’s AI also supports predictive analytics, allowing IT teams to anticipate potential network problems and address them proactively.

Additionally, LogicMonitor utilizes AI to provide intelligent troubleshooting, thereby reducing the time required to resolve incidents and enhancing overall network performance. This makes LogicMonitor a powerful tool for organizations that require real-time network monitoring, along with the added benefits of AI-driven automation and intelligence.

3. Auvik

Auvik integrates AI-driven features to enhance its network monitoring and management capabilities. Auvik’s AI helps network operations by automating tasks such as network mapping, device discovery, and configuration backups.

Its anomaly detection features utilize AI to identify unusual patterns in network behavior, enabling IT teams to spot potential issues before they escalate quickly. Additionally, Auvik’s AI-powered insights offer predictive analytics, enabling proactive maintenance and optimization of network performance.

4. NinjaOne

NinjaOne integrates AI-driven features to enhance its network monitoring capabilities. It focuses on automation, real-time monitoring, and proactive issue resolution. Key AI-powered features include automated anomaly detection and alerts.

NinjaOne also supports predictive analytics to prevent problems before they escalate, and it automates routine tasks such as network discovery, device monitoring, and patch management. This level of automation reduces downtime and improves network reliability.

FAQs for AI Network Monitoring

External Links

External Links

• 1. What is AI-Native Networking? | HPE Juniper Networking US.
• 2. expert Case Study | HPE Juniper Networking US.
• 3. Toyota Motor North America | Datadog.
• 4. Dynatrace Case Study.
• 5. Meet Davis, our powerful AI-engine.
• 6. “Cisco AI Network Analytics Overview”. Accessed: April 24 2024.
• 7. Cisco AI Network Analytics: Making Networks Smarter and Simpler to Manage - Cisco Blogs. Cisco Systems
• 8. LivePerson Uses AI-Powered Analytics to Address Most Challenging Customer Engagement Issues - Anodot.