Artificial intelligence is profoundly changing enterprise and service provider networks, and operators must evolve to support AI workloads, according to a Cisco executive who testified last week at a U.S. Senate subcommittee meeting.

Bob Everson, chief architect of provider mobility with Cisco, spoke before the Subcommittee on Telecommunications and Media, which falls under the U.S. Senate Committee on Commerce, Science, and Transportation. Everson shared Cisco’s perspective on the status of AI and focused on two core questions: How is AI reshaping our networks, and how can networks leverage the power of AI?

The July 30 hearing, titled “Intelligent Networks: Powering Artificial Intelligence and Transforming Communications,” was planned to explore how the rapid adoption of AI has impacted network infrastructure. In her opening statement, U.S. Senator Deb Fischer (R-Neb), Chairman of the Senate Commerce Subcommittee on Telecommunications and Media, said, “We will explore how widespread AI use has forced networks to evolve, requiring more capacity and more complex designs so that AI can run efficiently on those networks. We will consider how government, providers, and other industries are responding to that demand. Private companies have invested hundreds of billions of dollars in network deployment in recent years. Various federal broadband programs have also provided billions to support targeted network deployment and maintenance throughout the country.”

Others who testified came from organizations including U.S. Telecom, Vanderbilt University, and Nebraska Public Service Commission.

AI’s impact on network traffic

Everson told the panel that AI is changing not only the volume of network traffic, but its behavior. He noted that Cisco measured a fourfold increase in AI inference traffic over eight months. Traditional networks have been optimized for content flowing downstream, but AI is far more two-way and uplink-intensive. Prompts, context, sensor data, and agent activity all travel back toward AI models, and the resulting connections are active longer than conventional web transactions.

“AI agents amplify these effects by operating at software speed,” Everson said. “In our testing, an agent generated 450 percent more traffic than a person performing the same task, and roughly 70 percent of that additional traffic was inference.”

He also cited specific data from enterprise environments. “In campus and branch networks—like the one that powers the Senate office building we are sitting in today—we have already seen customers report a 34% increase in traffic tied to AI workloads over the last 12 months, and they expect to see a 96% increase this coming year,” Everson said. “Half of enterprise customers report that AI demand is concentrated on their Wi-Fi networks, and 73% of organizations already face or expect to face campus and branch capacity limitations within the next 24 months.”

This is largely because large majorities of organizations report increases in east-west traffic, latency-sensitive traffic, and continuous, automated AI traffic. While the large majority of AI to date has come from foundation models running on central infrastructure, enterprises are increasingly deploying small language models, open-source models, and specialized models—such as vision and voice models—which can be distributed throughout the network. Everson emphasized that each of these characteristics underscores the value of the FCC’s forward-thinking decision in 2020 to authorize the full 6 GHz band for unlicensed Wi-Fi use.

Key areas affected by AI

In his prepared remarks, Everson cited a number of areas that are impacted by AI, including infrastructure, technical considerations, cost, and data sovereignty and security.

Infrastructure

AI is driving the shift toward edge computing. Service providers must consider “AI-native” traffic profiles for several reasons, including technical considerations, cost, and data sovereignty and security issues. As network operators move compute toward the network edge—such as at a cell site where a tower sits—they will be able to run applications directly from the network.

Technical

Physical AI use cases such as robotics, autonomous vehicles, and industrial automation could require sub-millisecond decision-making. If an autonomous robot sends data to a central cloud and has to wait for a response, the round-trip latency could be too high for safe, real-time operation. Edge computing can help overcome these challenges by bringing computation closer to the source of data.

Cost

AI operations generate massive amounts of data. For example, high-definition video analytics for public safety can generate terabytes of data daily. Backhauling that data to a central cloud is prohibitively expensive and creates massive network congestion. By processing data at the edge, organizations can significantly reduce bandwidth costs and improve operational efficiency.

Data sovereignty and security

Enterprises and governments are increasingly concerned about data sovereignty and security. Many customers have security or regulatory concerns about moving sensitive information across the public internet to a third-party cloud provider. Edge computing and AI-native networks can help address these concerns by keeping data within defined boundaries while still enabling powerful AI capabilities.

