How AIOps can streamline troubleshooting and incident management in modern IT environments

Tobias Johansson
Jul 1
4 min read

Troubleshooting in modern IT environments is rarely complicated.

But it is almost always time-consuming.

Not because there is a lack of tools or data, but because the analysis is still done manually, step by step, across multiple systems.

In this article, we go through what troubleshooting actually looks like in practice today, why it takes time, and how a more structured approach with AIOps can make a difference.

Troubleshooting in practice

Whether it's Kubernetes, AWS , or a CI/CD pipeline, the pattern often looks the same.

A service stops working as it should. Some alarm is triggered.

Let's start with the most obvious:

check status of podskubectl get pods -A
inspect eventskubectl describe pod
read logskubectl logs

If nothing stands out immediately, the troubleshooting continues:

compare latest deployment in ArgoCD or pipeline
check image tags and configuration changes in Git
verify that services have endpoints
control networks, routing and security groups in the cloud

Those are the right things to do.

But it happens in several steps. And each step requires a new hypothesis.

The problem is not what we do, but how

In most environments, all the information needed to find the root cause already exists:

application logs
metrics and alerts
Kubernetes events
changes via CI/CD and GitOps
network and infrastructure layer

The problem is that it doesn't fit together.

Troubleshooting therefore becomes:

→ jump between systems→ compare timelines→ identify changes→ try to build a coherent picture

It is not unusual for signals to the root cause to be present early, but for it to take time to connect the signals.

A concrete example

A deployment goes through without errors. Pods start, but do not go into ready.

At first glance:

no clear errors in logs
no crash loops
metrics look normal

After a while of troubleshooting, it turns out:

a label/selector does not match→ the service does not receive any endpoints

A small configuration error. But it requires that you:

checks deployment
compare labels
verifies service definition
connects the result

Not difficult. But several steps, in several systems.

What we mean by AIOps

AIOps is often described as “AI that resolves incidents.”

This is rarely the case in practice.

For us, it's more about structuring what is already being done.

To take the troubleshooting:

→ which today is done manually in several steps

and run it in a coherent flow.

What a structured troubleshooting flow might look like

For example, an AIOps flow can:

1. Collect status from Kubernetes

list pods and identify outlier states
read events and status from describe
check readiness/liveness

2. Link changes to the incident

compare latest deployment against previous working version
identify changed image tags or config
link the change to the time of the incident

3. Verify networks and dependencies

check that services have endpoints
verify that the correct ports are open
test reachability between components

4. Correlate the results

The important thing is not each check in itself. The important thing is to put them in context.

Example:

new deployment + changed image tag
pods in ImagePullBackOff→ clear direction for continued analysis

or:

service without endpoints
pods exist but do not match selector→ root cause

What it changes in practice

Such an approach does not mean that everything will be automatically solved.

But that means that:

troubleshooting doesn't start from scratch every time
the analysis becomes more consistent
you get on the right track faster

And perhaps most importantly: you spend less time understanding what happened and more time actually solving it.

Why this is relevant for modern IT environments

Environments have become more distributed:

Kubernetes and containers
multiple cloud services
CI/CD and GitOps
more integrations and dependencies

It increases flexibility. But it also increases complexity.

Continuing to troubleshoot in the same way as before quickly becomes ineffective.

This is where AIOps fits in.

Not as a new tool in the pile, but as a way to use what you already have – better.

AIOps as part of modern IT consulting services

For organizations working with DevOps, Kubernetes, and cloud, this is not just a technical issue.

It's about working methods.

As an IT consulting partner in Kubernetes, DevOps, and modern cloud environments, we often see the same pattern:

strong competence in the team
good tool
but no structure in troubleshooting

By introducing more structured flows for analysis, it is possible to:

shorten the time to root cause
reduce manual labor
create more predictable incident processes

Do you want to see what it might look like in practice?

We have built a concrete example where we let an AIOps flow run the entire analysis across multiple systems in one coherent step.

👉 Read the case here

Would you like to discuss how it would work for you?

Many organizations already have the tools in place: Kubernetes , observability platforms, and CI/CD flows. The challenge is often to get data, processes, and ways of working together. That's where external specialist expertise can help shorten the time from analysis to actual change.

Would you like to see what a similar setup could look like in your environment? Book a demo using the button below, and we'll show you how it works in practice! 👇

Book a demo

Or contact our colleague Ronnie Qvist , and he will help you further.