What is AI for Networking?

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More and more companies are capitalizing on the synergy between artificial intelligence (AI) and networking. With the proliferation of user devices and the data they generate, companies are increasingly relying on AI to help manage a sprawling network infrastructure.

By 2024, 60% of enterprises will have an AI-infused infrastructure that will entail more widespread automation and predictive analytics for networking aspects like troubleshooting, incident prevention, and event correlation.

What is AI for Networking?

AI is becoming ever-pervasive as companies try to manage increasingly complex networks with the resources their IT departments have. What network administrators used to do manually is now largely automated – or moving that way. 

However, the use of AI does not shield even the biggest companies from network outages. Facebook experienced a major outage in October 2021 that the company blamed on faulty router reconfiguration. AWS likewise experienced an outage in December 2021 that it chalked up to a network scalability error.

In spite of AI’s sophistication and all it can do for networks, it is not foolproof. This underlines the continued importance of human intervention in networking.

Read more: Cloud is Down: How to Protect Your Organization Against Outages

How AI is Deployed in Networking

AI, more specifically the application of machine learning (ML), helps network administrators secure, troubleshoot, optimize, and plan the evolution of a network.


A proliferation of endpoints in the network in the age of work from home – and work from anywhere – widens a network’s attack surface. To remain secure at all times, a network should be able to detect and respond to unauthorized or compromised devices.

AI improves the onboarding process of authorized devices to the network by setting and consistently enforcing quality-of-service (QoS) and security policies for a device or group of devices. AI automatically recognizes devices based on their behavior and consistently enforces the correct policies.

An AI-powered network also detects suspicious behavior, activity that deviates from policy, and unauthorized device access to the network more quickly than a human could. If an authorized device indeed gets compromised, an AI-powered network provides context to the event.

Device categorization and behavior tracking helps network administrators manage various policies for various devices and device groups and reduces the potential for human error when introducing a new, authorized device to the network. It also helps them detect and troubleshoot network issues in a fraction of the time.

Read more: Best Network Security Software & Tools of 2022


Prior to AI-driven networking, NetOps (network operations) needed to determine network problems by reviewing logs, events, and data across multiple systems. This manual work not only took time and extended outages but also presented opportunities for human error. The sheer amount of data involved in today’s networks makes it humanly impossible for any NetOps team, no matter how large, to sift through event logs to identify and fix network problems.

Now, AI enables networks to not only self-correct issues for maximum uptime but also to suggest actionable steps for NetOps to take.

When a problem occurs, an AI-driven network uses data mining techniques to sift through terabytes of data in a matter of minutes to perform event correlation and root cause analysis. Event correlation and root cause analysis help to quickly identify and resolve the issue.

AI compares real-time and historical data to discover correlating anomalies that begin the troubleshooting process. Examples of relevant data include firmware, equipment activity logs, and other indicators.

An AI-infused network can capture relevant data from just prior to an incident, aiding investigation and accelerating the troubleshooting process. The data from each incident helps machine-learning algorithms in the network to predict future network events and their causes.

In addition to detecting and learning from network faults, AI automatically fixes them by drawing from the network’s rich historical data bank. Alternatively, it relies on this data to make precise recommendations on how network engineers should approach the problem.

AI capabilities streamline and drastically improve the troubleshooting process. AI reduces the number of tickets IT must process, and in some cases it can resolve problems before end users, and even IT, notice an issue.

Network Optimization

Keeping a network functioning and secure at baseline is one thing, but optimizing it is another. The continuous process of optimizing a network is what keeps end users happy and retains them as customers in the long run.

Wireless connectivity standards have evolved in terms of speed, number of channels, and channel bandwidth capacity. These standards are more than any traditional NetOps initiative could handle, but not too much for a network that is infused with AI.

Network optimization involves the trifecta of monitoring the network, routing traffic, and balancing workloads. That way, no one part of the network is overburdened. Instead, the network is able to efficiently deliver the best quality of service by distributing traffic more evenly across the network.

Today’s networks require self-optimizing AI networks that thrive on real-time, event-based network data. Through deep learning, for instance, a computer can analyze multiple datasets related to the network. Based on that data, the network’s recommendation engine checks the policy engine to make smart recommendations to enhance existing policies.

On the one hand, the suggestions meet baseline service quality standards in spite of changing circumstances, such as a traffic spike in a particular geographical area or on a user’s device. The recommendation engine may suggest switching on idle assets or rerouting traffic through longer paths to mitigate congestion.

At the same time, the suggestions adhere to the network’s baseline operational constraints, such as prioritizing phone calls and SMS text message performance over video streaming.

The network will then re-optimize the equipment on its own based on the recommendations. Self-optimizing networks maximize a network’s existing assets, directing it on how to best operate given its finite resources, while also ensuring adherence to service-level agreements (SLAs).

Through the observability and orchestration of AI-powered networks, users get the best possible network experience.

Network Planning

Given the growth of 5G networking, AI will have the biggest impact in network planning to provide new services or expand existing services to underserved markets.

A 2018 Ericsson report found that 70% of service providers worldwide report AI as having the greatest impact on network reliability. Not far behind reliability, network optimization and network performance analysis are two further areas where 58% of respondents say AI is gaining traction.

Using AI for network performance analysis enables communication service providers to accurately predict what a network will need and are thus able to better prepare.

For example, AI can be deployed to improve the provider network’s geolocation accuracy. Doing so provides critical information to help the provider evaluate the quality of service in a particular area. That information, in turn, informs plans for future network upgrades.

AI also comes into play when trying to identify underserved market areas. It helps distinguish served versus unserved markets from satellite images.

AI gives businesses, communication service providers in particular, a competitive edge by helping them identify and act on strategic opportunities.

Read more: From Co-existence to Convergence: The Union of 5G and Wi-Fi

Benefits of Leveraging AI for Networks

AI-infused networks provide organizations with a host of benefits, including:

  • Continuous monitoring.
  • Event correlation and root cause analysis to detect, fix, learn from, and prevent network issues.
  • Predictive analytics to proactively identify and address future issues.
  • Fewer instance of downtime.
  • Shorter downtime when it occurs.
  • Automated network provisioning, such as for devices and optimization.
  • Automated network-boosting recommendations.
  • Enhanced network performance.

Also read: Best Network Automation Tools for 2022

The Future of AI Use in Networking

Given the many benefits of AI-infused networks, they are sure to keep growing in adoption across today’s enterprises. AI is playing an increasingly important role in managing networks that are rapidly becoming more complex.

However, the fear that AI will replace networking professionals is a noted but ultimately unwarranted concern. Networks still need humans to verify and occasionally augment AI functionality by:

  • Addressing discrepancies between a network problem and a proposed solution that the system generates.
  • Assisting the machine when it cannot produce a solution with a high level of confidence.
  • Inspecting event correlation and using human logic to guide the algorithm in what it should and should not learn in terms of event dependencies.
  • Validating the machine’s analysis before implementing its recommendations.
  • Understanding how a machine arrived at an insight, decision, or conclusion.

Read more: What is Explainable AI (XAI)?

Aside from these interventions, because of AI’s largely automated role in networking, IT teams can devote their resources to strategic, high-value tasks, such as digital experience and digital initiative roll-ups.

Read next: The Future of Network Management with AIOps

Read next: Top Business AI Trends to Watch for 2022

Lauren Hansen
Lauren Hansen
Lauren Hansen is a writer for TechnologyAdvice, covering IT strategy and trends, enterprise networking, and PM software for CIOInsight.com, enterprisenetworkingplanet.com, project-management.com, and technologyadvice.com. When she's not writing about technology trends, she's working out or spending time with family.

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