IT network managers, beware: AI is not a magic bullet

If there’s one constant in the tech world, it’s the ongoing tug-of-war between hype and reality. I’ve seen this play out whenever a new “transformative” technology arrives on the scene. With the emergence of artificial intelligence (AI), it’s back to the future as we ask how this promising advance will change network management. 

In theory, AI should be a game-changer. Network teams will be able to identify problems in real-time and get ahead of potential trouble spots before they become critical. The same goes for tracking traffic patterns and managing network performance. The upshot: better use of network capacity, fewer support calls, and happier users.  

But before rushing in, network managers should take a closer look at what an AI transition means in practice and try to separate the hype from the reality.

Take stock of your infrastructure

With complexity on the rise and device proliferation at a record pace, network managers’ jobs have become that much harder. IT budgets are still shrinking, and with organizations looking to reduce network support spend, stretched IT departments are operating at dangerously thin levels.

This is where network teams can use AI to dig out of a hole.

The ability to more rapidly remediate and resolve problems translates into reduced network downtime and improved network performance — all the while driving down overall IT costs. It also helps deliver a great experience for customers with fewer support calls and fewer complaints.

Here is a real-life example of how the industry could help support that. By building AI into networking solutions, technology providers can create conditions in which when a customer reports an issue, they take a snapshot of the entire network and run the data through a learning engine to figure out what happened.

Using an AI/ML engine that learns from issues seen on other customer networks ensures that problems, once seen, are not repeated elsewhere. This is a huge time saver as problems can crop up anywhere. A glitch might be connected to software loads on an access point. Or perhaps it’s in the supporting network. But with AI’s help, an organization can now get a granular picture of what’s going on in a fraction of the time it previously would take to troubleshoot the problem.

Unlock big data

AI is going to be particularly useful when it comes to parsing the immense amount of client telemetry generated by a network infrastructure. In the past, the only way to extract information from all this data was with the help of (highly paid) experts who knew their way around different network technologies. However, if a company couldn’t afford the right personnel, this trove of valuable data remained largely underutilized. This gets particularly amplified when customers deploy network solutions from different vendors, thus preventing a single view into the network.

With the help of AI tools, organizations can now solve this big data problem and get the insights they need to address questions facing IT departments, including:

• Which sites and clients are facing a poor network experience?
• What are the root causes of poor performance?
• Which sites are running at full capacity, and what network changes are required to improve the situation?
• Can the network be automatically scanned on an ongoing basis to maintain a good security posture for the network?
• Are IoT devices introducing security vulnerabilities?
• Are network services functioning well during peak times in my network?

Don’t get sucked into the hype

There is no doubt that AI is becoming more relevant to network management all the time. And as processing power increases, the technology will continue to get better. But be smart about how you use it. Don’t ignore the fact that AI is not something that should get applied indiscriminately. 

Some mundane and manual tasks are still better left automated. For example, you don’t need AI to issue network patches. That’s why I believe not everything can or should be turned over to AI, which can get pricey when you deploy these kinds of solutions. 

Focus on your use case. What business problem are you trying to solve? This might seem rudimentary, but too many times, this basic question gets ignored. 

Second: does it fit your economics?  Every company has to adhere to a budget. Make sure that any AI deployment doesn’t break the bank.

Third, test it out to make sure that the network you’ve deployed truly delivers the desired outcomes. Is it helping you solve the business problem? How is it doing that? And is it working reliably? 

Choose pragmatically, not based on hype

There are a number of tools out there. Some of them are AI-powered, and some are not. Don’t get sucked into the hype. Instead, make sure you pick the ones that solve your problem. Otherwise, your expenses will only exponentially increase.

Most of all, keep in mind that this AI transformation is not going to take place overnight. Throughout my career, I’ve seen this play out every few years as markets sort out the proper balance between enthusiasm and excessive trust in new technology. This is all exciting, but plan your journey in increments.

As AI starts to garner more trust and more automation happens in the network, you can build out your capabilities accordingly. This is a journey that will take time, and your patience will pay off. So, take it step by step.

Rad Sethuraman is VP of product management at Cambium Networks.