Resolve incidents faster with AI for IT operations
Before you start, this guide covers:
What AIOps is and what Rovo brings to IT operations teams
The top use cases for AI across incidents, alerts, and change
The Rovo features and agents behind each use case
Frequently asked questions and additional resources
Service Collection products referenced: Jira Service Management, Rovo
Reading time: 5 minutes
What is AI for IT operations teams (AIOps)?
AI for IT operations (also known as AIOps) is the application of machine learning and natural language processing to automate and enhance IT operational workflows. Atlassian delivers these capabilities through Rovo, an AI-powered solution in Jira Service Management.
Unlike generic tools, Rovo is powered by the Atlassian Teamwork Graph – a unique data layer that connects deployments, services, and team activity in real-time. This deep context allows Rovo to surface root causes, assess change risk, and resolve incidents with a level of accuracy that competitor AIOps tools can’t match.
What are the benefits of AIOps for IT operations?
AIOps reduces manual work across incident management, alert triage, change reviews, and post-incident documentation.
Accelerate incident response: Generate incident summaries, timelines, affected services, and recent deployments when an incident begins.
Identify likely root causes faster: Correlate code deployments, feature flags, and system dependencies to point responders toward probable causes.
Cut alert noise: Group and classify high-volume monitoring signals into clear, actionable incidents to reduce on-call fatigue.
Safeguard change delivery: Evaluate proposed changes against historical incidents and dependencies to move safe changes faster and flag risk early.
Automate post-incident learning: Draft post-incident reviews, stakeholder communications, and runbooks directly from incident history to ensure continuous improvement.
How do teams use Rovo to accelerate IT operations workflows?
Here are some of the most common ways operations teams put Rovo to work across the incident and change lifecycle.
Assess change risk with AI
Manual change reviews can be slow and inconsistent, and risky changes can still slip through. Rovo evaluates a proposed change against related history, dependencies, and past incidents to assess risk.
How Rovo assesses change risk:
Analyzes history: Scans past deployment data and incident records.
Identifies dependencies: Maps related services using the Teamwork Graph.
Calculates risk: Assigns a Low, Medium, or High risk score with suggested mitigations.
This gives teams a consistent starting point for reviewing changes and helps them move lower-risk changes forward more quickly.
Turn alert noise into one actionable signal
Monitoring, observability, and event tools generate a high volume of signals, which can overwhelm on-call teams and obscure the early signs of a serious issue. Alert intelligence, powered by Rovo, analyzes and groups related alerts from first- and third-party sources and helps distinguish signal from noise.
It then escalates related signals into incidents, giving responders a clearer starting point for triage and helping reduce alert fatigue.
Automate incident context and root cause analysis
When an incident occurs, on-call teams and incident managers need to quickly understand what went wrong and decide how to respond. But the information they need is often scattered across tools, documentation, and subject-matter experts. Rovo intelligently surfaces the right context, related resources, and next steps together faster, so responders can spend less time searching and more time restoring service.
During an active incident, Rovo Ops correlates Teamwork Graph data with information from third-party monitoring and observability tools such as New Relic, Dynatrace, and Honeycomb. It points responders toward likely causes, including an affected service, a recent deployment, a change request, or a feature flag. It can also suggest experts to involve.
How Rovo identifies root causes:
Correlates data: Connects monitoring alerts with Jira deployment logs.
Flags changes: Highlights recent feature flag toggles or code commits.
Suggests experts: Identifies the right service owners to involve based on historical activity.
Responders can then ask Rovo Ops follow-up questions, for example:
Which configuration items are affected?
Which recent changes are most likely related to this incident?
How were similar incidents resolved?
Close the loop with post-incident reviews and runbooks
Post-incident reviews help teams capture what happened, share lessons learned, and prevent recurring issues. But compiling them manually takes time and can leave gaps.
Rovo captures incident summaries, timelines, outcomes, and lessons to help teams create post-incident documentation. Teams can then review and share the content via Confluence, Slack, or email to prevent similar incidents in the future.
Customer spotlight: Vodeno
Switching to Jira Service Management was a game-changer. Now we've just got one tool running all of IT operations. And the AI features? Honestly, they're a lifesaver. Things like gathering data, creating tickets, and assigning work happen in the background, so we can focus on solving problems instead of chasing them.
Rafał Starzec, Jira Admin, Vodeno
The results: Vodeno cut alert noise and reduced incident resolution time from weeks to days, helping its IT Ops team spend less time firefighting and more time improving customer experience.
Frequently asked questions
What is AIOps?
AIOps, or artificial intelligence for IT operations, applies AI and machine learning to IT operations data and workflows. It helps teams correlate events, reduce alert noise, identify likely root causes, assess change risk, and automate repetitive incident-management tasks. Rovo is an AI solution for IT operations that helps teams accelerate incident and change management by summarizing incidents, identifying likely causes, reducing alert noise, assessing change risk, and drafting post-incident reviews. It draws on shared context from the Teamwork Graph.
How does Rovo help reduce mean time to resolution?
Rovo helps reduce mean time to resolution by assembling incident context and correlating changes and dependencies to point toward likely causes. By reducing the time responders spend gathering information and investigating, it helps teams work toward faster resolution.
Can Rovo help with change management?
Yes. Rovo can evaluate each proposed change against related history, dependencies, and past incidents to assess risk and streamline approvals.
Does Rovo work with third-party monitoring tools?
Yes. Rovo can correlate data from Atlassian and third-party monitoring or observability tools, and group alerts from first- and third-party sources.
How does AI improve incident management?
AI improves incident management by correlating vast amounts of data to identify root causes, summarizing complex incident timelines, and suggesting remediation steps, which significantly reduces Mean Time to Resolution (MTTR).