How can AIOps Empower the Network Operations Center?

An unprecedented rise in operational complexity has brought the telecom industry to a crossroads as networks expand  across optical, IP/MPLS, SD-WAN, and 5G domains. Alarms pour in by the millions. Trouble tickets sprawl across fragmented systems. Knowledge is siloed, inconsistent, and often outdated.

At the same time, service providers face mounting business pressures:

• Escalating operational costs due to manual troubleshooting
• Talent shortages as senior engineers retire faster than replacements can be trained
• Customer demands for zero downtime in an always-on digital economy

In this high-stakes environment, the old ways of running a Network Operations Center (NOC)—manual searches, static playbooks, tribal knowledge—are no longer sustainable. Service providers need an AIOps solution that will reduce Mean Time to Repair (MTTR) and evolve toward autonomous network operations.

Operational Pain Points for Network Operations Centers

Based on conversations with operators, architects, and CIOs, we see four key challenges emerging:

• Data fragmentation
• Inconsistent accuracy
• Tribal knowledge
• Multivendor, multidomain complexity

Data Fragmentation

Incident and network knowledge lives everywhere: ServiceNow, SharePoint, Confluence, chat transcripts, vendor portals, and PDFs. Engineers waste valuable time searching across silos.

Inconsistent Accuracy

Many incident records are incomplete or lack hindsight validation. False positives create costly rework—engineers execute incorrect steps, only to escalate problems and delay resolution.

Tribal Knowledge

The expertise and background understanding needed to resolve problems is often stored in the minds of senior engineers. Thus, training new hires takes years, and as these senior experts age out of the workforce, the resultant knowledge drain threatens operational and network resilience.

Multivendor, Multidomain Complexity

Every vendor uses different alarm codes and log structures. Outages ripple across layers and domains: an optical fault can trigger IP/MPLS congestion, SD-WAN reroutes, and mobile service degradation.

Together, these challenges slow down incident triage, inflate  opex, and jeopardize customer SLAs.

Introducing Virtuora® AX Network Coach: GenAI for the NOC

Virtuora AX Network Coach tackles all four pain points head-on by combining curated historical intelligence with fresh, real-time insights in a unified GenAI engine.

These features are at the heart of Network Coach:

• 10+ years of curated knowledge articles from the 1Finity NOC, representing SME-reviewed, hindsight-validated “gold datasets.”
• Retrieval-Augmented Generation (RAG) pipeline that indexes fragmented sources and returns precise, context-aware answers.
• Confidence scoring + citations inform engineers why a recommendation was made.
• Multivendor, multidomain coverage across optical, IP/MPLS, SD-WAN, and mobility.
• Compliance-aware design, with sensitive IP/MAC/PII data masked before training.

With Network Coach, operators no longer depend on tribal knowledge or static portals. Instead, they query a trusted AI copilot that responds instantly with actionable Root-Cause Analysis (RCA) and recommendations.

Real-World Scenario: How AIOps Resolves a Multilayer Fiber Cut

Consider a frontline L1 operator monitoring a tier-1 backbone when a fiber cut occurs in the transport layer.

Before Network Coach

1. The operator sees a cascade of alarms—loss of light in optical, RSVP tunnel flaps in MPLS, and service degradation in mobile backhaul.
2. They manually search across multiple runbooks, trying to correlate symptoms across layers.
3. Data is inconsistent, sometimes outdated, and sometimes irrelevant.
4. The operator escalates to L2/L3 teams, losing hours or even days while traffic congestion spreads.
5. SLA penalties mount as enterprise and mobile subscribers experience service disruption.

After Network Coach

1. The operator queries the AI: “How should I respond to this multilayer alarm pattern (optical loss + RSVP tunnel flap + MPLS reroute)?”
2. Within seconds, the AI responds:
3. Within seconds, the AI responds:
   • “This matches RCA #5678 (Fiber cut in Region A, Transport Layer). Confidence: 93%.”
   • “Resolution: Initiate reroute via alternate optical path B; validate MPLS tunnel re-establishment; confirm mobile backhaul service continuity.”
4. The operator takes immediate corrective action, restoring service continuity before end users notice.