Potential benefits of AI-driven networks

Everson stated that networks will leverage AI for increased performance and resiliency. “While AI workloads present several challenges for network operators seeking to ensure seamless performance, reliability, and security, there is a tremendous opportunity to leverage AI to deliver new applications and better performance, infuse security into the fabric of the network, and manage the increased complexity,” he said. “Agentic AI will change the nature of traffic on the network, but it will also provide network operators new tools to operate at machine speed and deliver greater performance, efficiency, and security.”

AgenticOps also lets the network act as a self-healing system, Everson said. “Cisco’s AI-native tools enable the network to reroute traffic, adjust capacity, or reconfigure network nodes when the system detects performance degradation or an impending hardware failure. This dramatically increases uptime and reliability for mission-critical services,” he added.

One of the most significant challenges for network operators is the talent gap in managing increasingly complex, software-defined networks. Everson said that AgenticOps allows operators to automate repetitive, low-value tasks—such as ticket resolution, configuration updates, and routine maintenance. These tools also help close the workforce talent gap by lowering the barrier to entry and allowing more junior analysts to ramp up quickly. “By automating these tasks, Cisco’s AI-enabled platforms can free network engineers to focus on higher-level architectural strategy and innovation, and free cybersecurity analysts to dedicate more time to strategic threat hunting and detection engineering,” he explained.

In addition to changes in traffic patterns, networks are moving toward AI-native platforms that will become the fabric of intelligent connectivity rather than a simple pipe. Everson highlighted one promising application: Integrated Sensing and Communication (ISAC), which combines wireless communications and radio-frequency sensing to “see” objects’ position and path using radio waves that reflect off them. Unlike optical sensors, it can detect intrusion even in low-light conditions, through smoke, or around obstructions where traditional video analytics might fail. “This technology has been prototyped and demonstrated already, and it holds great promise for autonomous systems and robotics, AI-driven smart facilities, and public safety,” Everson said.

Recommendations for policymakers

In closing, Everson offered three suggestions for the committee to act on in the future.

• Accelerate the U.S. AI-native stack. Cisco is investing across multiple dimensions of AI-native networking, bringing new capabilities to 5G-Advanced today while building the foundation for 6G. One example of this commitment is AI-WIN—a collaboration among Cisco, NVIDIA, MITRE, Orion Development Company, Booz Allen, and T-Mobile—which brings AI, compute, and wireless together to create a secure, American-led path from 5G-Advanced to AI-native 6G. “I encourage Congress to lean in on areas where the United States has a strategic leadership role, such as compute, core networking, and applications,” Everson stated.
• Modernize permitting and infrastructure. As computing becomes more distributed, permitting must enable rapid and responsible deployment. As this Committee considers the future of the Universal Service Fund, it should account for the evolving costs of AI-ready networks so rural and urban communities can share in the benefits, he stated.
• Maintain a balanced spectrum policy. The 800 megahertz of licensed spectrum recently made available by Congress is essential to high-capacity, high-uplink connectivity. The FCC’s authorization of the 6 gigahertz band for unlicensed use is equally important to meeting enterprise demand. “A dependable pipeline of both is foundational to American leadership, and I thank you for your efforts to rebuild it,” Everson concluded.

The hearing underscored a growing bipartisan recognition that AI is no longer just a data-center phenomenon. It is reshaping the entire communications ecosystem from the core to the edge, and policies must evolve to ensure that networks can meet the demands of an AI-driven economy. As federal programs continue to fund broadband deployment, lawmakers are now weighing how to integrate AI-readiness into infrastructure investments, workforce training, and spectrum management.

The rapid adoption of AI also raises questions about network security, reliability, and resilience. With AI workloads increasingly running across distributed environments, the need for secure, high-performance connectivity has never been greater. The testimony from Cisco and other experts provides a roadmap for how network operators and policymakers can work together to build the intelligent infrastructure of the future.

For enterprises, the message is clear: networks must become AI-native. That means investing in edge computing, adopting AI-driven operations tools, and planning for traffic patterns that are fundamentally different from those of the past decade. The shift to AI-native networking is not just a technical evolution; it is a strategic imperative for organizations seeking to harness the full potential of artificial intelligence while ensuring security, performance, and reliability.

Source: Network World News