At Team '25, Atlassian introduced the Teamwork Collection, bringing together Jira, Confluence, and Loom, significantly enhanced by Atlassian Rovo. This new integration helps break down information silos and promotes more effective human-AI collaboration. Previously, accessing these powerful tools, including Rovo's capabilities, required separate subscriptions and setups. Now, with a single offering, enterprises can tap into AI-enhanced workflows without the friction of complex setups or budget silos.

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While this removes many initial financial and technical hurdles, the real opportunity (and challenge) lies in how you maximize AI's value within your organization.  This article will guide you in building a roadmap to ensure you not only adopt AI-powered tools, but truly optimize its potential for measurable returns.

Beyond AI-powered tools

Getting ROI from AI isn’t just about access (as discussed in our Rovo for Everyone article). It’s about thoughtful, strategic integration and building a culture where AI is a meaningful part of how your teams work.

Common challenges to effective AI implementation and adoption include:

• Limited readiness: Organizations often lack a clear strategy for using AI in day-to-day operations.
• Decision-making complexity: Teams struggle with data overload, leaving them without the clear, AI-driven insights necessary for making quick, informed decisions.
• Tool and platform misalignment: When systems are siloed, the result is a set of disconnected tools that prevent AI from driving cohesive, cross-functional results.

Step 1: Determine your AI adoption baseline

Think of this as a maturity journey, moving from basic interaction to fully integrated, intelligent systems.. Inspired by proven industry models, ServiceRocket’s AI adoption framework includes:

• Level 0: Prompting.
  This foundational level is where most users begin, interacting with AI through direct questions or reusable instructions to accomplish specific tasks.
  Examples:
  
  • Ask Rovo in Confluence to summarize meeting notes.
  • Use Rovo in Jira to generate a draft user story.
• Level 1:Context-aware augmentation.
  Here, AI is brought closer to the user interface, integrated into the platform and offering contextual assistance in a few clicks. Imagine an AI assistant within your Jira work item or Confluence page, providing structured output, identifying gaps, suggesting edits to policies, or even recommending risk mitigation strategies to streamline and enhance your work.
  Examples:
  
  • Rovo in Confluence suggests edits or flags gaps in a document.
  • In Jira, Rovo identifies risk factors or recommends watchers.
• Level 2: Workflow-integrated agents.
  This stage enables significant workflow optimization. Here you identify key workflows and strategically embed AI agents aimed at automating tasks, performing assessments, and even assisting in issue re-prioritization, triaging and more. At this stage, these agents operate independently on low-complexity tasks within a single domain or specific area.
  Examples:
  
  • A Rovo agent in Jira helps categorize work items or suggests related service requests.
  • A Rovo agent creates knowledge base drafts in Confluence based on patterns identified in Jira.
• Level 3: Orchestrated agents.
  This represents a significant leap, where multiple AI agents function as a cohesive unit, passing outputs to or processing inputs from one another to achieve a common objective. Imagine a sequence of agents seamlessly handling a service request, from initial triaging and component identification to suggesting knowledge base articles and even updating the knowledge base, co-teaming with humans.
  Examples:
  
  • AI agents triage incoming requests, link relevant articles, and update Confluence.
  • Incident review workflows are assembled automatically across Jira and Confluence.

Many organizations find themselves at the earlier stages of this maturity model. Advancing through these maturity levels to unlock the full potential of the Teamwork Collection requires a strategic roadmap.

Step 2: Build your AI implementation roadmap

Once you have assessed your current AI maturity and identified some opportunities, the next crucial phase is to develop an evolving implementation roadmap that strategically integrates Rovo AI and Teamwork Collection features to advance your organization along the AI maturity spectrum.

Your roadmap should be dynamic, prioritizing initiatives that deliver measurable value and align with your business objectives. Here is how to approach it:

Define your AI destination and impact 

Start by establishing your current AI baseline (Step 1) and setting a realistic near-term AI target. Simultaneously, develop a rigorous understanding of how AI can deliver quantifiable business outcomes within your specific operational context. Knowing this will inform your strategic choices and enable you to assess the effective impact of AI.

Optimize workflows for AI infusion

Map your processes to find opportunities where Rovo Agents can streamline workflows by simplifying, accelerating, or automating tasks. A good place to start is using AI to “shift left” by handling early work like research or drafting content, freeing your team for higher-value activities.

Prioritize what matters 

Develop a clear, phased plan that balances quick wins with long-term strategic goals for AI integration. This involves prioritizing initiatives based on potential ROI versus implementation effort, focusing on building momentum and delivering quick wins.

Cultivate internal AI knowledge

Create a living library of prompts, best-practice examples, and instructions. This foundational knowledge is the key to guiding AI agents and guaranteeing grounded, accurate results.

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Diligently following these steps can lead to enhanced productivity and the development of a future-proof System of Work.

Step 3: Drive effective adoption and enablement 

The implementation roadmap created in Step 2 provides the strategic framework. But to truly get the anticipated benefits, the team has to adopt and use the Rovo AI-powered Teamwork Collection effectively. 

This step will focus on the critical human element: How leadership can foster the genuine adoption and skills needed to transform a new technology investment into real results?

Here’s how to champion this transformation:

Share the vision and benefits

Clearly explain the vision and benefits that Rovo AI can bring. Position it as an evolving capability instead of a static solution. Be up front about changes, timelines, and support while encouraging open, two-way dialogue to address concerns. Also, regularly share progress and success stories.

Empower teams with training

This practical, hands-on training should be tailored to their workflows in Jira, Confluence, and Loom, focusing on real-world Atlassian Rovo use cases. Then, encourage internal AI champions to explore and share novel applications, and maintain a dynamic knowledge base that is regularly updated to reflect the latest AI functionalities and evolving best practices.

Keep iterating and re-evaluating through feedback

Set up channels for feedback. Track adoption and performance. Revisit your roadmap regularly to stay aligned with business goals. Celebrate wins to keep people inspired and invested.

By focusing on both tools and people, you’ll turn AI from a feature into a competitive advantage.

Empower Your Organization with Atlassian Rovo 

Accessing AI tools is just the first step. To unlock real value, you need a partner who can help you implement, optimize, and evolve your approach. ServiceRocket helps organizations turn Atlassian Rovo into a strategic asset, aligned with your goals, integrated into your workflows, and delivering measurable results.   

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About the Author:

YC Lian is ServiceRocket’s Executive Director of Technology. As a leader in AI adoption and technology governance, YC Lian is focused on leveraging new technologies to drive organizational efficiency and scale.