Famous AI pioneer Oren Etzioni once said, “AI is a tool. The choice of how it is deployed is up to us.”
Companies choose how to deploy AI, and its use cases are evolving. Network teams typically use AI as a defense against potential security threats, a network management tool, or a method to enable automation. As network environments become more complex and distributed, they produce vast amounts of data beyond what humans can handle on their own.
Businesses can consider implementing AI to manage complex systems, like 5G networks, or collect data analytics. AI can monitor network performance and alert managers to potential issues before they arise. Some forms of automated AI can also solve problems without requiring human intervention.
According to a 2021 Gartner Reportthe adoption of AI for IT operations (AIOps) – which describes the process of IT teams using AI technologies, like machine learning (ML), to automate tasks – is on the rise among businesses. Gartner estimated the AIOps market to be between $900 million and $1.5 billion in 2020, and Gartner expects it to grow at a compound annual growth rate of 15% by 2025 .
One of the reasons AIOps adoption is increasing is that companies are on the cusp of digital transformation. As operations become digital, it becomes difficult for humans to analyze, monitor and manage the newly accumulated data. In fact, the top digital transformation trends of the past year included the deployment of ML operations.
Organizations are using AIOps strategies to “replace traditional monitoring tools” as they ultimately plan for a “post-COVID-19 post-pandemic environment dominated by practical outcomes,” the report says. New business demands and pandemic pressures have prompted organizations to deploy AI.
However, while interest in AI is growing, not all organizations are implementing it quickly. Benefits aside, AI is still a new and advanced technology that has yet to reach its full potential, which makes business leaders reluctant to deploy it – And they are not alone.
Network teams are also among those reluctant to implement AI. Read below to see what three network analysts have to say about the status of AI in enterprise networks and how they think networks will use AI in the future.
Editor’s note: Responses have been edited for length and clarity.
What role does AI currently play in the networks that have deployed it?
John Burke, Research Analyst, Nemertes Research: It is still early for the deployment of AI and networking. Corn, [in areas] where it currently plays a role, the rules focus on visibility for management purposes. What’s really going on in my network? What’s weird about what’s happening on my network right now? What are the abnormalities? It’s in the same vein for security: what’s really going on on my network? Which of the weird things going on in my network should I be worried about?
The purpose of AI in both cases is to come between raw data and humans and eliminate normal flutter and non-harmful, irrelevant anomalies. [so teams can] focus their attention on important things, be it performance or safety. On the performance side, it’s, ‘Where do I have patching issues that I need to fix?’ And in the longer term: “How should I plan for capacity in the future? This is more of a problem in data centers than anywhere else, but it is by no means limited to data centers.
Juniper just made an announcement about adding AI capabilities to its SD-WAN [software-defined WAN]. There will be many more in this vein from everyone including Network as a Service providers – especially SASE [Secure Access Service Edge] vendors – on using AI to better direct traffic, optimize delivery, and help resolve issues when things go wrong.
Are there any use cases for AI in 5G networks?
John Fruehe, Independent Analyst: 5G carrier networks are environments where many variables are constantly changing. The AI makes sense because you have a ton of data input that you provide on how things work. AI is of little value in stable networks where network traffic is the same every day and things don’t vary much. But, in a carrier network, things are constantly changing and AI can be used to help with much of the provisioning. As we deploy more 5G and can get more granular, a lot of these connections and switches are happening, and that’s where AI comes into its own.
Is AI likely to change the roles of network teams in any way?
Fruehe: Part of what’s happened is that the pandemic has absolutely decimated all of the traditional roles of [networking]. All major projects [network teams] had for 2019, 2020 and 2021 — [such as] migrations and network deployments – were thrown a huge wrench. I think teams have probably spent the last two years in scramble mode trying to figure out how to overcome all of these issues and get back to a stable position.
I don’t think we’re at a point where things are stable enough that people can start thinking about doing higher level things. There are still a lot of blocks and tackles to be made. Networks have become more distant than they were in the past. The biggest changes that have happened in networking are in the end-user location, which has pushed a lot of software-defined WANs and VPNs.
How do you see AI influencing networking in the future?
Bob Laliberte, Principal Analyst, Enterprise Strategy Group (ESG), a division of TechTarget: Because the environment is becoming more and more complex, there is more data flowing through the network. This adds to more complexity beyond human comprehension for effective management. This is where artificial intelligence and machine learning technologies will play a role.
It is important to note, however, that AI/ML is not intended to replace humans. There will definitely be times when AI/ML will alert and recommend but cannot make a change. If you physically have something wrong with a switch or a cable, there’s no amount of AI/ML or automation that’s going to fix that. A human has to replace a cable or swap out a switch or power supply.
The progression of technology use is Alert, Alert and Recommendation, and Automation. [ESG research showed] that 20% of businesses are fully automated. About 60% are in this alerting and recommending phase, which helps them leverage this intelligence to do their job effectively. The bottom 20% are organizations that said they want AI to just alert them to a problem so they can fix it themselves. Over time, as environments become more distributed and more complex, it will be more difficult to do this and to be able to fix things in a timely manner. This is where the AI engines will come into play.