When to Use Rovo, and When Not To
by @Denis Boisvert (Trundl) & @Dave Rosenlund _Trundl_
“When should we use Rovo, and when should we use ChatGPT? Our company has both.”
That’s just one example of a question we’re hearing more and more—from project managers, system administrators, product owners, and the occasional executive who’s trying to make sense of a rapidly expanding AI toolbox.
It’s a reasonable question. And it’s a timely one.
AI tools are evolving at a pace most organizations haven’t experienced before. Today it’s Rovo. Yesterday it was ChatGPT. Tomorrow it might be the latest Gemini release that’s suddenly “better than everything else.” Whatever your company is using right now, chances are that picture will look different in six months.
Atlassian is betting heavily on Rovo, positioning it as a cross-application AI layer embedded directly into the Atlassian Cloud platform. At the same time, general-purpose tools like ChatGPT, Claude, and Microsoft Copilot are spreading rapidly across organizations—powerful, flexible, and sometimes frustratingly inconsistent.
So how do you decide which tool makes sense, and when?
This article isn’t a marketing recap. It’s an attempt to provide a practical, peer-to-peer perspective based on what we’ve seen in real Atlassian customer environments. We’re less interested in the Rovo promises, and more interested in what it reliably delivers today—and where it still falls short.
Getting Past the First Barrier: Security and Trust
Before comparing use cases, we need to address an obvious blocker: trust.
In our previous article, AI Privacy & Security: Rovo Adoption Should Start with Trust we made the case that AI adoption inside Atlassian environments can’t move forward unless your team feels confident in how data is handled, protected, and stored. That hasn’t changed.
If your legal, security, or compliance teams haven’t reviewed the implications of Rovo—or any AI tool in your stack—this discussion is premature. Trust isn’t a feature. It’s the foundation. Everything else depends on it.
If your organization has cleared the AI trust hurdle with Rovo, or if you anticipate that happening soon, read on.
The Reality of Rovo: What It’s Good At (Today)
Once your teams have access to Rovo, curiosity kicks in. They open it and ask the obvious question: “What can this actually do?”
This is where expectations start to meet reality. What we have seen is, in practice, Rovo performs best when the task is:
-
repetitive,
-
clearly defined,
-
and grounded in existing Atlassian data and workflows.
Think Jira work item classification, Confluence page summaries, follow-up task creation, or surfacing relevant content. These are well-bounded problems, and Rovo handles them competently.
That doesn’t mean Rovo can’t do more. It means it’s most reliable when you stay within clear lanes. Push it into ambiguity or loosely defined work, and results become inconsistent. When used thoughtfully, though, it can be a genuine accelerator. We’ve seen teams use it to draft internal updates, triage support requests, and summarize customer conversations—without leaving the Atlassian ecosystem.
One underrated benefit is reduced context switching. Instead of bouncing between Jira, Confluence, Slack, and other external tools, Rovo helps surface information where the work already happens. That kind of friction reduction adds up quickly.
Another area where Rovo shows real promise is enterprise search.
Because Rovo is built on Atlassian’s Teamwork Graph and supports multiple connectors (see Unlocking the Power of Teamwork Graph), it can act as a unified entry point into your organization’s data—across Jira, Confluence, and connected systems. In environments where knowledge is fragmented and spread across tools, this can be genuinely useful.
That said, its effectiveness still depends heavily on data quality, permission models, and how well your data is structured. Rovo won’t magically fix poor information hygiene—but when the foundations are solid, it can significantly reduce the time spent hunting for context.
What Rovo doesn’t yet do is behave like a true co-pilot. It doesn’t learn your preferences, adapt to how your team works, or understand the “why” behind decisions. It helps you move faster—but only when the path is already well paved.
