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AI in Networking: Revolutionizing Network Operations

Written By Ari Stowe
May 21, 2024

In the ever-evolving world of network operations, staying ahead of the curve is crucial. With the exponential growth of data, increasing complexity of networks, and rising demand for seamless connectivity, traditional network management methods are becoming insufficient.  

Artificial Intelligence (AI) is the transformative technology poised to radically transform network operations. By integrating AI into network management, organizations can achieve unprecedented efficiency, reliability, and performance. 

So, let’s start with a quick primer. 

Popular Industry Terms for AI Networking and What They Mean 

AI in networking is often referred to by various marketing terms, each highlighting different aspects of its capabilities: 

  • Autonomous Networking: Networks that can manage themselves with minimal human intervention. 
  • Self-Driving Networks: Similar to autonomous networks, emphasizing the automation of network management tasks. 
  • Self-Healing Networks: Networks that can automatically detect and resolve issues without human intervention. 

These terms, while not strictly defined, generally describe the application of AI across various networking domains, including data center switching, wired and wireless LAN, SD-WAN, managed network services (MNS), and multi-cloud networking software.  

While some capabilities may be more mature in certain domains, the core focus across all areas remains the same: leveraging data collection, data lakes, algorithms, data science, and automated responses to enhance network operations. 

Now that we’ve got the basics cleared up, let’s talk about why you should care.  

AI Is the Shiny New Thing: Start with Baby Steps for Big Success 

Implementing AI in networking can seem daunting, but starting with small, manageable steps can yield significant benefits. Here are some common scenarios where AI is making a difference: 

  • Solve Problems Quicker: AI can proactively respond to issues after they occur but before they impact users. Unlike predictive analytics, which is still aspirational, AI-driven problem resolution is already helping organizations respond faster to network issues. 
  • Correlate Multiple Data Sources: AI can centralize problem identification by analyzing data from multiple monitoring systems, creating a comprehensive view of the network. This encompasses topology construction and a thorough understanding of the contextual relationships that exist within the network. 
  • Minimize False Positives: AI systems can perform advanced analyses to confirm or reject trouble tickets, reducing the resources spent on addressing non-issues. 
  • Autonomously Resolve Level 1/Level 2 Support Issues: Chatbots powered by natural language processing (NLP) can handle many Level 1 and Level 2 support tasks, interacting with end users to resolve common issues. 
  • Automate Responses/Recommendations for Complex Issues: AI can generate dynamic packet captures and data flows necessary for investigating complex issues, determining appropriate resolutions without human intervention. 

AI can make your job easier! 

Implementing AI in network operations brings a multitude of benefits: 

  • Improved Network Availability: AI can enhance network availability by up to 25%, ensuring a better end-user experience and improving application performance. 
  • Simplified Network Management: AI reduces the reliance on deep networking skills, enabling organizations to manage networks in-house without needing extensive technical expertise. 
  • Cost Reduction: By reducing the need for extensive personnel to manage networks, AI can cut operational costs significantly. Organizations have reported savings of up to 50% in areas such as troubleshooting, installation, and site visits. 
  • Minimized False Positives: AI reduces the time and resources spent on unproductive activities by minimizing false positives in trouble tickets. 
  • Risk Reduction: Leveraging digital twins, AI can simulate network changes and perform “what if” scenarios, reducing the risk associated with network updates and changes. 
  • Increased Agility: AI allows for quicker changes to support the dynamic needs of digital enterprises, driving greater agility in network operations. 
  • True Automation: AI goes beyond automating predefined workflows and templated tasks. By incorporating generative AI, such as ChatGPT, AI networking achieves true automation without requiring initial human setup. 

Where can you start 

AI in networking presents a variety of practical applications, including but not limited to: 

  • Proactive Hardware Failure Detection: AI can predict hardware failures (e.g., faulty ASICs, CPUs, or flash drives) and schedule replacements to avoid outages. 
  • Root Cause Analysis: By correlating multiple datasets, AI can pinpoint the root cause of issues, such as elevated latency in specific network segments. 
  • Adaptive Wireless Networks: Wireless access points can reroute to alternative channels in response to noise, interference, or congestion. 
  • Optical Networking Adjustments: AI can reroute traffic in response to optical networking issues in the network underlay. 
  • Automated Malware Response: AI can quarantine infected network segments to contain malware spread and initiate remediation. 
  • Dynamic Bandwidth Management: AI can request additional bandwidth or reroute traffic in response to sudden spikes in network demand. 
  • Advanced Chatbot Functionality: Chatbots equipped with NLP and AI like ChatGPT can provide advanced troubleshooting resolutions, reducing the workload on network administrators. 
  • Performance Issue Resolution: AI can identify and resolve issues like excessive TCP transmits or high server response times before users notice any impact. 
  • Vendor-Neutral Troubleshooting: AI can offer troubleshooting and provisioning recommendations without needing operators to know specific vendor terminologies. 

AI in networking is not just a trend—it’s a transformative force driving efficiency, reliability, and innovation in network operations. By taking measured steps and leveraging AI’s capabilities, organizations can significantly enhance their network management and overall performance.

Book a demo today and discover how Resolve Systems can help you shake off the shackles of conventional network management and revolutionize your network operations. 

About the author, Ari Stowe:

About the author, Ari Stowe:

VP, Product at Resolve Systems

As VP, Product, Ari Stowe leads Resolve's product organization. He is a resourceful product management professional and highly driven individual, continuously looking to further his skills and knowledge through constant learning. Ari's primary role as a Senior Product Director has allowed him the opportunity to navigate emerging technologies and drive innovation across multiple product lines. Along with his passion for product management, Ari has a strong passion for mentoring others. He takes great pride in seeing others succeed and in reaching their full potential.