Artificial intelligence (AI)represents one of the most revolutionary technologies ofour time. As AI, with the help oflargelanguage modelsand machine learning, continues tochangecloud-based technologies and redefine how businesses operate, it’s clear that the role of the corporate network, as a provider of basic utility services, is rapidly evolving.
The network is advancing to become a strategic enabler and the goal of intelligent, secure and high-performance networking is being realised within the scope of an AI-driven world.
AI is revolutionising network infrastructure by optimising operations and augmenting security, while minimising the necessity for manual intervention.
In the past, conventional network management depended extensively on manual configuration and oversight, resulting in time inefficiencies and a high likelihood of errors.
Today, AI-driven systems offer predictive analytics, automation and real-time monitoring, significantly enhancing network performance and security.
More changes are coming from cloud-based AI tools from major players like OpenAI, Amazon Web Services, Microsoft Azure and Google Cloud. These tools are optimising the ways in which organisations access and apply intelligence across the enterprise. And they offer game-changing capabilities − from real-time analytics and autonomous operations, to hyper-personalised user experiences.
However, the services they provide are incredibly demanding. To function reliably, AI workloads depend on low-latency, high-throughput networks that can seamlessly bridge distributed systems andprocess vast amounts of data quickly and reliably.
Robust networks represent the bloodstream of AI, as confirmed by Yang Pu, a senior research associate at BNP Paribas Exane. “As AI continues to push the boundaries of technological innovation, the demand for sophisticated networking solutions will likely intensify,” he says.
This intensification will be driven by massive volumes of data that networks will be required to carry in real-time, with minimal disruption.
Against this backdrop, organisations can expect AI outcomes to be only as good as the data feeding the AI tools and the responsiveness of the systems delivering them.
In essence, there is a two-way relationship: AI needs the network, but networks also benefit from AI.
This sentiment is echoed by BNP Paribas Exane CEO Karl Ackerman, who says that as AI technology evolves, so the importance of networking infrastructure will continue to grow, presenting unique opportunities for business and investors alike.
He alludes to the fact that AI algorithms analyse real-time data, and any network bottlenecks can severely impact performance, accuracy and effective decision-making.
Additionally, security is a top priority because AI systems frequently handle sensitive data. In milliseconds, AI-powered cyber security systems need to analyse and react to anomalies across huge networks. Throughput, signal integrity or data packet prioritisation slowdowns can all compromise the AI's overall usefulness.
It's not a one-way reliance, though. AI is also changing how networks are constructed and run. For example, AI-enhanced SD-WANs optimise performance by dynamically modifying traffic patterns. And by using predictive analytics for intelligent maintenance, uptime is increased.
Moreover, compared to conventional systems, AI-based monitoring solutions identify and address irregularities appreciably faster. In essence, there is a two-way relationship: AI needs the network, but networks also benefit from AI.
“AI has moved beyond being a customer of the network − it’s now part of the management team,” says Tommi Uitto, head of Nokia’s Mobile Networks Business Group. He points to the next frontier − reshaping networks with advanced AI and automation.
“As networks evolve, so too must the underlying intelligence that makes them more efficient and productive,” says Uitto. “This is not just about faster networks; it’s about smarter and more adaptive optimisations that support organisations’ business targets. Investments in autonomous networks are critical to enable the next level of transformation.”
Despite this elevated status, it’s important to clearly define the network’s role. The network is vital − but it's not the sole pillar. Without high-quality data, even the most powerful AI models are meaningless. And without the compute muscle of specialised hardware accelerators like graphics processing units and tensor processing units, AI cannot run at scale. The network must be seen as part of a broader ecosystem.
In this light, the urgency surrounding intelligent networking is set to increase. As AI is deployed closer to the network’s edge − whether in remote healthcare, or industrial internet of things environments, or autonomous vehicles – networks will have to contend with greater complexity, more endpoints and tighter performance expectations.
It’s no longer enough to build large, centralised data pipelines. Enterprises will require agile, decentralised network architectures capable of processing and transmitting data across fragmented geographies.
This means more fibre, more 5G, more intelligent edge gateways and more AI at the network level itself.
For South Africa, this presents both a challenge and an opportunity. Lack of skills development, legacy infrastructures and connectivity gaps in some regions will make it harder to support sophisticated AI workloads.
However, the country’s robust compute capabilities, a viable research ecosystem and forward-looking specialists in the reseller community armed with the latest solutions are poised to open doors for organisations looking to leapfrog traditional models by embracing AI-optimised networks.
In SA and across the African continent, the strength of the push toward digital transformation in sectors such as banking, healthcare, mining and agriculture will depend heavily on the convergence of AI and networking to boost strategic opportunities.
To this end, SA should boost investments in digital infrastructures, support AI hubs and innovation, and prioritise education and reskilling for the imminent AI-centric future.
Going forward, AI’s challenge to users lies in its latent abilities. Corporate infrastructures must evolve to be context-aware. This means understanding the needs of AI systems in real-time and being able to adjust accordingly and dynamically.
The future will only work well if it’s supported by fast, secure, reliable and intelligent networks. And even though networks won’t be the stars of the AI show, they’re what will make the show possible.
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