A Day in the Life: How Atlassian's Technical Adoption Success Team Turns AI from Buzzword to Value

[#1]

Kate Judina is a Senior Technical CSM (AI) on Atlassian's Technical Adoption Success (TAS) team. In this post, she walks through the journey customers go through from "AI sounds great in theory" to measurable, production-level business value, and shares what that transformation actually looks like in practice.

Can you walk us through what a "Transform with Rovo" engagement looks like?

Every customer we work with starts in roughly the same place: Rovo is switched on, but it's not yet part of how teams work day-to-day. Our job on the Technical Adoption Success team is to bridge that gap. We are taking customers from "AI is too hard" to "I can't imagine working without this."

Here's how that journey typically looks like:

• Discover — We sit down with the customer to understand their pain points, map their workflows, and identify where Rovo and third-party connectors can make the biggest dent.

• Design — Together, we scope two or three high-impact use cases and design the right combination of agents, automations, and connectors to achieve the outcomes they need.

• Build — We then co-build the solutions hands-on in their environment, testing as we go.

• Enable — We share follow up enablement materials, Loom recordings, and documentation so the team is fully self-sufficient and can build on what we've started together.

Every engagement is very collaborative. We co-design, co-build, and co-deploy alongside the customer's own teams.

Can you share a real example of that journey?

One of our enterprise customers had a rigorous project governance framework whereby every project had to pass through multiple approval gates, from ideation through to closure. The problem was preparing a single governance document took 40–60 minutes of manual work: collating data from across Jira and Confluence, formatting presentations, and constantly context-switching. And even after all that effort, documents frequently failed their review gate because teams lacked clarity on what "good" looked like which lead to multiple rounds of rework. Multiply that across dozens of active projects and you're looking at a serious delay on delivery timelines.

To help them solve this, we co-designed a Rovo agent that acts as an automated quality gate. Before any project document goes to a governance review, this agent checks it against the organisation's approval standards, highlights gaps, and guides the project manager on exactly what needs fixing within seconds.

The results:

• Governance document prep: from 40–60 minutes down to under 5 minutes — a ~90% time saving

• Status reporting: from manual collation to under 2 minutes

• Review cycles: from multiple rounds of rework to first-time ready quality

Those are the customer use cases but how do you use Rovo and AI internally?

Honestly this is one of the things I love most about the role. We're not just telling customers to adopt AI, we're very much in the trenches ourselves.

I use Rovo constantly. When I'm preparing for a customer call, I'll use Rovo Chat to pull up everything across our internal knowledge base like previous engagement notes, agent templates from our repository and relevant product documentation. Instead of hunting through dozens of Confluence pages, I ask Rovo and get a synthesised answer with sources in seconds.

What's surprised you most about how customers react when they see what's possible?

Almost every customer comes in thinking AI is either too complicated for their team or too generic to be useful. Then we sit down, map their actual workflow, and show them an agent that does something specific and valuable.

For example, reading their Jira tickets and flagging which epics have exceeded their original estimates so project managers can forecast more accurately. And suddenly it clicks that isn’t just noise but something that that makes their day to day much easier.

The other thing that surprises people is who ends up building agents. It's not always engineers. We see HR teams building onboarding assistants, product managers creating feedback analysis agents, IT teams building self-service bots for their portals.

Rovo Studio lets you build agents with natural language instructions without any code required. Everyone builds, everyone uses.

Ready to explore what's possible?

Start with a real pain point, not just a technology test. The best AI use cases I've seen aren't the flashiest but the ones where someone says "I spend two hours every week doing this repetitive thing and I wish I didn't have to." That's your agent.

And you don't have to go it alone. If you're curious about what AI can do for your team's specific workflows, join us for one of our upcoming webinars where we walk through real examples and live demos. Or reach out to your account team to see what resources and opportunities are available to help you get started.