AI in Action: Streamlining and Optimizing Network Management
Modern network environments are complex, and managing them is a significant challenge, let alone optimizing performance. Remote work, emerging technologies such as AI and IoT, and new cybersecurity concerns all make network management much more sophisticated than it was just five years ago.
Extreme Networks recently conducted a survey of more than 200 CIOs and senior-level IT decision-makers, and found that top priorities include securing networks, integrating networking and security, and evaluating AI.
What’s more, optimizing networks is top of mind for tech leaders: 86% of respondents reported plans to upgrade their network in the next 18 months.
So, how can these priorities – security, AI, and networking – come together to make life easier for IT professionals?
Using AI to Streamline Network Management
One of the fundamental ways to get started is through AI, which will improve IT operations with artificial intelligence integration. AI presents a tangible and practical use case for the enterprise network as it leverages capabilities to improve automation, filter noise, and reduce false signals.
Integrating AI into network management is a necessary step toward automation and optimization. It helps maintain service-level agreements, prevent downtime, monitor network security, and filter alerts. For network admins, this translates to improved efficiency, allowing them to focus on more strategic concerns, such as aligning network infrastructure to business objectives.
More granularly, AI can monitor the network, identify and proactively address anomalies, and significantly reduce the amount of time network administrators spend responding to false alarms.
Another advantage of AI is that it provides expanded visibility, opening the door to network analytics – an often-untapped business resource that can be used for business decision-making and future planning.
Driving Cloud-Based Networking Forward With AI
It’s no secret that CIOs love the cloud. Our survey shows 92% of respondents agree that cloud-based network management combats workforce challenges, addresses security concerns, and provides network analytics.
However, managing hybrid cloud environments poses the second biggest day-to-day challenge for IT leaders (considering their current network infrastructure), behind protecting against cyber threats.
AI simplifies cloud network management by improving visibility across cloud environments. It can also be used to support the management of one network that connects the campus, data center, and branch – wired, wireless, SD-WAN, IoT, and beyond. Unifying endpoints reduces risk and optimizes cloud usage, which is the essential next step for tech leaders dealing with distributed workforces and shrinking budgets.
Evolving Role of Network Managers
Another area of consideration is how the role of a network manager will evolve in the new world of AI. AI will never completely replace the human network manager but will instead make the role more efficient and more productive, with the ability to eliminate day-to-day mundane tasks so that managers and admins can focus on optimizing performance, detecting security threats, and improving operations more proactively.
Ultimately, AI's role is to support network management, configuration, and troubleshooting through automation and human intervention. This balance is key to foundationally understanding and embedding AI into your network operations.
Over time, CTOs should support network managers by providing educational resources on working with AI and best practices for analyzing the data that AI tools use and produce.
Understanding what data AI uses to complete its task will help network managers effectively manage AI tools by understanding their limitations and potential for network activity improvement. This will ensure the successful implementation of AI into the network, with the ultimate outcome of streamlining and optimizing network performance.
Transitioning to Generative AI in Network Management
AI can be incredibly powerful, but for all organizations, generative AI (GenAI) is likely to be more transformative.
IT is complex and extensive, and it’s impossible for even the most experienced IT professionals to know everything about IT operations. GenAI simplifies and streamlines IT and network management.
GenAI enables IT professionals to work faster and smarter by extracting intelligence from documentation, network, and device data to deliver proactive, real-time data insights. For network admins and managers, this makes troubleshooting, operations, and security detection easier and more seamless.
For CIOs and IT leaders, this translates to streamlining resources, minimizing overhead costs, and reducing operational costs to unlock more time and investment to drive product innovation (and revenue) forward.
Over time, GenAI-enabled insights will become even more innovative and customized to your network management needs. Its ability to provide quick and precise answers to technical questions by searching for specific information in technical documents will save significant time and become even more helpful as the AI taps operational data to tailor recommendations to the particular network environment it's working in.
Let’s use network security as an example. Under a Zero Trust Network Access framework, which is becoming the industry standard in today’s threat landscape, strict controls are implemented on all users and devices on a network, and they are continuously verified to ensure they are who they say they are and have not been compromised by threat actors. This can be overwhelming for IT teams to implement on their own, especially if they are not familiar with the strategy. However, a GenAI tool that has access to technical documentation and data on the network environment can provide answers to related questions at the click of a button, expediting timelines and reducing human error.
Overall, GenAI has the power to deliver proactive, automated problem-solving with predictive analytics and continuous monitoring, with less human intervention and assistance over time.
Optimized Network in Action
One of the best case studies of network transformation leveraging AI I’ve seen came from Oral Roberts University (ORU). ORU recently welcomed its largest-ever incoming class, meaning more devices and users on campus than the IT team has ever had to support. This, combined with the increased use of technology, video streaming, and even things like AR/VR in classrooms, could create a network management nightmare.
However, because ORU has tapped into AI, it can easily optimize its network and ensure that IT teams have the most up-to-date information possible when responding to potential issues and ensuring everyone on campus has the bandwidth needed so that learning is never interrupted. False network alarms are minimized, and the IT team has access to recommendations personalized to ORU’s specific environment, making them more agile and better able to keep up with the needs of students and staff.
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Final Thoughts
While network optimization can be a significant undertaking, it is necessary to keep up with today’s cybersecurity threats, distributed IT environments, and AI integrations. Luckily, AI, specifically GenAI tools, are available to significantly reduce the manual workload and simplify this transformation, making it easier for everyone to race toward their future goals.
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