AIOps Services and Implementation

OVERVIEW

AIOps (artificial intelligence for IT operations) applies machine learning and automation to the alerts, events, logs, and metrics your IT operations teams already generate — correlating related events into a single problem, identifying the likely root cause, and triggering the right response automatically. The goal isn't more dashboards. It's fewer tickets, quieter on-call rotations, and faster recovery. Avocado implements AIOps on top of observability data in Dynatrace and Splunk, integrated with ServiceNow and your existing ITSM workflows, so automation is built on accurate topology rather than guesswork.

AIOps is only as good as the data underneath it. Many organisations invest in an AIOps capability expecting it to cut noise straight away, then find it can't correlate events because it doesn't know how services depend on each other, or because parts of the estate aren't monitored at all. That's why Avocado treats AIOps as an observability problem first: get topology, telemetry, and data quality right, then layer correlation, root cause analysis, and automation on top.

Who is this for?

You'll recognise the situation

Your operations teams are overwhelmed by alerts, and most of them don't need any action.

A single incident triggers dozens of tickets across different tools and teams.

Mean time to resolution isn't improving, despite more monitoring.

You want to automate routine fixes, but don't trust your data enough to let automation act on it.

You've invested in an AIOps or event management tool that hasn't delivered the noise reduction you expected.

What's included?

Fewer tickets, quieter on-call, faster recovery

Event correlation and noise reduction

Group related alerts from across your tools into one actionable problem, instead of dozens of separate notifications.

Automated root cause analysis

Identify the most likely cause using real-time topology and dependency data, not just alert timestamps.

Anomaly detection and predictive alerting

Baseline normal behaviour and flag deviations before they turn into outages.

ITSM integration

Create, enrich, route, and close incidents in ServiceNow automatically, with root cause and business impact already attached.

Automated remediation

Trigger runbooks and workflows for known, repeatable issues — restarting services, scaling resources, or rolling back a release.

Capacity forecasting

Use historical trends to anticipate resource constraints before they affect performance.

How do we deliver it?

Assess, build foundations, automate

Operations assessment

Review alert volumes, incident patterns, tooling, and data quality to find where AIOps will have the most impact.

Observability foundations

Close telemetry and topology gaps, so correlation and root cause analysis have accurate data to work with.

Use case prioritisation

Start with high-volume, low-risk scenarios such as noise reduction and ticket enrichment.

Automation build

Integrate with your ITSM platform and build remediation workflows, with human approval steps until confidence is established.

Continuous tuning

Measure noise reduction and MTTR, refine correlation and automation, and expand coverage over time.

What tools and technology do we use?

Davis AI and Splunk ITSI

Dynatrace (Davis AI) and Splunk (IT Service Intelligence) as the core AIOps engines, built on observability data from the same platforms and integrated with ServiceNow and other ITSM platforms, plus your existing automation and runbook tooling.

What outcomes can you expect?

What you walk away with

Fewer, more meaningful alerts.

Faster MTTR through automated root cause identification.

Less manual triage and fewer duplicate tickets.

More incidents resolved automatically, before users notice.

How is this engagement structured?

Flexible engagement models — a focused AIOps implementation, part of a broader observability program, or ongoing managed services. Contact us to discuss the right approach.

Common questions

What is AIOps?

AIOps uses machine learning and automation to analyse operational data — alerts, events, logs, and metrics — so IT teams can detect, diagnose, and resolve problems faster and with less manual effort.

What's the difference between AIOps and AI observability?

AIOps uses AI to run IT operations better. AI observability does the reverse: it monitors AI systems themselves, such as large language models and AI agents. The names are similar, but they solve different problems.

Do we need observability in place before AIOps?

Ideally, yes. AIOps relies on accurate topology and telemetry. Without it, event correlation and root cause analysis become guesswork. Closing those gaps is part of the engagement.

Will AIOps replace our operations team?

No. It removes repetitive triage and routine fixes, so your team can focus on complex problems and improvement work.

Does AIOps work with ServiceNow?

Yes. Incidents can be created, enriched with root cause and business impact, routed, and closed in ServiceNow automatically.