AI’s growing demand on corporate networks

Artificial intelligence is no longer the stuff of science fiction and academia, it's quickly becoming a key part of doing business. From enhancing decision-making to automating processes, it has the potential to impact every professional occupation, industry and internal function - and the network team is no exception.

Networks are critical infrastructure for supporting AI applications and for enabling the organisation to meet its AI ambitions. For network professionals, however, the rise of AI presents a dual challenge: ensuring networks can handle the heavy demands of AI traffic while leveraging also AI to enhance network operations. 

Carrying more AI traffic over the corporate network

AI is incredibly resource intensive, driving a new wave of data centre construction and higher electricity consumption. Alongside these demands, organisations must also address the critical challenge of managing the rapidly increasing volumes of AI-driven network traffic. 

While many are aware of the first challenge, the ability of networks to cope with the rigorous demands of AI has received less attention. A new survey by Extreme Networks changes that - putting a number on how AI is impacting corporate network infrastructure. 

According to the survey, nearly half (49%) of respondents reported experiencing bandwidth challenges when they began their AI implementation. However, this figure could be overly conservative because every implementation (or attempted implementation) of AI in the business may not be visible to the network team. 

Many applications used in day-to-day operational contexts are now AI-augmented. In that scenario, the AI component is indistinguishable from regular application traffic; it may only be distinguishable if it is at a level that is consistently and measurably above historical traffic patterns associated with the particular application. 

Analyst firm Omdia predicts "that by 2025, AI will be involved in the majority of applications content carried by [telecommunications] networks." It is not a stretch to say that this will also be felt by the operators of networks internally at organisations as well.

The Strain on Networks – Where AI implementations have faltered

While AI adoption is on the rise - 84% of survey respondents have started integrating AI into their tech stacks - the road hasn't been without bumps. Nearly a third (32%) of respondents reported that they have yet to see a strong return on investment (ROI) from AI, underscoring the hurdles many organisations face in realizing AI's full potential. 

Access to appropriate bandwidth is not the only challenge. Some 39% of survey respondents admitted that their AI rollout was hurried due to demands from the C-Suite, and 30% said employees didn't receive the proper training or guidance to make the most of these new technologies. This often resulted in poor outcomes, as teams weren't fully prepared to handle the complexities of AI integration.

Furthermore, 26% of respondents reported that their AI implementations haven't led to significant efficiency gains. For 18% of respondents, AI hasn't yet lived up to its potential in their organisation, leaving a gap between expectations and reality.

For AI to succeed, organisations need to invest in the right infrastructure, like boosting network bandwidth and making sure employees receive the proper training. Rushing into AI without addressing these foundational elements can lead to missed opportunities for innovation and efficiency. 

AI as Network Ally

AI doesn't just place demands on networks; it can also help manage and optimise them. By incorporating AI-based solutions, network teams can significantly enhance operational efficiency and free up time for strategic priorities.

AI presents a tangible and practical use case for the enterprise network. It can continuously 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. 

For network admins, this translates to improved efficiency, allowing them to focus on more strategic concerns, such as aligning network infrastructure to business objectives. 

AI won't replace the network manager but will instead make the role more efficient and more productive, with the ability to eliminate day-to-day mundane tasks so they can focus on optimising performance, detecting security threats, and improving operations more proactively. 

Preparing Networks for AI-Driven Innovation

The successful implementation of AI into network operations requires preparation. Network teams must invest in the right infrastructure to handle increased traffic loads and ensure that AI tools are integrated thoughtfully into their environments.

According to the same survey, the vast majority (86%) of IT leaders are planning to invest in their networks over the next 18 months. A significant portion of these upgrades will focus on adopting cloud-based solutions that can support AI and other emerging technologies.

By addressing bandwidth challenges and adopting AI-friendly infrastructure, organisations can unlock new opportunities for innovation, efficiency, and growth. AI has the power to transform networking, but it's up to network teams to ensure their infrastructure is ready to support this next wave of technology.

As both a workload and an augmentation, AI is reshaping corporate networks, driving both challenges and opportunities. By preparing their networks to carry AI traffic while embracing AI-driven tools, organisations can position themselves at the forefront of technological innovation, optimising not only their networks but also their business as a whole.