ER TLC

The real value of AI-native networking is not just automation. It’s prediction.

RAD highlights how AI-native networks can move from reactive operations to proactive and predictive ones, identifying traffic surges, emerging faults, or performance issues before users are impacted.

As networks become more complex across edge, core, and multi-domain environments, reacting after the problem is no longer enough.

The shift is toward infrastructure that can continuously analyse, anticipate, and optimise in real time.

RAD

As AI adoption grows, the opportunity for service providers is moving beyond raw capacity.

RAD highlights a more service-led model built around:

✔ Premium 100G and 400G DCI
✔ Assured performance and traffic isolation
✔ Bandwidth on demand at the carrier edge
✔ Quantum-safe security and data-sovereignty support

The shift is important: AI connectivity is becoming not just a transport layer, but a managed service opportunity tied directly to enterprise performance and uptime.

Source: RAD , Carrier Ethernet for AI

AI agents are introducing a new traffic pattern at the carrier edge.

RAD describes these interactions as distributed, autonomous, highly dynamic, and latency-sensitive, increasing the need for:

• fast connection setup
• fine-grained traffic classification
• strict traffic isolation
• real-time prioritization

As AI-driven business services expand, the carrier edge is becoming increasingly important for maintaining predictable performance.

IoT is scaling fast, and so are the demands on infrastructure and resilience.

A few figures worth watching:

✔ Worldwide IoT spending is expected to surpass $1T in 2026.
✔ Global IoT connections reached 22.3B in 2025 and are projected to keep growing.
✔ In one recent IIoT market estimate, the sector was valued at $514.39B in 2025, with manufacturing holding the largest share.
✔ IBM’s latest global benchmark put the average cost of a data breach at $4.88M.

The opportunity is massive, but secure and resilient deployment is becoming just as important as scale.