In today’s digital-first world, network complexity is growing at an unprecedented pace. With the rise of cloud adoption, the shift toward microservices and containers, and the explosion of consumer-driven mobile transactions, traditional approaches to managing and optimizing network performance simply aren’t cutting it anymore. 

What once worked—basic monitoring and reactive issue resolution—is now falling short in a landscape where every second of downtime or latency can affect user experience and revenue. 

The Network Is Evolving. Are You Keeping Up? 

Recent insights from EMA Research reveal how significant this transformation has become:

•  20% of IT workloads are now hosted in the public cloud. 

• Cloud services are growing at an average annual rate of 10%. 

• Flexibility and agility are the top drivers of this shift.  

While these changes bring clear business benefits, they also introduce greater complexity. In particular, they make it harder to trace performance issues back to their root causes.

One of the most telling stats? Over 35% of performance-related issues are flagged by customers—not internal systems. And while most organizations resolve issues within six hours, 20% take over 11 hours to fix problems. That’s a lot of downtime in the modern era.

The Hidden Cost of Change: Performance Blind Spots

Here’s what EMA uncovered when digging deeper into the issue:

• 60% of application performance issues are linked to infrastructure changes.

• Teams are more focused on closing tickets than understanding the real issue.

• There’s often no clear correlation between individual network components and application performance.

This lack of visibility—especially when change is involved—is a growing pain for many
IT teams. If you can’t connect the dots between changes and performance outcomes,
how can you optimize?

The Way Forward: AI-Driven Network Observability

The answer lies in evolving your approach to Network Performance Management
(NPM). Specifically, it’s time to embrace AI-driven observability—a smarter, more
proactive way to manage your network.

Here’s how:

Behavioral Analytics 
Deploy machine learning that continuously learns your network’s “normal” state and
spots deviations. This enables real-time anomaly detection far beyond the capabilities of manual monitoring. 

Intelligent Alerts 
Rather than bombarding teams with alerts for every spike, modern tools focus on
contextual deviations—giving you the insights that matter most, right when they matter.

 
Automated Diagnostics 
Use automated workflows to identify likely root causes of performance issues. This
slashes mean time to resolution (MTTR), improves Quality of Service (QoS), and
enhances the overall customer experience (QoE). 

Accelerated Application Delivery 
Faster diagnostics and proactive insights help teams deploy and manage applications more efficiently—keeping pace with the speed of digital transformation. 

Rethinking the Role of Your Network 
The network is no longer just an infrastructure to support applications. It’s a strategic
enabler of business performance and transformation. 

When you make change awareness a core part of your NPM strategy, you stop
reacting—and start optimizing. Your network begins to work for you, not just with you.

References

• EMA Research: “AIOps and IT Analytics at the Crossroads” 
• EMA Research: “Optimizing IT for Financial Performance”