With 79% of people surveyed in a recent McKinsey report said they’ve had at least some exposure to generative AI, either for work or outside of work, AI is taking over. But as the adoption of AI grows in the workplace, IT teams need to consider the inevitable network-traffic implications that will come with it.
Why is it so important for IT teams to consider the implications of AI on network traffic?
AI is rapidly transforming the way we live and work, and this includes the way we use networks. Increased use of AI in corporate and cloud networks inevitably means that increased and new types of network traffic must be routed to and from data centres, cloud, across the LAN and WAN.
Most AI applications require more bandwidth, and if enough of them are used at once it can lead to an increase in network traffic. AI improves with the more data you feed it, so as a direct result of using more AI tools, more data will be transmitted across networks. Most organisations are fully reliant on a suite of business applications to function, and without enough bandwidth to support applications regularly using AI, application performance and user experiences can suffer.
While AI applications are designed to increase productivity, poor application performance will cause an immediate decrease in what employees are able to accomplish, especially those who are working remotely or from a branch location. AI applications can therefore certainly be beneficial for their organisation, but only when IT is prepared to manage them properly.
What are the most significant network traffic challenges that come with the increased use of AI? How can these be overcome?
Networks are more complex than ever before. It’s no longer just a campus or data centre with a few branches – most environments involve SD-WAN, fabric, connectivity for remote users and, occasionally, temporary locations that must be quickly set up and taken down. IT teams and network administrators can get quickly burnt out if all these parts of the network have to be managed manually. With AI monitoring all parts of the network and automatically optimising network traffic, generating alerts and creating IT tickets, that amount of manual labour is significantly reduced. IT teams can spend less time keeping the lights on, and more time finding ways to improve user experiences and use their networks more intelligently.
However, it’s also important that network administrators have full visibility into network and application performance so they can identify problems before they arise and address issues before they start widely impacting the organisation. If an AI application is being widely used while another application is drawing on the network in the background, administrators need a way to identify this and turn down the amount of bandwidth dedicated to other background applications.
Not every use case is the same, and network teams need to be able to respond to new demands in real-time. For example, in a college or university that is live-streaming a lecture and using an AI application to generate subtitles, network administrators need to be able to turn up the bandwidth for video traffic and that application, and turn down bandwidth for applications that are running in the background but not being actively used. When the lecture is over, IT teams can then readjust the network to whatever their standard performance needs require. A good first step for IT teams is to establish a baseline of network traffic before AI applications are rolled out and monitor how much traffic increases once those applications are used at the same time.
How can AI tools optimise application performance and user experiences?
Some vendors will engage in AI-washing, claiming that AI can solve every problem or that customers should automatically trust the recommendation, but this simply isn’t true. Most AI tools should be seen as assistants that can take over tedious, repetitive tasks and free IT teams to focus on bigger goals that require creative problem-solving.
Many AI tools in networking products are focused on network management and troubleshooting, which can create a significant benefit for the IT team. These tools can help identify patterns in network usage, note troubled devices that may become a future issue, or even automatically respond to common issues and resolve them before users notice any difference in performance. IT teams can become more data-driven and proactive, reduce risk for the organisation, and ensure a seamless experience for users.
When looking for a networking vendor that leverages AI, IT teams should look for solutions that will support them to focus on more valuable tasks, not those that are marketed as something that can automate an entire department. While these tools will eventually get to that level, realistically, they aren’t there yet.
What will be the impact of AI on IT networking teams in the future?
AI is poised to continue to impact networking teams in the years to come, but network teams will need to develop the right skills – it’s really less about keeping up with AI and more about ensuring network teams have the skills necessary to get the most out of AI and AIOps tools. As organisations lean more into AI tools and applications, they’re only going to be able to leverage them properly if they have well-defined, high-quality and well-understood data structures. Data engineering comes first before any meaningful, reliable insights and actions can be derived from the data that AI tools rely on.
In addition to recruiting talent, it will be critical to invest in upskilling workers in basic data engineering skills, as this will be necessary for organisations to better understand the concepts of data, ML and AI and unlock their true potential across the organisation. Overall, by embracing AI and addressing the implications, IT networking teams can improve their efficiency, effectiveness, and decision-making capabilities.
Other magazines that may be of interest - Data Centre Magazine.
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