Earlier this year, we explored how AI is transforming workflows, tools, and business models across industries. Behind the scenes, another critical transformation is underway. AI is fundamentally reshaping the networks that support our digital infrastructure.
This blog examines how AI is impacting network design and operations, including automation, resilience, and the infrastructure necessary to support the AI workloads of tomorrow. Preparing your network now ensures that your organisation remains competitive, agile, and prepared for the future.
Traditional network management has long relied on human expertise. Engineers manually monitor alerts, configure devices, and respond to issues after they occur. As networks grow in complexity and scale, this reactive model becomes increasingly unsustainable.
Artificial Intelligence for IT Operations (AIOps) is changing this paradigm. By leveraging machine learning and predictive analytics, AIOps automates many aspects of network management. It collects telemetry from routers, switches, and applications, identifies anomalies, diagnoses root causes, and often resolves issues without human intervention.
Some of the most impactful benefits of AIOps include predictive maintenance that detects hardware degradation before it affects users, self-healing networks that isolate faults and reroute traffic automatically, and dynamic load balancing that adjusts data flows in real-time to optimise performance.
AIOps is more than a tool. It represents a strategic shift that allows IT teams to focus on innovation rather than constant troubleshooting.
AI workloads are inherently data-intensive. While GPUs often receive the most attention, the underlying network infrastructure is what enables large-scale, high-performance AI.
According to Gartner, the AI switching market is projected to exceed $10 billion by 2027. Cisco forecasts over $1 billion in AI infrastructure orders from cloud providers in fiscal 2025. These figures highlight a critical point. The network is no longer just a conduit for data; it has become a powerful platform for innovation. It is a performance enabler and a source of competitive advantage.
At the heart of AI computing lies the graphics processing unit (GPU). Initially designed for rendering graphics, GPUs have evolved into the preferred hardware for AI and machine learning tasks.
GPUs are uniquely suited for AI workloads due to their ability to process thousands of tasks in parallel. This enables efficient training of complex models on large datasets. They also scale horizontally, allowing organisations to build GPU clusters that function as AI supercomputers.
Additionally, software ecosystems like NVIDIA CUDA provide developers with fine-grained control over hardware acceleration, further enhancing performance. According to AnandTech, Nvidia currently holds approximately 92 per cent of the AI GPU market, with AMD, Intel, and others making up the remainder.
Behind every GPU cluster is a high-speed data fabric that moves information with minimal delay. Two technologies dominate this space: InfiniBand and RDMA over Converged Ethernet (RoCE).
InfiniBand is a high-performance, low-latency networking protocol widely used in high-performance computing environments. It supports specialised topologies and delivers exceptional throughput. RoCE brings remote direct memory access capabilities to standard Ethernet networks. It offers lower costs, compatibility with existing infrastructure, and easier deployment at scale.
The choice between InfiniBand and RoCE depends on specific use cases, scale, and budget. Organisations must carefully evaluate trade-offs when designing AI networks, considering not only bandwidth but also latency, scalability, and operational complexity.
AI is no longer confined to centralised data centres. Increasingly, it is being deployed at the edge in hospitals, factories, retail environments, and smart cities.
Edge AI enables real-time decision-making by processing data closer to where it is generated, allowing for faster and more accurate analysis. This reduces latency, improves data privacy, and minimises the need to transmit large volumes of data to the cloud. It also supports compliance with data sovereignty regulations.
According to Grand View Research, the edge AI market is expected to grow significantly through 2030. Deploying AI at the edge requires resilient, distributed networks that can support compute-intensive tasks in diverse environments. This shift demands a rethinking of network architecture, security, and observability.
As networks become more intelligent and autonomous, security must evolve in parallel. AI introduces new vulnerabilities, including model poisoning, data leakage, and adversarial attacks.
To address these risks, organisations should adopt well-established zero-trust architectures, where every device, user, and packet is continuously verified and authenticated.
Additionally, AI-driven threat detection systems can identify anomalies and respond to threats more quickly than traditional tools. Encrypting telemetry and data in motion is crucial for protecting sensitive information throughout AI pipelines.
The Cloud Security Alliance notes that AI and machine learning solutions can identify patterns, detect abnormalities, automate testing, provide actionable data insights, and even predict potential outcomes through analytics. Security must be integrated into the design of AI-ready networks rather than being added as an afterthought.
Whether building GPU clusters or deploying AI at the edge, your network must evolve to meet new demands. Key areas of focus include:
AI is transforming not only business operations but also the infrastructure that supports them. Networks are evolving from passive conduits to intelligent, automated systems that are critical for future workloads.
Investing in AI-ready networking today can help avoid costly rebuilds tomorrow and position your organisation for long-term success in an increasingly AI-driven world.
Assess your network readiness and partner with experts who can guide you through this essential transformation. The future of AI depends on networks that are prepared to keep pace with advancements.
AI switching market projections: Gartner
Nvidia Spectrum-X forecast: Barron’s
Arista Networks AI revenue targets: Arista Networks
Nvidia AI GPU market share: AnandTech
Edge AI market size: Grand View Research
AI-enhanced Zero Trust security: Cloud Security Alliance