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The Critical Aspects of Applying AI to Your Networks



Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise software categories. Bob Friday of Juniper Networks takes a deep dive into the critical ins and outs of applying AI to your networks.

Artificial intelligence (AI) is everywhere these days. You can’t skim LinkedIn or read an industry trade publication without being bombarded with news about the latest company to wrap itself in an AI blanket. We are reaching the point where organizations need to learn how to cut through the noise and understand what the technology truly is about– and whether it can be applied to their critical business processes in a way that makes a difference, increasing efficiency and productivity.

The networking industry is no different– there has been a great deal of buzz. Expanded use of AI technology in the future will likely change how networks are set up, managed, and optimized. In fact, approaches such as AIOps are already moving networking from a paradigm of managing network elements to one of managing the user experience. But for companies looking to incorporate AI into their current operations, the main questions remain: where should it be applied, and can it truly help me?

In the networking industry, there is an area that is repetitive, includes mundane tasks, and is ripe for improvement. I’m talking about the process of identifying network service problems and repairing them. AI is a transformational step in the evolution of automation, allowing us to build solutions to manage and operate networks on par with human IT domain experts.

As a technology vendor or service provider, the team’s reputation is at risk when there is a service disruption, lag, or outage. Every second down increases customer dissatisfaction and decreases employee productivity. In fact, Gartner estimates that downtime costs about $5,600/minute. A team will be judged by whether it can solve the issue and return service quickly and easily, regardless of who is at fault. Applying AI to network management means that problems can be pinpointed faster, and solutions implemented quicker. This is the perfect example of a focused area to apply the technology– and one where it can make a real difference.

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A Starting Point: Focusing AI on Service Disruptions

The modern definition of a network continues to expand. With every new piece of equipment, connected device, or connected remote home office, the network that needs to be optimized for performance grows. The larger it gets, the greater the time and effort that’s required to monitor it. Teams need to ensure uptime, watch for security issues and solve any lag or disruptions quickly.

In the past, as network needs expanded, so did the size of the team tasked to manage them. This is no longer the case for several reasons – skills shortage, the high cost of talent, hiring freezes, etc. Teams are tasked with managing a greater number of variables without additions in staff that can help them to do so. In addition, IT teams must keep up with the rapid pace of change within an organization and the industry itself. It’s impossible to focus on digital transformation when constantly troubleshooting issues. The days of pouring over log files and responding to each and every alert as they come in feel like a relic of ancient history.

This is where AI can help. Because of the conflict between the number of tasks that need to be done, the repetitiveness of network monitoring, and the need to do it cost-effectively, a focused implementation of AI technology can make a difference – and also provide the leadership at an organization with a solid example of why this technology has a bright future.

AI is going to touch every aspect of society. In healthcare, we want to ensure our doctors have an AI assistant to help diagnose our symptoms. In agriculture, we want to make sure our farmers are using the latest AI technology to detect weeds in their fields and minimize the pesticides used on our food. And in networking, we want to ensure our network providers use AI to provide the best mobile internet experience.

By harnessing the power of AI in network monitoring and management, service issues can be swiftly pinpointed, and intelligent assistance can be provided in rectifying them. AI can help to minimize or eliminate the number of alarms and alerts that need to be responded to, helping team members prioritize where to spend their limited time and resources.

When applied to a networking monitoring and management situation, true, mature AI technology can even go a step further. AI now allows us to join Zoom data with network feature data and build models that can accurately predict a user’s Zoom performance, or even explain a user’s low performance. More than just monitoring and eliminating false alarms, when there is an outage or disruption, AI can review devices and connections in the affected area, methodically working through a checklist until the root cause is found. Without AI, something as specific as tracing an outage back to a severed power cord could take a human user hours to discover.

AI can dramatically cut the time it takes to locate a problem– and in some cases, can even be trained to provide technicians with advice or assistance in fixing it. This will enable IT and network management teams to stay one step ahead of employees’ needs and can provide invaluable guidance to team members who may lack experience.

Critical AI Considerations

While network monitoring and management is a ready-made problem waiting to be solved by AI, there will likely be other areas that AI can assist with, especially as the technology matures over time. To prepare for the expanded use of AI, there are some key variables for organizations to pay attention to:

  • Quality: As the saying goes, “garbage in, garbage out.” AI is only as good and accurate as the data and information it runs on. Invest in tools that can help clean and validate data to maximize the potential of any AI efforts.
  • Integration: How will AI work with the current network infrastructure, tools and processes? Will this introduction seamlessly interact with other components efficiently and effectively, leveraging existing data sources and improving decision-making? Ask these questions before getting started, or time-to-value may be delayed.
  • Maturity: Has the technology being worked with been tried and tested by other organizations in similar situations? Does the technology have years of development and real-world experience informing its operations, or is the organization a test subject? The more mature the AI is at the start, the stronger the impact.
  • Scalability: AI can tax the resources of an unprepared organization. Does the organization have the necessary infrastructure to support growing the use of AI? As data volumes and processing demands increase, will existing hardware, storage, and network capacity tools be able to handle the increased attention? Consider these possibilities at the start, even if the answers aren’t needed right away.
  • Resilience: Is the AI being used robust and resilient? Can it handle disruptions or attacks? Be sure to test different models under different scenarios to evaluate their performance and identify potential vulnerabilities.
  • Collaboration: Encourage collaboration between AI and human operators to augment decision-making and improve network operations. Value the role that experienced team members can have in getting the most out of AI tools. Remember that the goal is for AI to be a tool that enables IT to improve overall performance.

Final Thoughts

The future of network management and users’ mobile internet experience lies in harnessing the power of AI. An organization’s technology must function flawlessly all the time. Suppose end-user employees cannot access critical data, applications, and information for any prolonged period. In that case, the business operations will suffer– and the blame, fairly or unfairly, will fall on the IT team. AI can give organizations a competitive edge by enhancing the efficiency and effectiveness of the team, helping to identify and correct service disruptions and outages faster than ever before. It can review and prioritize the massive amount of alerts an organization faces, troubleshoot causes, and ensure that the overall network experience remains positive for end-users.

With AI in place, the IT culture can be changed from reactive to proactive, enabling team members to focus more on strategic initiatives and innovation instead of wasting hours locating bad cables.

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