AI Readiness starts in the network

round 60 percent of Swiss SMEs see AI as an opportunity (kmu.admin.ch) – yet their own networks are not keeping pace with this ambition. According to the Cisco AI Readiness Index 2025, nearly two-thirds of Swiss companies consider their infrastructure insufficiently prepared for AI applications (Cisco AI Readiness Index). This means that the biggest obstacle to widespread, value-generating use of AI lies neither in understanding the technology nor in data availability – but in the network itself. This article shows how modern  Enterprise Networks are becoming the decisive foundation for successful AI adoption.

Artificial intelligence is high on the agenda for Swiss companies. The share of SMEs that have already fully integrated AI into their business processes rose from 2024 to 2025 to 34 percent (kmu.admin.ch). However, according to the Cisco AI Readiness Index 2025, 65 percent of companies in Switzerland simultaneously consider their existing infrastructure not sufficiently prepared for AI applications.

This applies to companies in two different situations: those using AI services from external providers - such as cloud-based language models or AI-powered business applications - and those operating or further developing their own models. In both cases, the enterprise network is the critical factor. Companies using external AI services rely on stable, high-performance cloud connectivity and controlled data access. Those training or running their own models place even higher demands on throughput, latency, and network segmentation.

This is where a structural issue becomes visible: although many existing networks remain stable in day-to-day operations, they reach structural limits when it comes to data-intensive AI applications. A lack of transparency makes it difficult to detect issues early, historically grown WAN architectures increase complexity, and insufficient segmentation amplifies the impact of disruptions or security incidents.

At the same time, scalable architectures are often missing to reliably support growing data volumes and hybrid workloads. AI therefore exposes weaknesses that have long remained hidden in traditional IT environments.

The Network Resilience Guide 2026 by Axians shows how to identify these typical weaknesses and how to strategically evolve your enterprise network.

For AI applications to run in a stable, secure, and scalable way, they require an infrastructure designed for transparency, controllability, and resilience.

Key prerequisites include:

1. End-to-end visibility across network, cloud, and applications

2.  standardised and scalable network architectures 

3.  integrated security and segmentation concepts 

4.  high-performance WAN and cloud connectivity 

5.  proactive monitoring and clearly defined operational processes 

Modern Enterprise Networks therefore provide the foundation for reliably integrating AI applications into business operations in the long term - regardless of whether they are operated internally or consumed from external providers.