The Future of Teamwork and Service Management in the AI Era
At Atlassian Team'26 California, during the session Team Talk: State of Teamwork in the AI Era & Service Teams Deep Dive: The Future of AI-Powered Service, Atlassian continued to highlight an important message in its product strategy: AI should not exist as a separate tool. It should be part of a System of Work, where work, data, people, processes, and operational context are connected on one platform.
The session featured MaSonya Scott, Service Management Evangelist at Atlassian; Sven Peters, AI Advocate at Atlassian; and Janessa Drainville, AI Strategist at Atlassian. Through discussions about Teamwork Graph, Rovo, and Service Collection, Atlassian showed how AI is becoming more deeply embedded in teamwork and enterprise service management.
AI Is an Important Part of the System of Work
One of the key points in the opening came from Sven Peters. He emphasized that companies are not only choosing software. They are choosing what kind of company they want to become. According to him, the answer is not in a strategy deck. It is in the System of Work, where real work happens every day.
This message clearly reflects Atlassian's direction. AI should not be locked inside a separate tool, a separate screen, or a standalone chatbot. AI needs to appear where teams already work, from Rovo Chat and Atlassian apps to browsers, terminals, and external tools. When AI is placed inside real workflows, it can do more than answer questions. It can help unblock projects, support incident resolution, move ideas into production, and suggest decision paths.
Teamwork Graph: The Context Layer That Helps AI Understand Work
Atlassian also introduced teamworkgraph.com as a central access point for Teamwork Graph. It is a place where users can download the CLI, connect MCP servers with tools such as Figma, Cloud Code, ChatGPT, and other AI apps, and see the graph of their own organization.
In simple terms, Teamwork Graph makes hidden work connections visible. It shows who is working on what, which work is connected to which apps, comments, teams, projects, and contributors. When this context layer becomes visible, AI agents can understand work more deeply instead of handling each question or ticket separately.
This is an important foundation for Atlassian to move AI from a personal support tool to an operating capability for the whole organization.
Learn more about how Teamwork Graph works in an AI-native organization – insights from Atlassian Team’26!
Service Collection: From ITSM to Enterprise Service Management
One of the main topics in the Team Talk session was Service Collection. Atlassian introduced it as a new set of service management capabilities designed to expand the role of service management in the enterprise. In the past, service management was often understood mainly as ITSM. It served IT teams by helping them manage tickets, incidents, alerts, and internal support requests. Now, Atlassian is positioning service management more broadly: as a shared operating capability for the whole organization.
What Does Service Collection Include?
According to the session, Service Collection includes Jira Service Management, Customer Service Management, Assets, and Rovo. Jira Service Management remains the core ITSM platform, supporting capabilities such as incident management, alert management, and employee support.
However, the new point is that Atlassian is no longer limiting service management to IT. It is expanding service management to other teams such as HR, Dev, Customer Support, Facilities, Finance, and Operations.
Why Does ITSM Need to Expand Across the Enterprise?
In reality, IT is not the only team that needs service management.
HR needs to handle onboarding, leave requests, benefits, and employee information changes.
Finance needs to manage payment requests, reimbursements, contracts, and invoices.
Facilities needs to receive requests about equipment, office space, and meeting rooms.
Dev and IT need to work together on incidents, bugs, and change requests.
If each department uses a separate tool, companies can easily face fragmented data, broken processes, and inconsistent user experiences. Employees may not know where to send requests. Service teams may not have enough context to work together quickly and accurately.
Enterprise Service Management: Bringing ITSM Discipline to Every Department
Service Collection solves this problem by bringing many service workflows into one platform. This is the core idea of Enterprise Service Management: applying proven ITSM principles such as request portals, routing, SLA management, workflows, automation, knowledge bases, and performance reporting across the whole enterprise.
In other words, Enterprise Service Management does not replace ITSM. It expands the value of ITSM to other departments. The goal is to help every service team operate more clearly, measure performance better, and improve continuously.
Jira Service Management Becomes a Service Operations Platform
In the Service Collection picture, Jira Service Management becomes a service operations platform for many functional teams, instead of being only a tool for IT. This is important because many business problems do not stay inside one department. A serious incident can involve IT Ops, Dev, Customer Support, Communications, Legal, and even leadership.
When all these teams work on one platform, they can share the same view: what the problem is, how serious the impact is, who is responsible, what has already been done, and what the next step should be.
A Foundation for AI-Powered Service
The value of Service Collection is not only about putting many processes into one system. More importantly, it creates the data and context layer that allows AI like Rovo to support teams more effectively.
When tickets, assets, processes, documents, and teams are connected, AI can understand problems more deeply, suggest more accurate actions, and automate repeated steps.
For this reason, Service Collection represents a shift from traditional ITSM to a connected, context-rich, and AI-ready Enterprise Service Management model.
Assets: The Data Foundation for Operational Context
One important point in the session was the new role of Assets. In the past, asset management was often understood in a narrow way: managing laptops, servers, network devices, software, licenses, or other IT assets. But in Service Collection, Assets is being elevated into a foundation for managing business objects across the enterprise.