Here are a few examples of the sorts of things you can do today:
-
Summarizing Confluence content — Rovo agents have been used to generate quick summaries of pages or help proofread and enhance documentation directly inside Confluence. (See 3 Rovo Agents That Make Your Life Easier, by Atlassian Community Champion, Steffen Burzlaff)
-
Assisting with Jira work item handling — Automation templates demonstrate Rovo agents analyzing issue details, prompting authors for missing fields, or categorizing issues on creation. (See Discover the Power of Rovo Agents with New Jira Automation Templates, by Atlassian Product Manager, Camilo Gomez)
-
Speeding up triage and classification — Rovo can automatically apply labels, assign issues, or kick off follow-up actions based on patterns in incoming requests. (See Harnessing Rovo in Jira: A Practical Guide for Atlassian Admins, by Atlassian Community Rising Star, Rinjini Poddar)
Where Rovo (and AI in General) Falls Short
Rovo, like all AI, makes mistakes.
Despite the excitement around “agentic workflows,” we haven’t seen many teams successfully replace complex, end-to-end processes with Rovo. That’s not a failure of configuration or ambition—it’s a reflection of where enterprise AI is today.
When something breaks in a workflow—a missing field, unexpected input, or permission issue—Rovo doesn’t troubleshoot or recover. It fails quietly. And while Rovo agents are often quicker to set up than traditional automation rules, they can still be brittle. If you’re expecting adaptive behavior or self-healing logic, you’ll be disappointed.
More broadly, AI still struggles with nuance. It can summarize content, but miss tone. It can suggest next steps, but not understand how your team actually makes decisions. Wherever human judgment or situational context matter, Rovo needs support.
Which brings us to a related point of confusion.
GenAI Agents vs. Agentic AI: Clearing Up the Terminology
The word “agent” gets used loosely—and not always helpfully.
The agents in AI tools like ChatGPT and Claude are best thought of as GenAI agents. They're prompt-driven assistants. You ask a question, they respond. They don’t act unless you explicitly ask them to.
Rovo agents are different. They’re closer to task-triggered assistants. When something happens in Jira or Confluence, they can take predefined actions—update fields, notify teams, create pages, or kick off follow-up work.
Today’s Rovo agents are more like configurable macros than independent coworkers. They execute scripts. They don’t reason, adapt, or debug.
True agentic AI—systems that can operate with meaningful independence—is still a vision rather than a reality. Think of Rovo agents as a step in the right direction. But, they're not agentic. Yet.
Choosing the Right Tool for the Right Use Case
We’re not here to crown a winner. We’re here to share what works.
If the work lives inside Jira or Confluence, follows a pattern, and benefits from less manual effort, Rovo is a strong candidate.
If the work involves brainstorming, drafting, analysis, or navigating ambiguity, general-purpose tools like ChatGPT, Claude, or Copilot usually perform better.
In practice, the most effective teams blend tools:
- explore and draft externally,
- refine and validate,
- then use Rovo to operationalize the result inside Atlassian.
AI works best as a toolbox, not a silver bullet.
Where Rovo Is Likely Headed (and What Actually Matters)
Rovo is evolving quickly. Atlassian is adding integrations, expanding the Teamwork Graph, and introducing new skills and agent capabilities at a steady pace. That momentum is real. But the more important question isn’t what features come next—it’s what kind of system Rovo is becoming.
From what we’re seeing and hearing, several themes are emerging. For example, one admin told us their team spent two weeks trying to build a ‘smart triage’ agent—only to abandon it because the logic broke every time permissions changed. Another told us, We tried to get Rovo to handle handoffs across time zones. It mostly worked—until it didn’t.”
From Task Execution to Context Awareness
Today, Rovo agents are good at executing well-defined actions. Tomorrow, their value will depend on how well they understand context.
That means more than recognizing fields or triggers. It means understanding project goals, historical decisions, team norms, and why work is happening in the first place. Without that context, agents remain fast—but shallow. With it, they could start to feel genuinely helpful—not just efficient.
Enterprise Search as a Strategic Capability
With the Teamwork Graph and an expanding set of connectors, Rovo is increasingly positioned as an enterprise search layer—not just for documents, but for work in motion.
