Three Companies Using AI Agents to Automate IT Service Workflows

Organizations are deploying AI agents that handle complete workflows rather than individual tasks, and the shift is accelerating. Agentic automations in Jira Service Management are growing 3.4 times faster than traditional rule-based automations, according to Atlassian, which recently shared implementation stories from three customers.

The distinction matters: traditional automation speeds up discrete actions, while agentic automation removes entire procedural steps from workflows. Three companies demonstrate how this plays out in practice.

Axpo eliminates translation friction for cross-language support

Axpo, a Swiss energy provider, operates second-level IT support from Madrid for German-speaking users. The language barrier created constant friction—agents copied ticket text into external translation tools, waited for results, then switched back to respond.

The company embedded Rovo, Atlassian's AI agent platform, directly into Jira Service Management. Now when a German-language ticket arrives, Rovo detects the language, translates the summary and description into English, and writes the translation to a custom field. Every subsequent German comment triggers automatic translation posted as an internal note, preserving the original author and timestamp.

Madrid agents now handle German tickets entirely within their primary tool, with no context switching or manual translation steps.

SpotOn surfaces institutional knowledge at ticket creation

SpotOn, a San Francisco restaurant technology provider, faced a different challenge: analysts spent significant time hunting for context across systems when new IT support tickets arrived. Relevant past tickets, documentation, and resolution patterns existed but required manual search.

SpotOn built a Rovo-powered agent that deploys multiple sub-agents in parallel the moment a ticket is created. The system adds a single comment containing a summary, similar historical tickets, recommended actions, a suggested assignee, and links to relevant documentation.

Analysts open tickets to find investigation work already completed, enabling faster initial response and more consistent handling based on institutional knowledge.

Vodeno automates the full incident lifecycle

Vodeno, a European banking-as-a-service platform, needed to compress the entire incident management process—from root cause analysis through post-incident review and follow-up task creation. Manual processes were slow and inconsistent, with follow-up work often deprioritized after incidents closed.

Using Rovo Ops with Jira Service Management automation, Vodeno now runs incident management as a continuous agentic workflow. When an incident is created, Rovo Ops analyzes available context and proposes the most probable cause. Upon resolution, the system creates a post-incident review in Confluence, links it to the incident record, extracts follow-up actions, and generates Jira tasks to track completion.

The result: faster diagnosis, consistent documentation, and guaranteed follow-through on preventive work.

Why it matters

These implementations share a common pattern: AI agents eliminate hand-offs between systems, tools, or process steps rather than simply accelerating existing tasks. Axpo removed translation hand-offs, SpotOn removed context-gathering hand-offs, and Vodeno removed documentation and follow-up hand-offs. Each change freed human workers to focus on judgment and decision-making rather than procedural steps. The 3.4x growth rate suggests this architectural shift—deleting workflow steps rather than optimizing them—represents a fundamental change in how organizations approach automation.

These customer stories were first reported by Atlassian in a blog post on Jira Service Management automation.