Incident management and AIOps: A guide for IT Ops teams
Before you start, this guide covers:
What is incident management and AIOps
How Jira Service Management handles each stage of the incident lifecycle
Additional FAQs and resources
Service Collection products referenced: Jira Service Management, Rovo
Other Atlassian products referenced: Jira, Statuspage, Bitbucket, Confluence
Reading time: 5 minutes
Most IT Ops teams don't lack tools. They have a shortage of signal. Monitoring systems generate more alerts than any team can meaningfully process, and when every tool requires its own login, context, and workflow, incident response slows before it starts.
Jira Service Management brings alert management, incident records, service context, and post-incident reviews into a single AI-native workflow. The Rovo Ops agent conducts investigations, drafts communications, and generates post-incident reviews, so responders can spend their time resolving the problem rather than managing the process around it.
What is incident management and AIOps?
Incident management is the process of detecting, responding to, and resolving unplanned IT disruptions, from the initial alert through service restoration and post-incident review. Its goal is to minimize business impact and reduce time to resolution. AIOps (artificial intelligence for IT operations) applies AI and machine learning to operations workflows, including grouping and deduplicating alerts, separating signal from noise, identifying potential root causes, and recommending runbooks so responders can focus on resolving incidents.
In Jira Service Management, the two come together to empower AI-native incident management where AI is built directly into every stage of the incident lifecycle - not bolted on as a separate module. In Jira Service Management, that means AI handles the work that slows teams down before a human ever opens a ticket: grouping and classifying alerts, pre-filling incident records, surfacing affected services, and recommending runbooks, all in context, inside the same platform where resolution happens.
The difference from traditional ITSM is structural. There's no tool switching between your alerting platform, ITSM, and knowledge base. Alert grouping, incident creation, agentic investigation, and post-incident review all run in one place, on one data layer, the Atlassian Teamwork Graph, which connects signals from Jira, Confluence, Bitbucket, and third-party monitoring tools into a unified service topology.
How Jira Service Management handles each stage of an incident
Before creating an incident, monitoring integrations send signals to Jira Service Management
Before an incident record exists, monitoring and observability tools send alerts into Jira Service Management through native integrations, webhooks, and supported connectors. Signals from tools such as Datadog, BigPanda, and AWS enter a shared operations workflow with the source, affected service, and alert details attached, giving teams a consistent starting point instead of requiring responders to monitor each tool separately.
Once alerts arrive, AI alert grouping deduplicates related signals and classifies them by urgency. Per-organization machine learning models group recurring patterns, score severity, and surface anomalies, so responders can focus on the underlying incident rather than processing a flood of individual notifications.
Responders can link relevant alert groups directly to an incident record, preserving the source context and full audit trail. Incidents can also be created automatically when configured alert thresholds are breached, with key fields pre-populated, allowing the team to move from signal to coordinated response without a separate ticketing step.
AI-assisted triage before a responder starts
Before a responder begins their own incident investigation, the AI suggestions panel has already completed its first pass. It pulls from the incident record, past similar incidents, and the Teamwork Graph to surface:
Affected services and supporting infrastructure
Related incidents and recent changes (commits, deployments, feature flags)
Relevant runbooks and knowledge base articles
Recommended playbooks for the incident type
Draft stakeholder communications
Responders act on any of these directly from the incident record. No tab-switching to cobble together context from separate tools.
On-call scheduling and escalation
Jira Service Management notifies the right responders through on-call schedules and escalation policies, then connects them in a dedicated incident channel where context, decisions, and updates link directly to the incident record. No separate war room tool required.
Rovo Ops agent: agentic investigation and resolution
Rovo Ops is a built-in AI agent for IT operations, available to all paid Service Collection customers on Premium and Enterprise plans.
During an active incident, Rovo Ops investigates the root cause in a conversational manner. Ask it which configuration items are affected, which recent changes are most likely to blame, or what past incidents with similar signatures were resolved. It pulls from the Teamwork Graph — first-party DevOps signals like commits, pull requests, deployments, and feature flags that no third-party AIOps tool can access natively.
Rovo Ops works in Slack, too. On-call engineers in incident war rooms can query it directly: "@rovo investigate this alert" or "@rovo who can help with this incident" without leaving their existing workflow.
It also connects to third-party observability tools via MCP, pulling telemetry from New Relic and Dynatrace, with Honeycomb, Coralogix, and Lansweeper coming soon.
Status pages: communicate incident updates proactively
Branded public and private status pages display real-time component status and historical uptime throughout the incident. The response team stays focused on resolution, and stakeholder communications run in parallel, automatically.
How Rovo Ops writes the post-incident review
PIRs are where repeated incidents get prevented, and the first deliverable a tired team skips. When an incident closes, Rovo Ops automatically drafts the post-incident review from the incident record, alert data, and the swarm channel, including timeline, root cause, contributing factors, key decisions, and follow-on tasks.
The team edits a draft. They don't write from scratch.
Six out-of-the-box automation templates, including "when an incident closes, automatically create a PIR,” mean Rovo Ops can trigger this workflow without a manual prompt.
Incident reporting and MTTR tracking
Jira Service Management tracks performance across the full incident lifecycle with reports and logs in Operations.
Volume and resolution trends: Spot where incident rates climb and where backlogs form before they compound.
MTTR by service and team: Find where response is consistently slow — with enough granularity to act.
SLA performance: Track met vs. breached in real time with SLA reports.
Custom dashboards: Configure gadgets and filters so team leads can generate and share reports without relying on an analyst.
Customer spotlight: 24 Hour Fitness
Having automated on-call [in Jira Service Management] is great for us because you don't have a human in the loop that's going to make a mistake and call the wrong person.
— Rick Westbrock, Principal Applications Engineer, 24 Hour Fitness
The results: 75% less alert noise, 37% of ITSM budget saved, and 100% change traceability.
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Frequently asked questions
What is Rovo Ops?
Rovo Ops is Jira Service Management’s built-in AI agent for IT operations. It investigates incidents, suggests likely root causes, drafts stakeholder communications, and generates post-incident reviews. Rovo Ops is available to Service Collection customers on Premium and Enterprise plans.
How does AI alert grouping work in Jira Service Management?
Per-organization machine learning models analyze incoming alerts from connected monitoring tools, group related signals by urgency, and surface anomalies. Responders see a consolidated incident view instead of individual alerts from each monitoring source.
Does Rovo Ops work in Slack?
Yes. On-call engineers can query Rovo Ops directly in Slack incident war rooms using natural language — "@rovo investigate this alert" — without switching to the Jira Service Management interface.
What monitoring tools does Jira Service Management integrate with?
Jira Service Management integrates out of the box with Datadog, BigPanda, AWS, and hundreds of other tools. The Rovo Ops agent also connects to third-party observability tools such New Relic and Dynatrace via MCP for direct telemetry access during investigations.
Are alert and on-call capabilities included in Jira Service Management?
Yes. Jira Service Management includes end-to-end alert, on-call, escalation, and incident response capabilities—all in one connected platform.
Is AIOps included in Service Collection Premium?
Yes. AI alert grouping, the AI suggestions panel, Rovo Ops, and AI PIR generation are all included in Premium and Enterprise editions — no separate AI module or additional licensing required.