We’re gathered here today to lay to rest the relics of service desks past – repetitive tickets, endless queues, scattered portals. They served us faithfully, for a time. They brought order to chaos and made work visible at scale. They taught us patience. So. Much. Patience.
But they couldn’t survive in the AI era. They were born at a time when the best we could do was log, route, and report. Now, in the age of AI, what got us here won’t get us where the world is going. When the work itself is happening in real time – across code deploys, SaaS apps, devices, collaboration tools – service can’t be trapped in a queue or gated behind a form.
It’s time to say a grateful (relieved) goodbye, and reimagine service from the ground up – with AI, data, and teamwork at the center. Because what comes next is better. So. Much. Better.
It’s time to shatter the service quo.
The era of AI-native service
Over the past five years, Atlassian has helped 65,000+ organizations modernize service management, proving teams move faster when dev, ops, support, and business share one platform. Now we’re applying everything we’ve learned to the AI era.
We’re ushering in a new age of invisible, AI‑native service management where support and operations are no longer a department or a static process, but a living system of work that understands every team, every tool, every workflow.
In this era:
- Context is the differentiator. Generic AI sees tickets; AI-native service sees people, tools, work, and the relationships between them – what’s broken, who’s impacted, how to fix it, and how to prevent it next time.
- Service happens proactively. Work doesn’t line up in a queue; it routes itself – to the right human, the right AI agent, or the right team – before anyone even has to ask.
- Experience is paramount. It’s not about how many tickets you closed this week. It’s about the best possible experience for your employee or customer.
- Teamwork evolves. Service has always been a team sport – and that team now includes AI agents working alongside humans in increasingly intelligent and autonomous ways.
But this future doesn’t happen by bolting AI onto a legacy tool. It requires a platform purpose‑built for AI‑native work.
The Atlassian advantage
Atlassian isn’t just a system of record, it’s a system of work. Our platform is where work happens end‑to‑end: from planning projects to managing knowledge, shipping code, running services, and supporting employees and customers. We connect the dots other service players don’t even see – without the cost, complexity, and bloat of legacy platforms.
Our AI‑native approach to service is anchored on four principles:
1. Provide the best context.
The real advantage isn’t better models. It’s better data. The Teamwork Graph, Atlassian’s unified data layer, is rocket fuel for AI-native service. With a rich map of people, teams, issues, services, assets, knowledge, and relationships, it gives every model the context it needs to answer better, act smarter, and orchestrate across tools and teams.
2. Make service proactive
The best service experiences are often the ones you don’t even know are happening. In Service Collection, AI isn’t just layered on top – it’s baked into every interaction, workflow, and decision. Instead of static queues and reactive ways of working, issues are invisibly detected, routed, and resolved before they become roadblocks. Employees and customers don’t look for help, help finds them. Incidents are caught early, diagnosed, and resolved before customers feel real pain.
3. Prioritize the human experience
AI is powerful, but people are what make service exceptional. Rovo puts the human experience front and center in Service Collection, augmenting agents, employees, and customers at every step. Fewer dead ends, fewer form fills, fewer “let me transfer you” moments – and more journeys that feel intuitive, guided, and human.
4. Empower connected teams and agents.
Teamwork isn’t going away! It just looks a bit different in the AI era. The best teams have always found balance between autonomy and alignment: enabling groups to run fast with new ideas and products while staying aligned with other stakeholders. This balance has never been more important as we turn to AI to handle large chunks of work with increasingly autonomy. Teams that provide systems and guardrails to safely deploy and manage AI will see better experiences and greater return as they scale with AI.
Delivering AI-native service experiences across Service Collection
Teams are tired of vague “AI-powered” claims. It’s not enough to duct‑tape a few AI features onto an old workflow, throw the word “agentic” on your website and call it “AI‑native.” Leaders want to see real innovation and real AI transformation.
That’s why Atlassian is delivering features that fundamentally reinvent your team’s most critical service workflows. Here are just a few of the ways we’re making this AI‑native vision real across Service Collection:
1. Rovo Service: End‑to‑end request resolution and complex journey orchestration (available now)
Internal service teams can finally move beyond basic chatbots and FAQ deflection.
With Rovo Service, AI teammates can autonomously execute multi‑step workflows like software provisioning, access management, HR onboarding, and common employee requests from end to end – with humans in the loop.
Rovo taps into the Teamwork Graph to ground every response in your company’s knowledge, processes, and ticket history. It can:
- Understand who the requester is, their role, and what they’re allowed to do
- Orchestrate approvals, changes, and updates across Jira, Confluence, identity tools, and SaaS apps
- Hand off to humans gracefully when the situation demands judgment, further customization or exception handling
2. Incident Command Center: An AI‑native response hub for faster resolution (coming soon)
Say goodbye to incident response tool sprawl.
We’re launching Incident Command Center to consolidate alerting, investigation, and communications into a single, AI‑native journey. It pulls together signals from all corners of the Teamwork Graph: 3P observability tools like New Relic and Dynatrace, service maps from Assets, and deployment data from Bitbucket, GitHub, or GitLab, along with feature flags to visualize your service graph and pinpoint where things are breaking.
Instead of sifting through an endless swarm of alerts, teams see clear connections of signal: likely root causes, probable blast radius, recommended actions, and predicted business impact. When a major incident hits, incident managers land in a single place where timelines, context, ownership, and suggested next steps are assembled in real time. Humans stay in control; AI does the legwork.
Rovo handles the heavy lifting post-incident, too. Rovo Ops takes the lead, drafting and publishing the PIR with all relevant details and timelines to give incident teams a break after the storm. That’s when Rovo Dev takes over, generating work issues from the PIR for review and even development – tightening that loop from incident learnings to improvement.
3. Solution Composer: Design AI‑native service journeys in minutes (coming soon)
Standing up a new service used to mean forms, portals, and weeks of configuration.
With Solution Composer, teams can describe the service experience they want – for example, “Create an HR onboarding portal for the Williams Racing team” – and let Rovo draft the underlying workflows, request types, automations, and AI agents that power it.
Because Solution Composer is grounded in Teamwork Graph and your existing Atlassian configuration, it can reuse proven patterns, connect the right services and assets, and give admins a head start instead of a blank page.
The result: new, AI‑native service journeys that are born connected – not stitched together later.
Service so good you don’t even know it’s there
The real promise of AI in service isn’t flashy demos – it’s smoother workdays, fewer repeat issues, and employees and customers who get what they need without thinking about the process behind it. That’s what an AI‑native system of work is designed to deliver.
Service Collection is uniquely built for this moment. With Atlassian’s platform as the backbone, Teamwork Graph as the context layer, and Rovo powering all things AI, Service Collection turns that promise into concrete workflows across IT, operations, and support. The result is less time managing tickets, more time moving the business forward, and exceptional experiences for employees and customers.
“Atlassian’s vision clearly outpaced our previous ITSM solution, making Jira Service Management the strategic choice for our future.”
Get the full AI‑native service blueprint
Ready to figure out why your old service management playbooks are failing in the AI era? In our latest white paper, we dive deep into AI-native principles, architecture, and real world use cases to help your team shatter the service quo.
Join the Team ’26 livestream to see AI-native service in action
Team ‘26, Atlassian’s teamwork conference, kicks off tomorrow! Sign up for the digital event to join the fun and learn more about our vision for AI-native service. You can also catch the full run down of Service Collection product announcements including new hardware asset management capabilities, live chat in the Customer Service Management app, and more.