This is an important change. As service management expands from IT to the whole organization, the meaning of "asset" also needs to become broader. An asset is not only a physical device. It can also be a business system, an application, a service, a contract, a vendor, a location, a team, an employee, or any business object related to operations.
When these objects are managed in a structured way, service teams have more context to handle requests.
For example, when there is an incident related to a payment system, the support team does not only see a single ticket. They can also see which service the system depends on, who the owner is, which vendor is involved, which customers or departments are affected, what the latest change was, and which assets are connected to the incident.
This makes service management more proactive and data-driven. Instead of relying on scattered experience or asking for information through many channels, service teams can use a clear operational context layer to make faster and more accurate decisions.
Customer Service Management: Extending Service Management to Customers
Alongside Jira Service Management and Assets, Customer Service Management is another major highlight in Service Collection. Atlassian introduced this app to extend service management beyond internal support and help companies serve external customers better.
Customer Support Should Not Be Only a Cost Center
During the discussion, Janessa Drainville asked about the journey of bringing Customer Service Management into the product set. The Atlassian representative emphasized that organizations need to rethink the role of customer support.
For many years, customer support has often been seen as a "cost center." It has been treated as a separate team, mainly measured by productivity, response speed, and the ability to handle more requests with fewer resources.
However, this view is becoming limited, especially when customer experience depends more and more on the ability of the whole organization to respond quickly, accurately, and consistently.
Connecting Customer Support with the Rest of the Organization
The key difference of Customer Service Management is its ability to connect customer support teams with other related teams inside the business.
When a customer sends a request or reports an issue, the ticket is no longer isolated in a separate support system. It can be linked to technical issues, incidents, Dev teams, assets, product documents, or internal operation processes.
This is especially important for technology, finance, telecom, and other organizations with complex products or services. In these environments, the support team often cannot solve the whole issue alone. They need information from Dev, IT, Product, Operations, or other expert teams.
Closing the Gap Between Customers and Resolution Teams
If collaboration between support and internal teams still happens through email, chat, or manual meetings, response time becomes longer and customer experience is affected. Customer Service Management is designed to close this gap.
The support team can receive customer requests, connect each request to the right resolution team, and track the full process on one platform. As a result, the company can respond to customers more consistently, solve issues faster, and reduce the risk of information getting lost between teams.
Improving Customer Retention
In the AI era, customer support is not only a place that handles problems after customers face issues. When it is connected with service management and operational data, customer support can become an important part of the customer experience.
Customer Service Management therefore does more than improve support efficiency. More importantly, it helps companies turn customer support from a cost center into a customer retention capability, where every request is handled faster, with more context, and with stronger connection across the organization.
Rovo and the Future of AI-Powered Service
In the discussion about the future of service teams, one message stood out: AI does not replace humans. AI works with humans to solve problems faster. MaSonya Scott and Janessa Drainville also explored this point when leading the discussion about Service Collection and AI in service management.
One example was the incident command center. When an incident happens, even before a human logs into the system, AI can start analyzing the possible root cause, collect context, and suggest the next actions. In employee support, many simple Tier 1 tickets can also be resolved automatically by AI.
This shows that Rovo's role in Service Collection is not only to act as a chatbot that answers questions. Rovo is placed inside the service management workflow to support three main activities: understanding context, suggesting actions, and automating repeated steps.
For service teams, these capabilities are important because a lot of time is often spent reading tickets, finding related information, identifying the right owner, looking for old documents, updating status, or asking other teams for more details.
When Rovo is connected with Teamwork Graph and data in the Atlassian Platform, AI can understand a problem in a broader context.
Which service is this ticket related to?
Has a similar incident happened before?
Who handled this issue in the past?
Which document is relevant?
Which recent change may have caused the issue? What should the next step be?
This is the difference between disconnected AI and AI placed inside a context-rich System of Work. AI does not only respond to commands. It can take part in how the organization operates work.
What This Means for Businesses: Service Management Becomes a Shared Operating Capability
From the Team Talk session, it is clear that Atlassian is positioning Service Collection as a unified service operations platform for the enterprise. It is not only a new set of tools. It is an important shift: from ITSM to Enterprise Service Management, from internal support to external customer support, and from manual ticket handling to AI-powered service management.
For businesses, this shift has three major meanings.
First, service management is no longer only IT's responsibility. Every department has requests, workflows, SLAs, and user experiences that need to be managed properly.
Second, AI can only deliver real value when it has enough context. If data, tickets, assets, documents, and teams are still scattered across many places, AI will find it hard to suggest the right action or act effectively.
Third, customer support and employee support are entering a new stage. Instead of only optimizing response speed, companies need to build a service management system that can connect, learn, and improve continuously.
Conclusion
Atlassian's session showed a clear direction: the future of teamwork and service management is not about adding one more AI tool into existing processes. The future is about building a System of Work where AI, data, people, and processes are connected.
With Teamwork Graph, Rovo, and Service Collection, Atlassian is bringing AI deeper into how businesses operate: from managing work and resolving incidents to supporting employees, serving customers, and making decisions.
This is also why Service Collection should not be seen only as a set of service management products. It should be seen as a platform that helps businesses redesign how teams deliver services in the AI era.
Learn more about AI-powered Systems of Work.
