The Perimeter Is Gone. The Stakes Are Higher Than Ever.
Historically, IT owned and controlled every layer of the digital stack. Those days are over.
Today, the cloud is the new data center, SaaS and APIs are the new application stack, anywhere is the new office, and the Internet is the new network. Agility and speed are up, but so is risk. With 81% of outages in 2022 caused by third-party providers, the reality is stark: You are now responsible for experiences that depend on systems you do not own.1
Now add AI to this already complex equation. No longer confined to labs, AI workloads are embedded in the very tools that run the business—productivity suites, collaboration platforms, CRM systems, and even customer-facing apps. Unlike traditional applications, generative and agentic AI introduce unpredictable traffic patterns, long chains of API calls, and extreme sensitivity to latency. These workloads strain networks designed for another era, often causing user experience problems that don't register on traditional monitoring dashboards.
The result? Traditional monitoring cannot keep up. Enterprises need AI-powered assurance, a new operational model that validates actions, helps predict issues before they escalate, and keeps digital experiences reliable at machine speed.
Real-world Scenarios Where Assurance Makes the Difference
The Campus Paradox: When Green Dashboards Don't Match User Reality
On the modern campus, performance issues rarely present as simple outages. Instead, they hide between layers—Wi-Fi signal strength, DNS lookup time, ISP routing, or SaaS responsiveness. Add AI-enabled apps like Copilot or Salesforce, and the complexity multiplies.
AI-powered assurance cuts through this noise, correlating wireless, switching, DNS, and SaaS performance into one view. The result: clarity, improved resolution, and satisfied end users, regardless of what your green dashboards are telling you.
Branch Office Bottlenecks: When AI Meets WAN Reality
Branch offices are often the weak link in delivering digital experiences. Here’s why: AI apps demand low jitter, rapid data fetches, and clean API chaining, but WAN performance does not always deliver.
With AI-powered assurance, network teams can monitor ISP health in real time, validate SD-WAN and cloud application performance, and identify degradations before they impact branch users. The shift is profound; instead of reacting to complaints, teams can proactively protect productivity where it matters most.
Operations Overload: Too Many Tools, Not Enough Answers
Network operations teams are drowning in dashboards. The problem is not data, it is insight.
AI-powered assurance integrates directly into workflows, automatically correlating alerts to root cause, surfacing guided troubleshooting steps, and generating visual evidence for faster escalation. The benefits compound: It not only helps reduce MTTR, it also accelerates mean time to innocence, showing when issues lie outside your domain. In a world of AI-driven complexity, this workflow simplicity doesn’t just save time, it significantly reduces operational burden.
From AI-ready to AI-resilient
Old approaches—reactive troubleshooting, siloed monitoring, and static thresholds—struggle to meet AI demands. To deliver optimized digital experiences in the AI era, enterprises need three pillars of assurance:
- End-to-end visibility across owned and unowned infrastructure
- AI-driven insights that cut through the noise
- Closed-loop operations that help keep automation safe and adaptive
This isn't optional. It’s the foundation for building AI-resilient enterprises that thrive in the future, rather than just surviving it.
Ready To See How?
Our new ebook, Delivering Assurance at the Speed of AI, explores how AI-enabled applications are reshaping network demands, and how you can harness AI-powered assurance to cut through noise, boost resilience, and help deliver digital experiences users can trust.
1. “Annual Outages Analysis 2023: The Causes and Impacts of IT and Data Center Outages.” Uptime Intelligence, UII Keynote Report 92M, March 2023.