Is Your Network Ready for the AI Boom? 5 Overlooked Stress Points

Prakash Mana
Cloudbrink
Learn more about Cloudbrink

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization.

But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect?

The focus so far has been on AI itself:

Which tools are we rolling out?

What use cases make the most sense?

How do we stay compliant and competitive?

These are important questions. But they often overlook the layer that determines whether AI feels seamless or frustrating: the delivery infrastructure.

Even the most advanced AI models won't create value if users experience them as laggy, glitchy, or unreliable. That gap between capability and experience is often a result of hidden performance stress points.

Here are five of the most commonly overlooked.

1. Latency-Sensitive Workflows Are Now Everywhere

AI is no longer confined to backend systems. It's becoming part of the moment-to-moment workflow across departments:

  • Sales reps rely on real-time call insights.
  • Engineers use AI code suggestions as they type.
  • Customer service teams leverage smart summaries between chats.

These are latency-sensitive interactions. A delay of even half a second can interrupt flow, create doubt, or lead users to skip the tool altogether.

Yet many enterprises still route traffic through centralized VPNs or legacy access infrastructure, introducing latency at the exact moment responsiveness matters most.

The shift to real-time AI interaction demands a corresponding shift in how we think about performance. Not just speed, but consistency under pressure.

2. Packet Loss Disrupts More Than Connectivity

In traditional office networks accessing local resources, mild packet loss may go unnoticed. But with AI in the loop using SaaS and GPUaaS resources, especially for live transcription, computer vision, or voice processing, packet loss directly affects the output.

Poor quality audio on a video call doesn't just inconvenience the listener, it degrades the accuracy of any AI assistant trying to capture notes or summarize outcomes.

When inference requests are repeatedly delayed, corrupted, or incomplete, it creates a feedback loop of degraded performance that users (rightly) blame on the tool.

In reality, it's the delivery path, not the AI engine, that's failing.

3. The Last Mile Is Now Mission-Critical

Pre-pandemic, the last mile — how data gets from your environment to the end user — was often treated as a best-effort concern. Today, it's a critical variable in application success.

As AI moves closer to the user powering in-app suggestions, decision support, and real-time collaboration, variability in home networks, ISP performance, or even Wi-Fi strength becomes a key limiter.

The user experience is shaped by the weakest link, and in remote or hybrid environments, that's often the last mile. When that link fails, the AI tool appears to "not work," even if the backend is performing flawlessly.

What's more concerning is that these issues often don't trigger traditional alerts. But they silently affect usage, engagement, and ROI.

4. AI Traffic Patterns Don't Follow the Old Rules

One of the more subtle shifts AI introduces is in how and when data flows through the network.
Unlike predictable, transactional workflows, AI activity tends to be:

  • Spiky – sudden bursts of demand from large language models
  • Context-heavy – requiring multiple data sources to converge in real time
  • Edge-driven – activated from a variety of user devices and locations

Legacy capacity planning models based on average load or scheduled usage fail to capture the dynamic, real-time nature of AI access.

And when network teams can't anticipate these patterns, they can't design for them. That's when performance dips, buffering starts, and adoption stalls.

5. Access Security Layers Can Create Bottlenecks

There's no question that Zero Trust and layered security must be foundational in the AI era. But not all access solutions are built with AI workloads in mind.

When every request from an AI assistant is routed through multiple hops like VPN concentrators, ping ponged through ZTNA PoPs, inspection firewalls, or authentication gateways, latency builds. In the process, the responsiveness that defines great AI is lost.

This is especially true when the AI tool needs to pull in context across apps — emails, documents, calendars, CRM data. If that access is fragmented or slow, the assistant feels broken, even if it's secure.

Security shouldn't come at the cost of usability. But in many environments, it still does.

What IT Leaders Should Be Doing Now

The organizations that will succeed with AI aren't just those that build smart models or write good prompts. They're the ones that deliver AI to their people in a way that feels effortless, intuitive, and immediate.

Here's where to start:

  • Run a performance readiness check: Treat AI rollouts like any other critical system launch. Map dependencies, latency paths, and weak points.
  • Move beyond uptime as the main metric: AI interaction demands a shift from uptime to real-time. Are your tools responding fast enough to feel natural?
  • Reevaluate access architecture: Look for bottlenecks in legacy VPNs, ZTNA, proxies, or legacy routing setups that could delay cloud-native AI access.
  • Bring network and application teams together: AI success isn't just an app or infrastructure issue, it's a systems issue. Solve it collaboratively.

Conclusion

Enterprise AI isn't just a technology initiative, it's a performance challenge. The next phase of digital work won't be defined by who has the smartest tools, but by who can deliver them at the speed users expect with no friction, no lag, and no excuses.

That's why infrastructure decisions today carry outsized weight. Because delivering a powerful AI experience isn't about pushing more to the cloud. It's about bringing performance to the edge where work happens.

Cloudbrink is helping organizations solve the delivery layer ensuring secure, ultra-low-latency access for the future of intelligent work.

