The Status Of AI In Networking Right Now

Enterprises are looking at different ways to integrate AI into their businesses. The use-cases for AI are constantly changing. Nowadays, a lot of network teams utilise AI to mitigate security threats or even as a management tool for their networks. It’s even being used as a means to automate tasks. As networks and their environments continue to get more and more complex, they produce much more data than a lot of humans are capable of comprehending on their own.

AI

Various enterprises may start to think about integrating AI into their systems to manage the much more complex environments like 5G or data analytics. Artificial Intelligence has a lot of use-cases and it can help to monitor the performance of a network and even provide alerts for potential issues before they become a major problem. You’ll even find a lot of forms of automated AI that can troubleshoot issues automatically without human involvement.

There was a report released by Gartner in 2021 that showed that the adoption of AI for Information Technology operations (AIOps) is increasing rapidly among a lot of enterprises. The same report estimated that the AIOps market share sat anywhere between 900 million and 1.5 billion the year prior. They also anticipate that it continues to compound annually at a rate of 15% by the year 2025.

One major reason why the adoption rate is skyrocketing has to do with businesses being in the middle of the digital transformation age. As more and more operations continue to become increasingly digitised, relying on humans to analyse and monitor data can be difficult and even impractical. A lot of digital transformation trends throughout the past year included the deployment of ML operations.

A lot of organisations have started to use AIOps to effectively replace traditional monitoring tools as they look to replace traditional tools for the soon to be the post-pandemic environment that is expected to be dominated by practical outcomes according to the report. A lot of newer business demands and pressures that were started by COVID-19 and its protocols have spurned the deployment of AI by a lot of businesses.

While there is a surge in interest in AI, not every organisation is integrating it as quick as it should be. Putting the benefits to the side, AI is still relatively new. It’s also an evolving technology that has been and continues to have unmet potential. This makes a lot of business leaders reluctant to go all in and deploy it within their organisations.

A lot of network teams are some of those who are much more disinclined to integrate AI. You can read below what a lot of analysts have to say about how AI works and its effectiveness in enterprise networks. Also, you can see how they think it will change and how it will be leveraged in the future.

What role does it play in networks that have already started to use it? There was a research analyst John Burke that noted it was still in the very early days of deployment for AI in networking. While it is currently playing a role in businesses today, it’s primarily focused on visibility for management. It’s being used to see what’s happening in a network, what problems might exist, and some of the things that could be negatively impacting the network. Likewise, it has its use case for improving security and finding anomalies that could be causing issues.

The primary goal of using AI in just about both cases is to gather the raw data needed and filter out everything that isn’t necessary. That way, the humans running the network know what to pay attention to and what to not worry about. This will help to improve their performance because they can be much more effective and efficient in their roles. On the performance side of things, it helps with knowing what to pay attention to. This is more of an issue with data centres than a lot of other places, but it’s not specifically restricted to them either.

Juniper announced adding some AI features to their SD-WAN (software-defined WAN). There is expected to be a lot more in the same arena from a lot of other companies. This includes various providers operating as a Network-As-A-Service.

Are There Are Use-Cases For Using AI In 5G Networks?

An independent analyst John Fruehe noted that 5G carrier networks are a dynamic environment. In such a dynamic environment, deploying AI makes a lot of sense. After all, you are always getting new data and the data will dictate how things should operate. AI is much less practical and useful when you are dealing with a much more stable network with not a lot of varying data input. However, in a carrier network, that’s not the case. Things are constantly evolving and the use of AI is essential because it can offer a lot of tweaks to ensure the network is running smoothly at all times. As we continue to deploy a lot more 5G, it allows engineers to get increasingly granular and a lot of the switching and connections can be handled by AI.

Is AI Likely To Alter The Way Network Teams Operate?

The analyst Fruehe noted that the changes brought on by the pandemic have completely erased the traditional operational roles of networking. The excellent plans that network professionals and teams had in previous years have been completely abandoned because they don’t work anymore. For the last two years or so, these teams have been scrambling to figure out how to get their teams and operations back to something more stable.

We are likely not at a point where things are stable enough for people to start integrating higher-level things into their mix. There is still a lot of different work that needs to get done. That being said, networks have become increasingly remote over the past decade or so. The main changes to the industry have to do with the end user’s location which has pushed networkers to software-defined VPN and WAN.

How Is It Going To Impact The Future?

There is a principal analyst Bob Laliberte that noted that there is more data than ever before going over a network. Because of this, there is more and more complexity added to the mix. This complexity goes far beyond what humans can comprehend and calculate in real-time. Thus, AI can enter the mix and handle this aspect easily.

However, it’s also important to understand that AI isn’t meant at all to replace humans in their jobs. Rather, it’s meant to help increase their efficiency and effectiveness. There is always going to be times when the AI will recommend a change, but not be able to make it. This includes dealing with hardware malfunctions like a power supply going bad or an issue with a switch.

There is an easy and linear sign of progression for this technology. It essentially boils down to alert-alert-recommend-automate. As many as 20% of enterprises operate at complete automation. Whereas, 60% of them are in the recommended stage. This helps them get the information they need to get the job done. Whereas, only 20% of organisations are looking to leverage AI only to get alerts so they can handle the issue themselves. Asenviornemnts get increasingly complex, it is bound to get more difficult to do that and to effectively fix things quickly. This is where the implementation of AI engines will pay off.

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