The AI revolution is reshaping the telecommunications landscape in profound ways. At the recent MWC 2026 in Barcelona, Nokia chief executive Justin Hotard emphasized the urgency of adapting networks to support the dramatically changing demands of AI workloads. With traditional networks built around Service Level Agreements (SLAs), the evolution towards AI requires a major operational shift.
Hotard detailed how AI traffic has transformed from simple human-machine interactions to complex machine-to-machine communications. To stay ahead, networks need to transition to deterministic, programmable architectures. This shift will facilitate the seamless connection of data centers, improve transport networks, and enhance edge computing.
Nokia’s approach involves rethinking precisely how we manage this AI traffic. The company’s AI-RAN initiative, in partnership with tech giants like BT, Elisa, NTT DOCOMO, and Vodafone, illustrates this. Hotard clarified, “This is not about putting in a GPU to leave excess capacity for intelligence,” focusing instead on optimizing token delivery and performance.
This change from peak-capacity planning to a model focused on real-time demands highlights the evolving expectations of network reliability. The traditional five-nines or six-nines SLA model is not equipped to handle the dynamic requirements of AI, particularly in a 5G and 6G world. Networks must incorporate AI at every level, not merely as an afterthought.
Moreover, the recent partnership with Nvidia is not about spare capacity but about managing AI traffic more effectively. Nokia sees AI reshaping network architectures, driving increased investments into transport networks. This is necessary to address the massive data generated every month – 77 exabytes, to be specific.
Furthermore, Nokia highlights the importance of scale-across networking. As AI applications grow, interconnected data centers become critical, extending beyond the capabilities of single centers. This shift aligns with discussions from companies like Lumen, emphasizing east-to-west connectivity necessary for today’s smart networks.
In summary, the leap towards AI-integrated networks represents a systematic transformation, impacting infrastructure from data centers to the edge. By embedding AI within network layers rather than adjoining it on top, Nokia aims to enable networks to adapt to the real-time demands of modern technology.
As the realm of AI continues to expand, the question remains: Will the industry embrace this shift promptly enough to keep pace with the rapid evolution? With collaborations and infrastructure investments ongoing, it is evident that the industry has begun to pivot in anticipation of this AI-driven future.
