Atlassian’s recent foray into artificial intelligence – Rovo – has introduced powerful new features across Jira, Confluence, Bitbucket, Trello, and beyond. Rovo is Atlassian’s generative AI teammate, combining AI-powered search, conversational chat, and smart agents to help teams work faster and smarter. By surfacing knowledge across tools, answering questions, summarizing content, and automating workflows, Rovo promises to boost productivity and reduce the friction of modern teamwork.
However, as organizations enable these tools, a critical question arises: How can you determine if your employees are actually utilizing Atlassian’s AI? In this blog, we’ll explore how to track Atlassian AI usage, why it matters, and how to do it in a way that respects employee privacy. We’ll cover Atlassian’s built-in usage tracking options (like Jira and org-level analytics), discuss employee surveillance concerns, and show how you can gain actionable insights – with a spotlight on Worklytics as a privacy-first solution to measure AI adoption.
Measuring the adoption of Atlassian’s AI, Rovo, is key to understanding its impact. For software developers and team leads, seeing usage patterns can reveal whether the AI is truly helping with day-to-day work.
For HR managers and people analytics teams, AI usage metrics offer insight into skill adoption and tool engagement across the workforce. And for executives who approved the investment in Atlassian’s Premium plan, usage data provides a tangible way to gauge ROI on the AI features.
Tracking usage also helps identify success stories and gaps. You might discover that a department isn’t using the AI features at all; in this case, you can investigate why – perhaps due to a lack of awareness, or maybe the AI doesn’t yet address their specific needs. These insights enable data-driven decisions: doubling down on effective use cases and providing enablement where adoption is lagging.
The good news is that Atlassian has since delivered on that promise. They introduced Rovo Insights in the admin dashboard, which gives basic metrics on Rovo usage. Specifically, Atlassian’s admin guide now includes an AI Usage section with two key charts:
Atlassian’s built-in Rovo Insights dashboard shows AI usage metrics. In this “Active Rovo Users” chart, an admin can see how many users have interacted in the past 28 days, helping track adoption over time.
In practice, Org Insights displays charts for active users across products (e.g., Jira, Confluence), user counts over time, and other usage patterns.
While Rovo Insights provides high-level numbers, you may find them somewhat limited in their detail.
They tell you how many people use the AI and how often overall, but not much about who or how in detail. For instance, if you want to know which teams or departments are embracing Rovo versus which aren’t, the out-of-the-box charts won’t break that down.
Likewise, the “active users” metric doesn’t distinguish between users who have tried the AI once and those who are heavy daily users. And, of course, these dashboards only cover Atlassian’s AI – they won’t show if employees are using other AI tools, such as Slack GPT or Microsoft Copilot.
To gain deeper insights or a more holistic view, organizations often turn to third-party analytics solutions like Worklytics — a privacy-first analytics platform that helps companies measure productivity, efficiency, and adoption across AI and collaboration tools by aggregating usage data into anonymized, actionable dashboards.
Whenever we discuss tracking employee software usage, it’s natural for employees (and managers) to have concerns. Nobody wants to feel like “Big Brother” is watching their every move at work.
In fact, numerous studies have shown that heavy-handed employee surveillance can backfire – monitored workers report higher stress, lower morale, and even reduced performance and skill development.
So how do we reconcile the need for insights with respect for privacy? The key is to approach usage tracking as a tool for empowerment, not punishment. Here are some guidelines to consider:
Focus on trends at the team or organization level, rather than singling out individuals. It’s more useful to know that “only 30% of the Engineering team has tried the new AI search feature” than to call out that “Alice hasn’t used it yet.” By examining aggregate data, you can gain adoption insights without creating a surveillance atmosphere. Modern people analytics platforms, such as Worklytics, are designed with privacy at their core, utilizing anonymization to protect individual data.
If you plan to monitor usage of Atlassian AI (or any tool), let employees know what you’re tracking and why. Emphasize that the goal is to improve support and identify needs, not to judge anyone’s performance. It’s vital for managers to be transparent about the choices they make, from setting strategy and goals to decisions regarding promotions, hiring, or layoffs.
Frame your analysis in terms of opportunities. For example, if a certain team’s AI usage is low, approach it as “perhaps they haven’t had a chance to learn it – let’s offer a workshop or identify useful cases for them,” rather than “they’re lazy or resisting change.”
Conversely, if some employees are making excessive AI requests (which could indicate they are struggling with a task), that might be a signal to check if they need better training or if the AI isn’t yielding good results, rather than immediately flagging it as misuse.
