Understanding the role of AI in network infrastructure

By Amit Sanyal, Data Centre Specialist, Juniper Networks

When it comes to IT operations and network infrastructure, the technology is proving particularly powerful, assuring optimal end user experiences and saving global enterprises significant time and money.

By extending AI from the end user across all layers within a network environment, outages, trouble tickets and application downtime will eventually be things of the past. Yet, for AI-powered networking to be truly optimised, it must be embedded in every step of the user journey, with solutions designed from the onset to leverage the right data via the right infrastructure to deliver the right real-time outcomes.

And this is what we see in AI-native networking.

The emergence of AI-native networking

AI-native networking is the term given to a new and exciting approach to the development, deployment and management of computer networks which have AI applications and capabilities at their core. And there are two primary cases for an AI-native network.

The first is AI for networking, where AI is used for IT operations (a concept known as AIOps) to identify patterns, anticipate network behaviour and detect anomalies. It can make proactive suggestions or self-driving corrections to optimise user experiences and simplify IT operations. Every connection within a networking environment is assured to be reliable, measurable and secure for every device, user, application and asset interacting with the system.

The second case is networking for AI, whereby data centre networks are optimised tosupport strict requirements for AI training and inference workloads. These environments demand ultra-fast, congestion free and reliable GPU interconnects, making the network a cornerstone to ongoing success of AI initiatives.

Ensuring the best experiences for users and operators

IT teams and organisations of all sizes can benefit from AI-native features that deliver simpler, more productive and assured online experiences at scale. These advantages include reduced configuration errors, automated and efficient workflows as well as the delivery of reliable and secure user connections.

Rolling out and scaling new networks brings complexities and human error often drives the bulk of such issues. An AI-native networking platform enables operators to proactively identify and resolve issues. Reaching a level of efficiency allows them to devote attention to innovating digital services and applications for the end user.

To scale effectively, organisations need to manage increased traffic while operating with the same or reduced budgets and a smaller workforce. To achieve more with fewer resources, operational teams must shift focus from routine tasks to strategic thinking, allowing them to concentrate on driving business results. AI-native networking enables operators to act on insights automatically without the need for human intervention - addressing any problems before they lead to downtime and poor user experiences.

Reimagining networks for the age of AI innovation

As data centre footprints expand and compute demands grow rapidly within and across regions, networking becomes increasingly important especially when handling fast-growing and performance in heavy distributed AI workloads, like large language model (LLM) training and inferencing.

Today’s AI/ML clusters can consist of thousands of GPUs in a data centre providing massive, parallel computational power required to train modern AI models. Distributing workloads across GPUs and then syncing them to train the AI model requires a next-generation network that can accelerate job completion time (JCT) and effectively reduce tail latency, which is when the system is waiting for that last GPU to finish its calculations.

AI-native networking effectively builds and reaps the benefits of AI application performance while simultaneously saving a lot of time and money.

Empowering the future of networking

By adopting AI-native networking solutions, organisations can determine network performance issues well before they can impact the user experience. This is a game changer for IT and network operations.

The technology empowers a move from reactive to proactive troubleshooting and even enables predictive network troubleshooting and management. This is particularly important for organisations within critical national infrastructure (CNI), including healthcare, government and education. In these organisations, networking services are crucial to support internal and external communication, enabling and optimising operations and improving the lives of end users, customers and the wider public.

Ultimately, the rise of AI-native networking combined with the integration of virtual network assistants (VNAs) marks a pivotal shift in global digital connectivity—empowering IT teams to interact with the network infrastructure in natural language, quickly identify root causes, receive prescriptive guidance and take automated actions.

Spanning from end user devices to application infrastructures, AI-native networking presents a forward-thinking solution to recurrent challenges in maintaining network performance. This includes capabilities to mitigate network outages, expedite troubleshooting and significantly decrease application downtime.

The advantage of AI-native networking is that it offers unparalleled levels of automation, intelligence and reliability. By utilising AI algorithms to streamline tasks and analysing volumes of data to predict network behaviour in real time, AI-native networking is elevating the current state of network connectivity.