To fully realise the potential of AI, IP networks must evolve. They must become more than passive pipelines for data—they must transform into intelligent, adaptive, and resilient platforms that can support, enhance, and even be enhanced by AI.

The evolving role of networks in the AI ecosystem

AI depends on data. And data depends on networks. From training models to performing inference, AI workloads are heavily reliant on high-speed, high-volume, low-latency data flows. This is particularly critical in real-time applications, where responsiveness can directly impact safety, efficiency, or user experience—think automated manufacturing, telemedicine, or live translation.

The rise of distributed AI workloads—from on-device processing to edge computing and cloud platforms—means networks must not only handle massive throughput but also enable flexibility in where and how AI is deployed. Increasingly, infrastructure decisions hinge on considerations like latency tolerance, data sovereignty, bandwidth cost, and security posture. Networks must support seamless interconnection across this decentralised topology, ensuring AI can operate wherever it’s needed most.

In short, if AI is the brain of digital transformation, the network is its central nervous system.

Capacity, performance, and determinism

To meet these needs, networks must provide more than just raw speed. They require deterministic performance—consistent and predictable throughput, minimal packet loss, and ultra-low latency under varying conditions. This is essential for applications like AI vision, immersive AR/VR, and automated systems where milliseconds matter.

Equally important is the ability to scale. As AI adoption grows across consumer, enterprise, and industrial sectors, so too does the demand for bandwidth. Future-ready networks must be designed with modularity and scalability at their core, using technologies that can grow alongside changing requirements without excessive cost or complexity.

Traffic engineering techniques—such as segment routing, network slicing, and hierarchical quality of service—are playing a growing role in managing congestion and ensuring the smooth delivery of AI traffic. These approaches allow networks to differentiate between types of workloads and ensure critical applications always receive the resources they need.

Securing an expanding attack surface

As AI-enabled services proliferate, so too does the risk landscape. AI systems often handle highly sensitive data—everything from financial transactions and patient records to proprietary business logic. At the same time, the network becomes more exposed, with billions of devices and endpoints now serving as entry points for potential attacks, from botnets to state-sponsored cyber threats.

Adding to this complexity is the emergence of AI-powered cyberattacks, which use automation and pattern recognition to probe for vulnerabilities. The attack surface expands with every connected sensor, camera, and autonomous device.

To counter these risks, the industry is increasingly adopting multi-layered security strategies. These include real-time threat detection powered by AI, robust encryption across network layers, and emerging quantum-safe cryptography to protect data well into the future. The goal is not just to secure individual endpoints, but to embed trust into the network fabric itself—ensuring confidentiality, integrity, and availability for all data in motion.

AI as an ally in network operations

Interestingly, AI is not only increasing the demands placed on networks—it’s also becoming a key tool in managing them.

Modern networks are vast, dynamic, and complex. Manual operation is no longer viable at scale. AI and machine learning are becoming essential for everything from predictive maintenance and fault detection to traffic optimisation and SLA enforcement. These technologies can analyse real-time telemetry, detect anomalies, forecast usage patterns, and suggest or automate remedial actions—often before human operators are even aware of an issue.

Natural language interfaces and AI assistants are also emerging to help operations teams navigate complex systems more intuitively. Rather than relying on deep command-line expertise, operators can ask questions, generate configurations, or troubleshoot issues in plain language—democratising access to network management and speeding up time to resolution.

As AI continues to be woven into the operational fabric of the network, we move closer to the vision of autonomous infrastructure—networks that can sense, think, and act, with minimal human intervention.

Looking ahead: Networks that think, adapt, and evolve

So, what does it mean to build a network for the AI era?

It means designing for agility, scalability, and performance. It means embedding intelligence not just at the application layer, but into the very core of the infrastructure. It means treating the network not as an afterthought, but as a strategic enabler of innovation and competitiveness.

It also means acknowledging that this journey is not just about technology. It’s about partnerships, standards, sustainability, and shared learning. The infrastructure choices we make today—how we build, secure, and operate our networks—will shape how AI unfolds tomorrow.

We are still in the early stages of understanding AI’s full impact on society, industry, and the global economy. Yet one thing is already clear: AI is only as powerful as the network it runs on. No matter how advanced the algorithm or application, its effectiveness can be compromised by latency, inconsistency, or lack of resilience. Conversely, a secure, high-performance, and AI-optimised IP network can amplify the potential of AI—enabling breakthrough innovation, operational agility, and new digital value across sectors.

As AI drives new expectations for scale, speed, and security, the role of the network is shifting from background infrastructure to strategic enabler. This requires not only rethinking how networks are built, but how they evolve—how they learn, adapt, and self-optimise in real time.

Through close collaboration with forward-thinking enterprises and service providers, Nokia is driving this transformation. Our approach combines deep networking expertise with a clear focus on future-readiness—enabling partners to deploy AI more effectively, automate with greater intelligence, and secure data in an increasingly complex threat landscape.

The road ahead will be complex, but the opportunity is immense. As AI becomes more deeply embedded in every aspect of society, those organisations and ecosystems that invest in future-ready, intelligent infrastructure will be best positioned to lead, adapt, and thrive.

In an AI-driven world, the network is no longer a utility—it’s a differentiator. Building it right means building it to think.