Enterprise-Grade AIOps Framework for Telecom Networks

Telecom and enterprise network operations generate enormous volumes of alarms, logs, metrics and tickets. Most teams still work reactively: an event happens, a ticket is raised, an engineer investigates. This dissertation asks what a credible, enterprise-grade path to AI-enabled operations looks like — one that a large operator could actually govern and adopt.

Operations teams face alert fatigue, slow root-cause analysis and fragmented tooling. AIOps products promise automation, but organisations struggle to connect data sources, trust model outputs, integrate with ITSM processes and stay compliant. There is little practical guidance that combines architecture with operating model.

• Define a reference architecture for enterprise-grade AIOps in telecom networks
• Describe how it integrates with existing ITSM and operational processes
• Propose a phased adoption model with governance and controls
• Identify the organisational capabilities needed to sustain it

A prescriptive framework combining a seven-layer architecture with an operating model and a maturity roadmap. The emphasis is on sequencing: build data and observability foundations first, introduce correlation and noise reduction next, then move toward prediction and closed-loop automation with explicit human oversight.

The reference architecture has seven layers:

1. Data ingestion — telemetry, logs, events, topology and ticket data
2. Observability — normalisation, correlation-ready storage, service mapping
3. AI engine — anomaly detection, event correlation, prediction
4. Automation — runbooks and closed-loop remediation with approval gates
5. ITSM integration — incidents, problems and changes linked to AI insights
6. Security & compliance — access control, auditability, model governance
7. Executive intelligence — service health, risk and value reporting

Signals flow from network and IT systems into the ingestion layer, are enriched with topology and service context, analysed by the AI engine, and surfaced as correlated incidents in ITSM. Approved automations execute through the automation layer, with every action logged for audit and fed back to improve the models.

This is an academic framework. No commercial deployment is claimed. The work draws on published research, vendor-neutral architecture patterns and my experience running network and infrastructure programmes.

Provides a structured way for operations leaders to plan AIOps adoption as a business transformation rather than a tool purchase. Placeholder: add any academic feedback or recognition here.

Balancing ambition (autonomous operations) with the realities of data quality, legacy tooling, change control and regulatory expectations in telecom.

AIOps succeeds or fails on data foundations and operating model far more than on algorithms. Trust has to be earned in stages.

Turn the framework into a self-assessment tool and a set of practitioner articles; explore a small proof-of-concept on open-source observability data.