Network downtime is difficult to avoid — 91 percent of CIOs experience it at least once per quarter. To reduce the risk, 92 percent have increased spending on network resilience, with a good chunk of that extra money dedicated to identifying and addressing network issues.
But spending alone can’t solve the challenges that come with increasingly complex network environments. To address emerging issues, companies need tools driven by artificial intelligence and machine learning that excel at monitoring, managing and maintaining network uptime.
How AI and ML Improve Network Resilience
Machine learning and artificial intelligence are similar but not identical. ML refers to the set of processes that collectively make up an AI framework, while AI tools are designed to deliver a specific function. In the case of networking uptime, ML processes might include algorithms capable of detecting unexpected network events and classifying them under designated rulesets, or tools that monitor user behavior to assess potential points of compromise.
AI solutions, on the other hand, use multiple algorithms to take intelligent action based on context. For example, if user actions lead to fluctuations in network stability, AI tools can terminate sessions and send reports to IT teams.
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In practice, AI and ML offer operational benefits in three key areas.
- Repetitive operations: Large network issues start with small network problems. Detecting these problems, however, requires accurate, repetitive monitoring that is both time-consuming and error-prone. AI solutions excel at completing repetitive processes and uncovering data that can be captured and used to create a better understanding of network environments.
- Data analysis: Visualization plays a key role in network uptime. The more companies know about what’s going on inside their networks, the better they’re able to minimize downtime risks. AI and ML solutions can help identify common patterns, such as where networks are dropping packets, where and when networks are busiest, and where traffic flows may be impeded. By combining this data, companies can spot potential failure points.
- Decision-making: The evolution of AI has given rise to the concept of AI operations, or AIOps. This term describes the platforms and processes that allow IT to make faster, better decisions in networking. For example, solutions such as Juniper Mist offer tools that will both predict failures and suggest solutions, allowing organizations to be proactive rather than reactive.
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Networking Companies That Are Capitalizing on AI
Four networking companies account for most corporate network switches: Juniper Networks, Cisco Meraki, HPE Aruba and Extreme Networks. Each offers tools for AI-driven network monitoring. For example, the Juniper Mist solution includes natural-language search capabilities to help IT teams quickly pinpoint potential problems, while the ThousandEyes platform from Cisco helps companies discover and visualize all dependencies across their networks.
The percentage of CIOS that experience network downtime at least once per quarter.
Source: Help Net Security, “Businesses Count the Cost of Network Downtime,” June 29, 2023.
While established enterprises often have a switch type they prefer based on operational needs or the use of legacy technologies, rapidly growing small businesses may have a mix of switch types and vendors that evolves organically as operations expand.
In both cases, it’s critical to have the right approach to switch deployment and management to make the best use of AI tools. For enterprises, the layering of additional toolsets can improve monitoring and management at scale. For SMBs, a consistent network platform can deliver improved visibility into operations and outcomes. Experienced service providers such as CDW are ideally positioned to help companies navigate the growing market of AI and ML tools.
Bottom line? Complex networks need intelligent monitoring to help organizations ensure reliable operations. AI and ML solutions deliver the accuracy, analysis and contextual awareness necessary to keep networks working.
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