For years, network growth was relatively predictable.
More streaming.
More video conferencing.
More cloud applications.
More connected devices.
The industry optimized around human behavior. People clicked, streamed, downloaded, paused, and logged off. Traffic patterns followed routines that network operators and infrastructure providers understood well enough to model.
AI changes that.
Cisco’s recent report on the impact of AI on wide area networks points to something much bigger than just higher traffic volumes. What stood out wasn’t the prediction that AI traffic will grow. Everyone already assumes that. The more important takeaway is that AI traffic behaves differently.
That distinction matters.
We are moving into a world where software no longer waits for humans to initiate every interaction. AI agents continuously exchange information with models, APIs, databases, and external tools. They operate constantly, often autonomously, and at a pace that traditional enterprise infrastructure was never really designed for.
Cisco described the connection between AI agents and models as the “spinal cord” of agentic AI.
That feels accurate.
If that connection slows down, the agent slows down. If it breaks, the workflow breaks. The network stops being a passive transport layer and becomes part of the application itself.
That changes the role of infrastructure entirely.
One of the more interesting findings in Cisco’s report is that AI inference traffic flows are lasting roughly twice as long as traditional web transactions.
That makes sense when you think about how large language models work.
Traditional web traffic is usually bursty. A request gets made, content gets delivered, and the session ends quickly.
AI inference is different. Models generate responses token by token. Context gets continuously passed back and forth. Agentic workflows may involve multiple systems interacting simultaneously before a task is completed.
The result is traffic that is:
Cisco also found that AI agents can generate up to 450% more traffic for the same task compared to a human doing it manually.
That number should probably get more attention than it will.
Because most discussions around AI infrastructure still focus on GPUs and data centers. But the operational reality is that these systems create continuous consumption patterns across networks, APIs, platforms, and services.
The infrastructure footprint of AI is much larger than the chatbot sitting in front of the user.
A lot of enterprise pricing models still assume software usage is tied to people.
Seats.
Licenses.
Users.
Departments.
But AI agents do not consume infrastructure the way humans do.
An AI sales assistant does not log off at 5 PM.
An AI operations agent does not handle one task at a time.
An autonomous workflow engine does not care how many employees exist inside the organization.
It consumes:
Continuously.
That is where the tension starts to appear.
Many companies are layering AI onto commercial models that were originally designed for human-paced software usage. Over time, that becomes difficult to sustain, especially as AI workloads become more autonomous and less predictable.
This is one of the reasons the market is moving so aggressively towardusage-based and hybrid pricing models.
Not because usage pricing is trendy.
Because AI consumption itself is variable.
The telecom industry has seen this kind of transition before.
When networks evolved from voice to data, monetization models had to evolve with them. The same thing happened with cloud infrastructure. Static pricing struggled once consumption became elastic.
AI is creating another one of those moments.
Every AI interaction generates measurable economic activity:
These are not passive software sessions anymore. They are dynamic consumption events.
And the more autonomous AI becomes, the harder it is to force those interactions into fixed commercial structures.
This is especially relevant for industries already dealing with high-volume, usage-sensitive environments:
Many of these organizations are heading toward environments where infrastructure usage fluctuates constantly depending on model activity, automation levels, and customer behavior.
That creates pressure on traditional quote-to-cash systems.
One part of Cisco’s report that feels particularly important is the idea that AI agents act like “network power users.”
That framing is useful because it shifts the conversation away from AI as simply a software feature.
AI agents consume infrastructure aggressively.
They generate traffic continuously.
They maintain context.
They trigger downstream workflows.
They interact with multiple services simultaneously.
And unlike humans, they operate at software speed.
As agentic AI adoption grows, this creates a very different operational environment for enterprises and service providers. Infrastructure planning starts looking less like supporting users and more like supporting autonomous systems interacting with each other nonstop.
That changes assumptions around:
The impact is unlikely to happen gradually forever, either.
Cisco projects that AI inference traffic could represent 25% of total network traffic by 2035. Enterprise network traffic growth could increase roughly 9x because of agentic AI adoption.
Those numbers suggest the market is not dealing with a small optimization problem.
This is a structural shift.
The easiest way to look at this report is as a networking story.
But the more interesting interpretation is that AI is starting to rewrite the economics of digital infrastructure.
As AI systems become more autonomous:
That has implications far beyond networking teams.
Finance leaders will care because cost structures become harder to predict.
Product leaders will care because pricing models will need to evolve.
Operations teams will care because infrastructure dependencies multiply quickly.
Telecom providers will care because AI creates entirely new forms of network demand.
And software companies will increasingly discover that traditional monetization models were never designed for autonomous systems consuming services at machine speed.
The companies that adapt fastest will probably not be the ones with the most AI features.
They will be the ones that figure out how to operationalize and monetize AI profitably at scale.
Watch this podcast with Natalie Louie, Head of Product Marketing & Pricing at RightRev, to explore why unlimited pricing models often fail in volatile AI, SaaS, CPaaS, and telecom environments.
The discussion breaks down how usage-based models can quickly erode margins when infrastructure costs spike, why usage should be treated as exposure rather than guaranteed value, and how leading companies are building pricing guardrails, real-time visibility, and margin protection directly into their monetization architecture.