The Operational Impact

• MTTR shrinks from days to minutes.
• Outage ripple effects across layers are contained.
• Millions in potential SLA penalties are avoided.
• Operator confidence is reinforced by citations to past fiber cut incidents and multidomain RCA playbooks.

The Business Impact: Metrics That Matter

Service providers deploying Network Coach realize both operational and financial benefits:

• 80–90% faster time-to-value in troubleshooting workflows.
• MTTR reduction from weeks to minutes, dramatically improving SLA adherence.
• Downtime avoidance in high-stakes scenarios, protecting millions in potential revenue.
• 90%+ accuracy in RCA recommendations, backed by gold datasets.
• Operational cost savings of 20–30%, as fewer cases escalate to higher tiers.
• Scalable knowledge transfer, empowering even junior staff to resolve incidents like seasoned veterans.

How It Works: The Network Coach RAG Pipeline

Historical Memory:

• 10 years of SME-curated knowledge articles, RCAs, and post-incident reviews.
• Multivendor, multidomain coverage ensures relevance across all CSP environments.

Bring Your Own Data (BYOD):

• Customers can index and train their own incident tickets, KBs, and logs into Network Coach.
• This extends the power of the AI by combining 1Finity’s curated gold datasets with the customer’s proprietary data.

Real-Time Data:

• Streaming telemetry, device logs, updated vendor docs, and recent tickets ingested via APIs.
• Keeps the model fresh and context-aware.

RAG Engine:

• Queries are chunked, embedded, and matched against historical + customer + real-time datasets.
• Most relevant chunks are retrieved, synthesized, and returned.

Confidence + Citation Layer:

• Recommendations include confidence scores and links to past incidents or documents.
• Builds operator trust by bridging AI suggestions with human-reviewed sources.

From Copilot to Autonomous Agent

We see adoption of Network Coach evolving in three phases:

• Phase 1: Conversational Copilot
  Engineers ask questions via chat-style Q&A. Faster than keyword search, smarter than static portals.
• Phase 2: Workflow Integration
  Embedded into ServiceNow or Jira panels, Network Coach pre-resolves tickets or enriches them with context before escalation.
• Phase 3: Autonomous AIOps Agent
  Higher-level orchestration systems query Network Coach in real time, automating root cause identification and remediation.

This progression mirrors how CSPs are gradually adopting agentic AI frameworks, moving from copilots to trusted autonomous teammates.

Why Network Coach is an AIOps Standout

While hyperscale platforms offer generic copilots (upload docs, ask questions), Network Coach is fundamentally different:

• Built for telecom: Not a general-purpose model, but one trained on real incidents.
• Powered by gold datasets: Human-reviewed RCAs, not incomplete ticket notes.
• Extendable by customers: Operators can index and train their own ServiceNow incidents, telemetry logs, and KBs alongside 1Finity datasets—creating a hybrid knowledge engine tailored to their environment.
• Multivendor, multidomain coverage: Supports multiple vendor products.
• Designed for compliance: Built-in masking of IPs, MACs, and sensitive network details.

This unique foundation ensures CSPs get trusted, actionable answers—not generic textbook responses.

The Future of AIOps

The industry faces a looming challenge: a generation of expert operators is retiring, and networks are only becoming more complex. We don’t have decades to train replacements.

Network Coach offers a solution:

• Capture and scale institutional knowledge.
• Augment new hires with instant expertise.
• Allow customers to embed their own data, making the system more powerful and contextually accurate.
• Move toward autonomous operations where AI agents proactively resolve issues before customers even notice.

This is the promise of GenAI in AIOps—not just faster search, but a fundamental transformation in how networks are operated, planned, and assured.