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Top 5 AI Network Monitoring Use Cases and Real-Life Examples in '24

Updated on Jun 20
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Researched by
Sena Sezer
Sena Sezer
Sena Sezer
Sena is an industry analyst in AIMultiple. She completed her Bachelor's from Bogazici University.
Sena has experience in e-commerce technology, software development, and website design.
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Enterprise IT leaders are increasingly adopting artificial intelligence driven automation to handle the complex interconnections across clouds, digital platforms, and devices. A recent survey reveals that 96% of IT decision-makers have already implemented AI in their network operations or plan to do so shortly.1

The integration of artificial intelligence (AI) into network monitoring enhances availability and performance. This helps maintain stable operations and bolster security by automating complex processes and preemptively managing potential problems, making AI network monitoring vital for optimal network performance.

Real life Examples

Here are some real-life examples of AI applications in network monitoring:

Case study #1 from Juniper Networks

Juniper Networks is a networking technology, offering solutions for enterprise and service provider networks. Its AI-Native Networking Platform leverages artificial intelligence and machine learning to optimize network performance, enhance security, and automate network management tasks.

Expert, a large electronics reseller in Germany, utilized the Juniper AI-Native Networking Platform, introduced by Wayv, a specialist in wireless network engineering. This adoption aimed to optimize network management and user experience across its operations. The AI capabilities of the Juniper platform provided Expert with enhanced operational insights and automation, significantly improving network reliability and security. Notably, the Marvis Virtual Network Assistant identified and resolved issues such as VLAN misconfigurations and DHCP errors efficiently. As a result, Expert achieved a streamlined and responsive network environment that supports its diverse operational needs.2

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.

Zakir Mohammed’s team at Toyota Motor North America has integrated Datadog. Deploying Datadog proved efficient cutting:

  • Setup time by deploying the agent within minutes
  • Mean Time to Resolution (MTTR) by 80%, streamlining troubleshooting processes.

When network disruptions plagued Toyota’s Automated Guided Vehicles (AGVs), Datadog identified the root cause, saving the company substantial production costs and resolving the issue within hours. Transitioning from reactive to proactive monitoring, Toyota utilizes Datadog’s Watchdog feature to forecast and prevent future outages, enhancing operational reliability.

Case study #3 from Dynatrace Davis AI

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

Prior to 2016, BARBRI, a legal exam prep platform, operated in a hybrid environment, utilizing both on-premises servers and cloud services. Recognizing the need to scale its operations and enhance resource utilization, BARBRI decided to transition to a fully cloud-based infrastructure.

Dynatrace’s platform was deployed to monitor BARBRI’s entire Azure environment, ensuring that the migration and ongoing operations were managed efficiently. The AI capabilities of Dynatrace allowed BARBRI to monitor and gain real-time insights into BARBRI’s topology. Therefore, BARBRI scaled its Azure environment to satisfy demand increases during peak times and improve user experience.3

ai in network monitoring

Source: Dynatrace Davis AI User Interface4

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 used in various scenarios, such as detecting unusual patterns that could indicate security threats or operational problems, thus allowing for quicker remedial action.

ai network monitoring

Source: Cisco AI Network Analytics Features 5.

REWE Group implemented Cisco AI Network Analytics to enhance their network management capabilities. This collaboration has reduced the time needed to resolve network issues, thereby allowing the IT team to allocate more time to new projects and innovations that are 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.6.

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 critical to ensure 24×7 service uptime and continuous availability of customer data. Anodot’s AI capabilities allow LivePerson to detect and respond to anomalies in real time, thereby maintaining high customer satisfaction and operational efficiency. 7

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 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 identify malware, ransomware, and other malicious activities quickly, 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.

FAQs for AI Network Monitoring

How does AI improve network monitoring?

AI enhances network monitoring by automating tasks, improving anomaly detection accuracy, offering predictive insights, and managing large-scale and complex networks more effectively.

Are there any drawbacks to AI network monitoring?

While AI network monitoring offers numerous benefits, challenges include integration complexities with existing systems and the requirement for significant initial setup and tuning.

Can AI network monitoring adapt to changes in network architecture?

Yes, AI network monitoring systems are generally designed to adapt to changes in network architecture. They continuously learn from network behavior, allowing them to adjust to new devices, configurations, and traffic patterns effectively.

How does AI network monitoring handle security?

AI network monitoring improves security by continuously analyzing network traffic for unusual patterns, detecting potential threats in real-time, and automating responses to security incidents, which enhances overall network resilience.

Can AI network monitoring reduce operational costs?

Yes, AI network monitoring can significantly reduce operational costs by automating routine tasks, minimizing downtime through predictive maintenance, and optimizing network performance, which in turn reduces the need for frequent hardware upgrades and manual troubleshooting.

For more on network monitoring

Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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