Prakash Mana is CEO of Cloudbrink

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Is Your Network Ready for the AI Boom? 5 Overlooked Stress Points

Prakash Mana
Cloudbrink
Learn more about Cloudbrink

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization.

But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect?

The focus so far has been on AI itself:

Which tools are we rolling out?

What use cases make the most sense?

How do we stay compliant and competitive?

These are important questions. But they often overlook the layer that determines whether AI feels seamless or frustrating: the delivery infrastructure.

Even the most advanced AI models won't create value if users experience them as laggy, glitchy, or unreliable. That gap between capability and experience is often a result of hidden performance stress points.

Here are five of the most commonly overlooked.

1. Latency-Sensitive Workflows Are Now Everywhere

AI is no longer confined to backend systems. It's becoming part of the moment-to-moment workflow across departments:

  • Sales reps rely on real-time call insights.
  • Engineers use AI code suggestions as they type.
  • Customer service teams leverage smart summaries between chats.

These are latency-sensitive interactions. A delay of even half a second can interrupt flow, create doubt, or lead users to skip the tool altogether.

Yet many enterprises still route traffic through centralized VPNs or legacy access infrastructure, introducing latency at the exact moment responsiveness matters most.

The shift to real-time AI interaction demands a corresponding shift in how we think about performance. Not just speed, but consistency under pressure.

2. Packet Loss Disrupts More Than Connectivity

In traditional office networks accessing local resources, mild packet loss may go unnoticed. But with AI in the loop using SaaS and GPUaaS resources, especially for live transcription, computer vision, or voice processing, packet loss directly affects the output.

Poor quality audio on a video call doesn't just inconvenience the listener, it degrades the accuracy of any AI assistant trying to capture notes or summarize outcomes.

When inference requests are repeatedly delayed, corrupted, or incomplete, it creates a feedback loop of degraded performance that users (rightly) blame on the tool.

In reality, it's the delivery path, not the AI engine, that's failing.

3. The Last Mile Is Now Mission-Critical

Pre-pandemic, the last mile — how data gets from your environment to the end user — was often treated as a best-effort concern. Today, it's a critical variable in application success.

As AI moves closer to the user powering in-app suggestions, decision support, and real-time collaboration, variability in home networks, ISP performance, or even Wi-Fi strength becomes a key limiter.

The user experience is shaped by the weakest link, and in remote or hybrid environments, that's often the last mile. When that link fails, the AI tool appears to "not work," even if the backend is performing flawlessly.

What's more concerning is that these issues often don't trigger traditional alerts. But they silently affect usage, engagement, and ROI.

4. AI Traffic Patterns Don't Follow the Old Rules

One of the more subtle shifts AI introduces is in how and when data flows through the network.
Unlike predictable, transactional workflows, AI activity tends to be:

  • Spiky – sudden bursts of demand from large language models
  • Context-heavy – requiring multiple data sources to converge in real time
  • Edge-driven – activated from a variety of user devices and locations

Legacy capacity planning models based on average load or scheduled usage fail to capture the dynamic, real-time nature of AI access.

And when network teams can't anticipate these patterns, they can't design for them. That's when performance dips, buffering starts, and adoption stalls.

5. Access Security Layers Can Create Bottlenecks

There's no question that Zero Trust and layered security must be foundational in the AI era. But not all access solutions are built with AI workloads in mind.

When every request from an AI assistant is routed through multiple hops like VPN concentrators, ping ponged through ZTNA PoPs, inspection firewalls, or authentication gateways, latency builds. In the process, the responsiveness that defines great AI is lost.

This is especially true when the AI tool needs to pull in context across apps — emails, documents, calendars, CRM data. If that access is fragmented or slow, the assistant feels broken, even if it's secure.

Security shouldn't come at the cost of usability. But in many environments, it still does.

What IT Leaders Should Be Doing Now

The organizations that will succeed with AI aren't just those that build smart models or write good prompts. They're the ones that deliver AI to their people in a way that feels effortless, intuitive, and immediate.

Here's where to start:

  • Run a performance readiness check: Treat AI rollouts like any other critical system launch. Map dependencies, latency paths, and weak points.
  • Move beyond uptime as the main metric: AI interaction demands a shift from uptime to real-time. Are your tools responding fast enough to feel natural?
  • Reevaluate access architecture: Look for bottlenecks in legacy VPNs, ZTNA, proxies, or legacy routing setups that could delay cloud-native AI access.
  • Bring network and application teams together: AI success isn't just an app or infrastructure issue, it's a systems issue. Solve it collaboratively.

Conclusion

Enterprise AI isn't just a technology initiative, it's a performance challenge. The next phase of digital work won't be defined by who has the smartest tools, but by who can deliver them at the speed users expect with no friction, no lag, and no excuses.

That's why infrastructure decisions today carry outsized weight. Because delivering a powerful AI experience isn't about pushing more to the cloud. It's about bringing performance to the edge where work happens.

Cloudbrink is helping organizations solve the delivery layer ensuring secure, ultra-low-latency access for the future of intelligent work.

Prakash Mana is CEO of Cloudbrink

Related Links

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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