Applying AI to network problems (Reader Forum)

Organizations are getting a ton of network-related data every minute of every day. There has been a massive increase in devices, applications, bandwidth, IoT equipment and cloud-based applications to deal with. Add to that the recent rapid expansion of the network environment (stemming from the pandemic and the rise of remote work), and it is no longer humanly possible to know everything that is crossing the network. 

Relying on the traditional approach of troubleshooting by sifting through log files or manually searching out problems is no longer sustainable.

Today’s digital world demands a reliance on artificial intelligence (AI) to ensure the best possible network user experience and to quickly troubleshoot issues that are akin to searching for a needle in a haystack. 

The importance of the experience

Networking success is measured more and more in terms of the experience provided to end users – i.e., bandwidth, uptime, etc. End users remember if their internet crashed, if their video call is interrupted or if they are unable to upload and download documents quickly. They do not remember the hundreds of times everything worked as it should. In today’s work-from-anywhere environments, it’s even more critical to get the end-user experience correct.

The other aspect of the modern enterprise where experience matters is with an organization’s internal teams. Are their jobs filled with mundane, repeatable, difficult tasks? Are they able to add value, or do they waste a majority of their time on troubleshooting network problems? 

Network managers and IT teams are facing an increase in network management complexity, and, at the same time, are under increasing pressure to speed time-to-value of any and all processes, such as network refreshes or deploying and operating a branch network. The combination of these issues also presents challenges for organizations looking to retain talented employees in the face of a hot job market.

By recognizing the importance of, and working to improve both the end user and network management team experiences, the operational burden on staff can be reduced, freeing up their time for more digital transformation-level projects that better support business initiatives. 

Improving the experience using AI

Applying AI tools to modern networking problems can go a long way to making these improvements in experience a reality. In no way will AI fully replace the role that an experienced network engineer plays, but it can provide the ability to scale and keep up with the ever-expanding environments and responsibilities folks in this role face. 

In some instances, the application of AI may even be able to eliminate the repetitive and mundane tasks from the job description. There are a few key ways that AI can help bring about a better experience:

• Visibility – Applying AI technologies to the network enables greater visibility into the traffic that’s traversing it. This enables network managers to see what’s happening at any given time, and be alerted to trouble that needs attention. They could then, for example, prioritize traffic as needed, in order to make the best connection possible. AI could be set up to only alert if certain conditions are met, or even to make adjustments itself if the variables are correct.

• Monitoring service levels – AI-powered visibility can help ensure the best possible user experience at all times. Once SLEs (service level experiences) are in place, network managers can use the technology to track whether SLE target levels are being met. For example, knowing that 92% versus 99% of user minutes met Zoom requirements is important – and could instruct the team that there is a potential issue to rectify. AI can help separate traffic by specific service, letting teams separate less important traffic (Windows Update Traffic) from meaningful traffic (Zoom) and see if one is affecting the other.

• Automating onboarding and offboarding – Onboarding and offboarding devices, equipment and applications are often the first things that jump to mind when one hears the phrase “mundane tasks.” These actions are critically important, but are repetitive and time-consuming. Applying AI to the problem can wipe the task from the job description, as rules and requirements are set and the AI handles the rest, allowing the employee to do higher-value tasks.

• Security – By utilizing AI, network managers can be automatically alerted to only those issues that matter, instead of having to sift through unimportant alerts or false positives. The AI can learn what issues are critical and which aren’t, and either take action itself, or bring to the team’s attention something that requires immediate attention. The ability to have an automated ally monitor potential security threats can go a long way toward preventing breaches, or worse.

• Compliance – Protecting compliance with industry regulations and security requirements – and reporting on the success and failure of compliance efforts – is another way that AI can assist and remove an important, but repetitive, task off of time cards. The AI can learn what guardrails need to be enforced and prevent non-compliance.

• Troubleshooting – Due to the improved network visibility that AI technology can provide, it can also help the team identify potential and existing problems and troubleshoot the cause. Traditionally, teams had to manually undertake trial and error to narrow down why a problem was occuring. Is there a loss of signal? Then check each device’s software and settings. If that wasn’t the problem, then perhaps it’s the Wi-Fi coverage area, which leads to wandering around trying to connect from different locations. The bottom line is that time would be wasted, along with valuable effort expended to locate an issue. With AI, the problem can be narrowed down and pinpointed right away – instead of taking days (or weeks) – letting the team fix the issue, lowering time-to-repair and speeding up a return to work.

• Repair insights – Troubleshooting with AI doesn’t have to stop with just discovering the issue. It can also assist in instances where the issue is pinpointed, but the team has no idea on how to make the needed repairs. Perhaps experienced team members have left the company, or are unavailable; or perhaps it’s a problem that’s never been encountered before. Regardless, with AI involved, steps to repair the issue can be recommended, enabling even a junior staff member to quickly make a correction. In some instances, the AI itself could make the corrections.

AI solves network problems

AI can offer network managers better visibility into network performance, anomaly detection that can ensure application performance and protect network uptime, and even automated troubleshooting that can self-heal the network before an outage can occur. 

The AI-driven future is rapidly approaching, and it will change the way network management and IT teams operate – and their experiences – forever.