Artificial Intelligence is making its enterprise pivot from hype and fear of missing out (FOMO) to strategic priority. Models have evolved dramatically, becoming smaller, faster, more specialized, and significantly more capable. As such, tech leaders are ramping up their AI investments— 78% of enterprises expect to expand AI budgets this year—and the pressure on enterprise networks is intensifying.

In the era of distributed, real-time AI, the network is no longer back-office plumbing—it’s a strategic performance enabler. Ambitious AI projects will falter if the underlying network can’t keep up. To move AI from pilot to production at scale, technology leaders are rethinking how their networks handle data flow, security, and resilience.

The AI Workloads Changing the Game

Different AI deployments enable distinct business capabilities and open new doors to enhanced experiences and operations—but they also each place unique demands on the supporting network infrastructure. Key emerging AI workloads include:

• Real-time analytics & decisioning: AI-powered analytics systems (e.g. fraud detection in finance or supply chain optimization in manufacturing) ingest streams of data and return insights instantly. These use cases demand fast, low-latency data delivery so decisions can be made in split seconds.
  

• Edge AI inference: In retail and healthcare, for example, AI models run on edge devices to analyze local data—think cameras in stores for inventory or patient monitors in hospitals. This pushes significant processing to the network edge, requiring reliable, high-throughput links to central clouds for aggregated learning and coordination.
  

• Generative AI at the edge: New services like virtual assistants, interactive kiosks, or customer service bots in physical locations use generative AI models. To feel responsive and human, these applications need fast, local processing or ultra-reliable connectivity to cloud AI platforms.
  

• Autonomous “agentic” AI: Looking ahead, autonomous AI agents and processes will act on their own to complete tasks, with Gartner predicting that 15% of all day-to-day work decisions will be made by agentic AI by 2028, and 33% of enterprise software applications will include the technology. 

Each of these scenarios hinges on moving data quickly, reliably, and securely across disparate environments. The network is the invisible thread that ties together sensors, AI models, and users. If that thread frays, even the most powerful AI algorithms cannot deliver results.

What AI Really Needs from the Network

To support these next-generation workloads, networks must deliver a mix of capabilities well beyond basic connectivity. Key requirements include:

• Low Latency & High Throughput: Advanced AI applications cannot tolerate lag or bandwidth bottlenecks. Models crunching data in real time need data to arrive without delay or loss. In fact, networking experts predict AI will drive a dramatic jump in data traffic, with direct generative AI data traffic reaching exabyte scale by 2033.
  

• Resilience & Consistency: AI can’t help the business if the network is down or unreliable. Self-healing capabilities, intelligent routing around faults, and built-in redundancy are must-haves to keep AI services running 24/7. Networks should be engineered to “fail over” gracefully and maintain reliable performance, so that an algorithm’s output is available when it’s needed.
  

• Cross-environment data integration: Today’s AI often lives in a multi-cloud, hybrid, and edge-based ecosystem. Data might be generated on an IoT device in the field, processed by an AI model in a public cloud, and then stored in a private data center. Networking across these environments needs to be seamless and secure. The network must integrate disparate environments, moving data across cloud and on-premises boundaries without friction. 

Aligning Architecture with AI Deployment Goals

How can organizations evolve their network architecture to meet these demands? Several strategic shifts are underway to align networks with AI initiatives:

• Distributed compute awareness: AI workflows increasingly span from cloud to edge, so networks must be designed with decentralization in mind. That means networks will be connecting a far-flung web of AI components—edge devices, branch servers, cloud platforms, and more. Software-defined wide area networking (SD-WAN) can help meet this need by dynamically routing traffic based on real-time conditions and policies. Network resources can be allocated on-demand to wherever the AI workload is running, ensuring data gets where it’s needed efficiently.
  

• AI-aware traffic optimization: Organizations are implementing AI-aware network controls that recognize traffic patterns and optimize accordingly. This might mean automatically prioritizing an important machine-learning data feed over routine email traffic, or caching frequently used AI datasets at network edges to reduce repetitive data transfers. AI can be applied within the network itself (called AIOps for networking) to forecast congestion and re-route traffic proactively.
  

• Full-stack observability: When AI applications span many layers, pinpointing performance issues can be daunting. Full-stack observability addresses this by giving IT teams end-to-end visibility into application performance and the underlying infrastructure, including networks. With unified monitoring and analytics, an enterprise can trace, for instance, why a predictive analytics dashboard is lagging. This level of insight is vital for AI, where delays or errors can be subtle and compounding.
  

Securing AI at the Network Layer

AI deployments amplify the importance of robust network security. These systems often handle sensitive data and help power critical decisions, making them attractive targets to would-be attackers. Key focus areas include:

• Zero trust principles: Adopting a zero trust architecture means that no user or device is implicitly trusted—every connection is authenticated and authorized. This model is essential in AI environments where machines (like IoT sensors or AI microservices) are talking to other machines.
  

• Network segmentation: Even with zero trust, assuming breaches can happen is prudent. Strong segmentation of the network can contain threats if they arise. Practically, this means isolating AI workloads and data pipelines in their own network segments, separate from the broader network. This is especially important for AI, where a corrupted model or data source could otherwise infect downstream processes.
  

• AI-powered threat detection: Just as AI is used to optimize network performance, it’s also quickly changing the game for network security monitoring. Incorporating machine learning-based can help identify anomalies in traffic that signify breaches or data exfiltration in progress. Going forward, instead of simply blocking, organizations will employ smarter analytics to differentiate legitimate AI usage from malicious activity.

As AI Is a Strategic Priority, the Network Should Be Too

In a business landscape defined by real-time insights and AI-driven experiences, the network becomes a competitive differentiator. Whether it’s faster time-to-insight or richer AI-enabled customer interactions, the right network infrastructure unlocks the full value of AI for the enterprise.

Comcast Business is the #1 provider of Managed SD-WAN for enterprises, with the largest market share and more sites added in 2024 than any other provider ranked on Vertical Systems Group’s U.S. Carrier Managed Leader Board, and has been as the fastest-growing Managed SD-WAN provider for the sixth year in a row. Learn how we are helping enterprise leaders build a technology foundation for their transformation journeys.