How AIOps is changing network management.

For decades, enterprise network support operated with a break-fix model that relied on reactive fault reporting and manual workflows to fix faults and maintain uptime and application availability.

By Amit Chhabra, Technical Consultant: Network Services at Connect

Only after a user noticed an application lagging and submitted a support ticket, would an IT technician begin the often slow process of investigating the root cause, finding a fix, and reporting back.

While this manual process was tolerable in the era of centralised, on-site campus network architectures, the transition to cloud-first operations and hybrid working models has made it an operational liability.

The decentralised office model has dissolved the traditional network edge. Instead of managing a single, controlled corporate campus, IT teams are now responsible for a broader user base across multiple branch offices and work-from-home (WFH) setups.

This distributed workforce also connects to the enterprise network with differing line speeds and variable quality, which can further degrade performance.

And many IT teams need to manage this wider estate and greater complexity without additional resources.

As such, when every support ticket is raised after the fault has already occurred, IT teams and service providers remain on the back foot, reacting to issues rather than addressing them before anyone notices.

Detect, decide, do.

To understand why conventional monitoring tools fail modern mid-market IT teams, enterprise leaders must look beyond basic dashboards and apply a clear litmus test: Does your management layer merely present data, or does it detect, decide, and do?

Traditional tools raise alerts, leaving technical teams to correlate logs and manually detect root casues. A true AI Operations (AIOps) architecture uses intelligence to eliminate the investigation phase.

Built on HPE Juniper’s foundational engine, a decade of operational data, and rich telemetry, Connect’s AIOps solution systematically identifies the problem, pinpoints its location across the environment, and determines the precise remediation required, resolving issues automatically in the background or providing guided recommendations where human oversight is preferred.

Turning telemetry into business value.

While legacy networks have always generated telemetry, without real-time machine learning (ML), vast quantities of valuable data sit unexamined in log servers.

AIOps replaces manual log analysis by evaluating network performance at scale, applying ML to distinguish true root causes from transient network noise.

In this model, an AIOps solution doesn’t replace IT engineers. After carrying out an investigation, the solution implements a fix where possible, or escalates more complex issues to an engineer to resolve, allowing these valuable resources to focus on higher-order tasks and work more efficiently.

The ability to detect faults and implement fixes quickly, often before anyone logs on for the day, has become critical in an era when user experience (UX) has emerged as the primary metric of network quality.

Real-time traffic, such as voice streams feeding cloud contact centres, like Genesys Cloud or Five9, is inherently unforgiving. While web traffic can absorb minor latency, voice and video degrade audibly into choppy or abandoned calls.

To address this issue, Service Level Expectations (SLEs) have replaced basic uptime Service Level Agreements (SLAs), measuring the true end-to-end performance delivered to every single endpoint.

Gaining complete visibility.

Using HPE Juniper Networks’ Marvis Minis – self-configuring, always-on digital experience software – Connect continuously probes the environment.

By synthetically simulating real user workflows, such as server authentications and SaaS application tests 24/7, Marvis Minis identify and resolve developing issues off-peak before employees even log on.

Coupled with dynamic packet capture at the exact moment of failure, the platform isolates conditions early, preventing minor glitches from escalating into sitewide outages.

Many network tools promise AI intelligence but only operate within a localised overlay, leaving critical internet and wide area network (WAN) paths completely blind.

Connect’s difference lies in delivering these intelligent services over our own global core network rather than the public internet. By pairing client-to-cloud visibility with a controlled, highly peered core underlay, Connect delivers predictable latency and jitter.

Should an issue arise outside the network footprint, IT teams still receive immediate, unambiguous clarity on where the fault lies, eliminating hours of cross-vendor analysis.

Furthermore, for enterprises modernising their voice architecture, our flexible "Bring Your Own Voice" framework allows organisations to repoint existing services directly into cloud contact centres, bypassing lengthy porting delays while securing end-to-end quality.

Elevating IT operations.

Shifting network operations from reactive ticket handling to predictive experience management delivers tangible commercial return on investment (ROI) by cutting network-related trouble tickets by up to 90%, freeing IT capacity for strategic growth initiatives, with continuous synthetic testing protecting productivity by fixing issues before they impact end-user workflows or customer experience metrics.

Shifting from reactive to predictive network management also eliminates time-consuming investigation phases and reduces costly business downtime, with the ability to implement granular guardrails that allow IT leaders to automate routine fixes safely while retaining full administrative control and auditability.

In a cloud-first ecosystem, waiting for a user to report an outage is no longer a viable operating model. By deploying an AIOps-managed network backed by Connect’s global infrastructure, mid-market IT teams gain the clarity, automation, and stability required to run a modern enterprise more effectively and efficiently.