What AIOps Actually Changes in IT and Network Operations

For my MBA dissertation at IIM Lucknow I designed a reference framework for enterprise-grade AIOps in telecom networks. The most useful conclusion wasn't about algorithms. It was that AIOps changes how operations work — and organisations that treat it as a tool purchase usually stall.

The problem AIOps is meant to solve

A large network can generate thousands of events an hour. Most are noise, many are duplicates, and the few that matter are buried. Engineers spend their time triaging rather than fixing, and root-cause analysis depends on whoever happens to know the topology best.

AIOps applies analytics and machine learning to that data to do three things:

1. Reduce noise — deduplicate and suppress events that don't need a human
2. Correlate — group related events into one incident with a probable cause
3. Predict and act — spot patterns before they become outages, and trigger approved automations

Seven layers, not one product

The framework I proposed has seven layers:

┌──────────────────────────────────────────┐
│ 7. Executive intelligence                │  service health, risk, value
├──────────────────────────────────────────┤
│ 6. Security & compliance                 │  access, audit, model governance
├──────────────────────────────────────────┤
│ 5. ITSM integration                      │  incidents, problems, changes
├──────────────────────────────────────────┤
│ 4. Automation                            │  runbooks, closed loop + approvals
├──────────────────────────────────────────┤
│ 3. AI engine                             │  anomaly, correlation, prediction
├──────────────────────────────────────────┤
│ 2. Observability                         │  normalise, enrich, topology
├──────────────────────────────────────────┤
│ 1. Data ingestion                        │  metrics, logs, events, tickets
└──────────────────────────────────────────┘

The layers matter because most failures happen at the bottom. If topology data is out of date, or events from different platforms can't be normalised, the AI engine has nothing reliable to work with.

A tiny example of where value starts

Before any machine learning, simple correlation already removes a surprising amount of noise. A toy version in Python:

from collections import defaultdict
from datetime import timedelta

def group_events(events, window=timedelta(minutes=5)):
    """Group events from the same site within a time window into one candidate incident."""
    incidents = defaultdict(list)
    for e in sorted(events, key=lambda e: e["time"]):
        key = e["site"]
        bucket = incidents[key]
        if bucket and e["time"] - bucket[-1][-1]["time"] <= window:
            bucket[-1].append(e)
        else:
            bucket.append([e])
    return [group for groups in incidents.values() for group in groups]

Real platforms use topology, service maps and learned patterns rather than a fixed window — but the principle is the same: fewer, better incidents.

What actually changes

• Roles shift from triage to engineering. Teams spend more time improving runbooks and data quality.
• Process changes — incident, problem and change management need to accept machine-generated insights and automated actions, with clear approval rules.
• Trust is earned in stages. Start with recommendations, then supervised automation, then closed-loop automation for well-understood, low-risk scenarios.
• Governance expands to cover models: who approves them, how they're monitored, how decisions are audited.

Where to start

If you're considering AIOps, start with three questions:

1. Can we trust our event, topology and CMDB data today?
2. Which two or three high-volume, well-understood problems would we automate first?
3. Who will own the operating-model change — not just the tool?

AIOps is one of the most practical applications of AI in the enterprise. But the order matters: data foundations, then correlation, then prediction, then autonomy.

Note: this article summarises an academic framework; no commercial deployment is claimed.