A modern car’s parallel park assist feature is a great invention that saves time, but it can’t make intelligent decisions about where to park or take into account no parking zones and street cleaning days. A driver still needs to be present. That is, until fully autonomous vehicles become a reality. Self-driving cars will rely on understanding road rules and use artificial intelligence (AI) to make decisions without drivers. Data will be constantly fed into the automobile so as conditions change, so too will the AI-based decisions.
The same is true for self-driving networks. An AI-powered software-defined wide-area network (SD-WAN) knows all the rules and can adapt to changes based on business intent. SD-WANs are paving the way for the convergence of networking, security, and AI to help businesses address growing data challenges as they migrate to the cloud. Simply put, next-generation cloud-based apps require smarter networks that just work.
Running a network manually is a slow, erroneous process that involves labor-intensive manual configurations performed by people. Legacy networks are difficult and time consuming to manage and it’s something many enterprises continue to struggle with.
Data from ZK Research shows it takes enterprises on average four months to implement changes across a network. That’s too slow for today’s businesses, especially when it comes to security. With mobile devices, Internet of Things (IoT), and cloud computing creating many new entry points and shifting to the network edge, companies are putting themselves at risk by not responding to changes faster.
The amount of time it takes to identify and fix problems is a major drawback of maintaining legacy networks. One study conducted by ZK Research found 30 percent of engineers spend at least one day a week doing nothing but troubleshooting problems. On a broader scale, more than 70 percent of a company’s network budget goes toward maintaining the status quo. Modernizing the network and using AI for network operations can significantly bring that 70 percent down to something more reasonable so companies can invest in innovation versus simply keeping the lights on.
Human error is the largest cause of unplanned network downtime. Automation can help companies eliminate human error and free up time to focus on higher-level tasks. However, automation alone cannot completely eradicate mistakes as humans are still making the decisions.
The only way to achieve a self-driving network—one that can monitor, correct, defend, and analyze with minimal human intervention—is through automation and AI. When AI is embedded in an SD-WAN solution, the network gains extensive data processing capabilities and a richer understanding of network and application performance.
Take, for instance, a network administrator who wants to increase the bandwidth of an app, but the action is banned in a certain country. A self-driving wide area network would know the rules and make changes automatically and only when appropriate. The same network could predict problems before they happen through fault prediction and alert administrators. It may even adjust the issue on its own before end users are impacted.
Going back to the analogy of a self-driving car, AI is indispensable in areas where humans are prone to making errors. Using a combination of AI software, real-time data from IoT sensors, cameras, GPS, and cellular connections, autonomous cars can actively monitor blind spots, synch with traffic signals, or take safety measures in case of an emergency. A human driver wouldn’t react fast enough in those situations, just like a network administrator wouldn’t have the ability to quickly react to all the network changes manually.
Instead of fearing AI, network administrators should embrace it. An AI-powered SD-WAN can transform network management to free administrators to focus on how services are delivered and ensure their quality, instead of getting caught up in the mundane details of how it happens.
One final word of caution regarding AI-based operations. I’ve talked to some IT leaders who have experimented with AI-based systems and have ditched them when mistakes are made. AI systems, like self-driving cars, are not perfect and will make errors, but the threshold should be better than people instead of perfection. In network operations, people make a lot of mistakes. Machines will make far fewer and will use them as learning inputs to get smarter every day.