In theory, this could make Rovo a trusted entry point for finding knowledge across Jira, Confluence, and connected systems, without pushing sensitive context into external GenAI tools. In practice, its success will depend on fundamentals: data quality, permission models, and how well organizations structure their information.
Rovo won’t fix poor knowledge hygiene. But in environments where those foundations are solid, it has the potential to significantly reduce time spent searching for context.
More Skills, More Agents — More Governance Needed
As Rovo gains skills and integrations, complexity will increase. That’s unavoidable.
The risk isn’t that agents become more powerful—it’s that they become harder to reason about. Without clear governance, teams could end up with dozens of narrowly scoped agents that are difficult to audit, debug, or maintain.
This raises practical questions for admins and platform owners:
-
How do you prevent agent sprawl?
-
How do you version, test, and retire agents safely?
-
How do you understand why an agent acted the way it did?
These are the same challenges automation faced—just with higher stakes.
Observability and Explainability Will Define Trust
At enterprise scale, capability alone isn’t enough. AI systems need to be observable.
Teams will increasingly ask:
-
What data did this agent use?
-
Why did it take this action?
-
What changed as a result?
Until those questions are easy to answer, trust will remain fragile. AI adoption doesn’t stall because tools aren’t powerful—it stalls because they’re opaque.
The Human Side of Agent Adoption
Finally, there’s the human factor.
As agents take on more work, teams will need to learn how to delegate without over-trusting, how to intervene without micromanaging, and how to keep AI from becoming just another hidden layer of complexity.
That shift won’t be technical—it will be cultural. And it may turn out to be the hardest part.
A Balanced Outlook
Rovo’s future isn’t just about more integrations or smarter agents—it’s about balance.
If Atlassian can pair increasing capability with transparency, control, and thoughtful governance, Rovo could evolve into a genuine coordination layer for enterprise work. If not, it risks becoming another powerful tool that teams use cautiously, selectively, and never quite fully trust.
Model Flexibility: Why Rovo’s LLM-Agnostic Approach Matters
One aspect of Rovo that doesn’t get discussed enough is its underlying model strategy.
Rather than betting everything on a single large language model, Atlassian is taking a multi-model approach. Depending on the use case, Rovo can leverage different LLMs—including models from OpenAI, Google, and potentially others as the landscape evolves.
That matters, because the “best” model changes quickly. Today one model excels at reasoning, tomorrow another leads in summarization or multilingual support. By abstracting those choices away from end users, Atlassian gives itself room to adapt without forcing customers to constantly rethink their AI strategy.
From an enterprise perspective, this is less about chasing performance benchmarks and more about resilience. It reduces dependency on a single vendor, allows Atlassian to match models to specific workloads, and helps future-proof Rovo as the LLM ecosystem continues to shift.
Of course, this flexibility also introduces new questions—around transparency, predictability, and data handling. Teams may want to understand which models are being used for which tasks, and under what conditions those choices change.
Still, as AI capabilities continue to leapfrog one another, a model-agnostic architecture may turn out to be one of Rovo’s most practical long-term strengths.
Final Thoughts: Curiosity Is Good. Caution Is Smarter.
We’re still early in enterprise AI adoption. The hype is loud. The results are uneven.
That’s not a reason to avoid tools like Rovo. It’s a reason to use them deliberately.
Start with trust. Experiment with intent. Share what works—and be honest about what doesn’t. The real strength of the Atlassian Community isn’t polished success stories, it’s candid conversations about what happens in the messy middle.
Denis and Dave are Atlassian Community Champions with experience on both the customer and partner sides of the Atlassian ecosystem. While neither claims to be an AI expert, both are deep in learning mode. That’s why this article was peer-reviewed by multiple Rovo explorers.
This is the second in a series of Rovo explorations. Feedback, questions, topic suggestions, and even pushback, are not only welcome—they’re encouraged.
Was this helpful?
Thanks!
Dave Rosenlund _Trundl_
About this author
Global Director, Products @Trundl
Boston
203 accepted answers
8 comments