The Hidden Network Impact of Embedded AI in Enterprise

Over the past year, enterprise software has undergone a material shift. AI is no longer confined to innovation labs or specialised use cases. It is now embedded directly into the SaaS applications employees rely on every day, such as Microsoft 365, Salesforce, SAP, Oracle Fusion, ServiceNow, and many more. These AI capabilities are undeniably valuable. They improve productivity, automate routine decisions, and unlock new insights across the organisation. What is less visible, however, is the impact this shift is having on enterprise networks.

AI-enhanced SaaS applications are changing how networks are consumed, often without any deliberate “AI initiative” driving that change. 

Omdia’s latest research indicates that almost one third of global network traffic is already AI-enhanced, largely driven by AI features built into everyday enterprise applications rather than standalone AI platforms. For many organisations, this change has happened incrementally, and often goes unnoticed, until rising utilisation, inconsistent performance, or user experience issues start to surface. 

When familiar applications behave differently 

Historically, SaaS traffic has followed predictable, human-paced patterns. Usage aligned to business hours, scheduled processes, and well understood growth trends. 

Embedded AI alters those assumptions. Features such as automated summaries, real-time recommendations, background analytics, and agentic workflows introduce continuous machine-driven activity alongside user interactions. In some cases, these applications no longer idle in the traditional sense. The same Omdia report notes that certain AI workloads operate as persistent micro batches, rather than discrete sessions, effectively blurring the distinction between peak and off-peak network usage. The result is higher baseline demand, more frequent traffic bursts, and reduced tolerance for latency, jitter, or packet loss, even for applications that were once considered relatively forgiving. 

The growing tension with best effort connectivity 

Many enterprises continue to rely heavily on the public internet to access SaaS platforms. That model has worked reasonably well for transactional applications, but AI-enhanced SaaS is far less tolerant of variability.

According to Omdia’s 2025 enterprise survey (part of the same research), reliability emerged as the number one driver of networking investment decisions, ahead of bandwidth expansion and cost reduction. This reflects a growing recognition that AI-driven application features degrade quickly under inconsistent network conditions. It is therefore not surprising that around 60% of enterprises are planning to rebalance away from pure public internet connectivity towards more business grade options. This is not about replacing the internet, but about being far more deliberate in how different application flows are connected, prioritised, and protected. 

Why the physical network still matters

While overlays such as SD‑WAN and Secure Service Edge play an important role, Omdia’s findings are clear: they cannot fully mitigate the constraints of the underlying network.

AI-driven traffic places renewed importance on physical infrastructure, peering depth, cloud adjacency, and routing control. These factors have a direct impact on application performance, resilience, and increasingly, data sovereignty, particularly as regulatory attention expands from where data is processed, to how it traverses networks.

What enterprise leaders should do next 

Embedded AI inside enterprise SaaS is not a future consideration. It is already reshaping network consumption in subtle but significant ways. Organisations that recognise this shift early and align their connectivity strategies accordingly will be better positioned to support AI-enhanced applications without unexpected constraints or performance bottlenecks. Actionable steps you can take:  

• Make AI-driven traffic, including AI features embedded in everyday SaaS, visible. This involves monitoring changes in application behaviour such as new traffic bursts, usage patterns and in automated workflows.
• Revisit planning assumptions built around predictable, human-paced usage.
• Be intentional about connectivity: distinguish best-effort from performance-sensitive flows, prioritise and allocate.
• Treat the physical network, not just the overlay, as part of AI readiness.