Depending on your region, there may be regulations around employee data and monitoring. Always comply with privacy laws and your company’s policies. Even if not legally required, obtaining some form of consent or at least acknowledgment from employees about data collection is a good practice.
The bottom line is that tracking software usage should never feel like spying. It should feel like observing a system to improve it. When done right, employees can actually appreciate that you’re looking at adoption data – for instance, to ensure no one is left behind in training, or to make a case for improving a tool if usage is low because it’s not helpful.
Having the numbers in hand is just the first step. The true value lies in interpreting those metrics and translating them into actionable insights for your organization. Here are a few ways you can leverage Atlassian AI usage data to drive improvements:
By focusing on these kinds of insights, you shift the conversation from “who clicked the AI button how many times” to “how can we help more teams benefit from AI and work smarter.” This is ultimately the mindset you want in adopting any new technology: use data to illuminate patterns and guide strategic decisions.
To truly unlock these insights, many organizations find value in specialized analytics tools that go beyond what Atlassian’s native dashboard offers. Let’s look at how a solution like Worklytics can help you dive deeper while keeping privacy front and center.
While Atlassian’s built-in usage charts give a snapshot of Atlassian AI adoption, you may need a more comprehensive view – one that can combine data across multiple tools, provide richer analytics, and ensure privacy is protected by design. Worklytics is one such solution that caters to these needs. Worklytics is a workplace analytics platform that integrates with a wide range of systems (Atlassian included) to deliver insights into how teams collaborate and use their digital tools.
One standout benefit of Worklytics is its ability to connect data from all your corporate AI tools – not just Atlassian. Many companies today are incorporating AI features into platforms such as Slack, Microsoft 365 (Copilot), Google Workspace, Zoom, and others. Worklytics can ingest data from these sources and provide a unified view of AI adoption across your organization.
You can track usage by team, role, or location and monitor progress over time towards your AI enablement goals. Such a birds-eye view is incredibly valuable for technology leaders and people analytics teams who are steering company-wide AI initiatives.
Unlike traditional “employee monitoring” tools that might infringe on privacy, Worklytics is built with a privacy-first philosophy. It anonymizes and aggregates data so that you get insights without reading anyone’s actual messages or content.
For instance, Worklytics might tell you that a team had 200 AI queries in Jira last week and show patterns of usage times, but it won’t show the text of those queries or who exactly asked what. By excluding work content and personal identifiers, Worklytics helps maintain employee trust while still providing leaders with the data they need to improve tools and processes. This approach aligns with the best practices we discussed in the previous section – it’s analytics, not surveillance.
Worklytics provides out-of-the-box dashboards tailored to various use cases. In the context of Atlassian AI, you might use a dashboard for “AI Adoption” that highlights key metrics like percentage of active users (and how this changes week over week), top teams by usage, and even comparisons to benchmarks in your industry. The platform can generate over 400 metrics from integrated data – for example, combining Atlassian data with calendar and email data to determine if meeting load affects how much people use AI (as a hypothetical analysis). All these metrics can be filtered and explored without needing a data scientist on hand.
Furthermore, Worklytics allows you to export the aggregated data to your own data warehouse or BI tools if you want to do custom analysis. So if there are very specific questions (like “do people who use the AI have higher engagement scores in surveys?”), the data is accessible for advanced analytics – all while respecting privacy constraints.
Imagine you’re a VP of Engineering interested in how the new Jira AI features are affecting your teams. Using Worklytics, you could see that in Q1, only 10% of engineers tried the “AI issue summarization” in Jira.
After an enablement program in Q2, which rose to 60%, and at the same time, you notice the average Jira ticket resolution time dropped by, say, 15%.
While many factors contribute to faster resolutions, data indicates a positive trend alongside increased AI usage. You also notice via Worklytics that one department still lags at 20% adoption. With that knowledge, you can zoom in on that department – perhaps schedule a meeting with its manager to understand their challenges or provide additional AI training targeted to their workflow. Throughout this process, no individual is being chastised for not using AI; it’s about spotting patterns and coaching teams toward better practices.
It’s also worth noting that Worklytics isn’t competing with Atlassian’s native features – rather, it complements them. Atlassian provides the basic counts for Atlassian tools; Worklytics adds additional context (including other tools and benchmarks) and advanced analytics capabilities. And importantly, Worklytics covers scenarios that Atlassian doesn’t – for example, analyzing collaboration patterns, focus time versus meeting time, burnout signals, and so on, in addition to tool usage. So by introducing Worklytics into your toolkit, you’re equipping your organization to get the most out of Atlassian AI and the rest of your digital workplace, all while maintaining ethical data practices.
