What is AI for the Network?

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, enterprises are increasingly relying on AI to help manage a sprawling network infrastructure.

By 2024, 60% of companies will have an AI-infused infrastructure that will involve more extensive automation and predictive analytics for networking aspects such as troubleshooting, incident prevention, and event correlation.

What is AI for the Network?

Artificial intelligence is becoming more pervasive as companies attempt to manage increasingly complex networks with the resources available to their IT departments. What network administrators once did manually is now largely automated – or evolving that way.

However, the use of AI does not protect even the largest companies from network failures. Facebook experienced a major failure in October 2021 that the company blamed on the faulty reconfiguration of the router. AWS also experienced a outage in December 2021 which he attributed to a network scalability error.

Despite the sophistication of AI and all it can do for networks, it is not infallible. This highlights the continued importance of human intervention in networking.

Read more: Cloud is Down: How to protect your organization from outages

How AI is deployed in networks

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 working from home – and working from anywhere – expands a network’s attack surface. To remain secure at all times, a network must be able to detect and respond to unauthorized or compromised devices.

AI improves the process of onboarding authorized devices to the network by consistently defining and 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 applies the right policies.

An AI-powered network also detects suspicious behavior, activity that deviates from policy, and unauthorized device access to the network faster than a human could. If an authorized device is indeed 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 risk of human error when introducing a new authorized device to the network. It also helps them detect and fix network issues in a fraction of the time.

Read more: Best Network Security Software and Tools of 2022


Prior to AI-driven networking, NetOps (network operations) had to determine network issues by examining logs, events, and data across multiple systems. This manual labor not only took time and prolonged outages, but also presented opportunities for human error. The amount of data involved in today’s networks makes it humanly impossible for any NetOps team, regardless of size, to sift through event logs to identify and troubleshoot network issues.

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

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

AI compares real-time and historical data to uncover correlated 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 just before an incident, making it easier to investigate and speeding up the troubleshooting process. Data from each incident helps the network’s machine learning algorithms predict future network events and their causes.

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

AI capabilities dramatically streamline and improve the troubleshooting process. AI reduces the number of tickets IT has to deal with, and in some cases, it can resolve issues before end users, and even IT, notices a problem.

Network optimization

Maintaining a functional and secure network initially is one thing, but optimizing it is another. The ongoing process of optimizing a network is what makes end users happy and retains them as long-term customers.

Wireless connectivity standards have evolved in terms of speed, number of channels, and channel bandwidth capacity. These standards exceed what any traditional NetOps initiative could handle, but not too much for an AI-infused network.

Network optimization involves the trifecta of network monitoring, traffic routing, and workload balancing. This way, no part of the network is overloaded. 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 artificial intelligence networks that thrive on real-time, event-based network data. Through deep learning, for example, a computer can analyze multiple sets of data related to the network. Based on this data, the network recommendation engine checks the policy engine to make intelligent recommendations to improve existing policies.

On the one hand, suggestions meet basic quality of service standards despite changing circumstances, such as a spike in traffic in a particular geographic area or on a user’s device. The recommendation engine can suggest activating inactive assets or redirecting traffic to longer paths to alleviate congestion.

At the same time, the suggestions meet basic network operational constraints, such as prioritizing phone calls and SMS performance over video streaming.

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

With AI-powered network observability and orchestration, users get the best possible network experience.

Network planning

Given the growth of 5G networkingAI will have the greatest impact on network planning to deliver new services or extend existing services to underserved markets.

A Ericsson Report 2018 found that 70% of service providers globally say AI has the biggest impact on network reliability. Not far behind reliability, network optimization and network performance analysis are two other areas where 58% of respondents say AI is gaining ground.

Using AI for network performance analysis enables communication service providers to accurately predict network needs and thus better prepare.

For example, AI can be deployed to improve the accuracy of provider network geolocation. This provides essential information to help the provider assess the quality of service in a particular area. This information, in turn, informs plans for future network upgrades.

AI also comes into play when it comes to identifying underserved market areas. It helps to distinguish served markets from unserved markets based on satellite images.

AI gives companies, and especially communication service providers, a competitive advantage by helping them identify and seize strategic opportunities.

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

Benefits of Leveraging AI for Networks

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

  • Continuous monitoring.
  • Event correlation and root cause analysis to detect, fix, learn, and prevent network issues.
  • Predictive analytics to proactively identify and resolve future issues.
  • Less downtime.
  • Shorter downtime when it happens.
  • Automated network provisioning, e.g. for devices and optimization.
  • Automated network strengthening recommendations.
  • Improved network performance.

Read also : Best Network Automation Tools for 2022

The future of using AI in networks

Given the many benefits of AI-infused networks, they are sure to continue to be adopted in today’s businesses. 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 always need humans to verify and sometimes augment AI functionality by:

  • Address discrepancies between a network problem and a proposed solution generated by the system.
  • Assist the machine when it cannot produce a solution with a high level of confidence.
  • Inspect event correlation and use human logic to guide the algorithm in what it should and shouldn’t learn in terms of event dependencies.
  • Validate the analysis of the machine before implementing its recommendations.
  • Understand how a machine arrived at an insight, decision, or conclusion.

Read more: What is Explainable AI (XAI)?

In addition to these interventions, due to the largely automated role of AI in networking, IT teams can focus their resources on strategic, high-value tasks, such as digital experience and digital initiative rollouts.

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