Community resources
Community resources
Community resources
Jira Maintenance: How to prioritise Jira clean-up work in the age of AI
Jira maintenance is entering a new phase.
Whether it’s work items, automations, or configurations, the barrier to making changes is lower, and as a result, Jira instances are evolving faster than ever before.
That comes with a trade-off - the faster Jira changes, the faster complexity builds up.
In other words, whilst AI isn’t creating new problems, it may well start accelerating existing ones.
All is not lost, however. With skilful application of the same AI tools, you can beat this shift at its own game, and help bring structure and control back to your Jira best practice.
It's just about knowing how to use it and where to start!
The challenge isn’t growth, it’s control
Health checks, audits, and reporting provide visibility into things like unused fields, complex workflows, and configuration sprawl.
But, as the pace of change increases, you’re no longer dealing with a static list of issues. You’re managing a system that’s constantly evolving.
The real challenge becomes:
How do you decide what to fix first, and what actually matters?
Where prioritisation breaks down
When everything looks like a problem, prioritisation becomes reactive.
Admin teams tend to:
- fix what’s easiest
- fix what’s most visible
- fix what someone flags internally
That might work at a smaller scale, but as Jira evolves more quickly, it becomes harder to focus on the deeper changes that actually improve instance health, usability and, by extension, company performance.
How to navigate this using goals and guidance
One approach that’s becoming more important is combining goal-based tracking with AI-driven guidance.
Instead of treating all issues equally, you can:
- define what “good” looks like
- set measurable targets
- prioritise work that moves you toward those goals
Marketplace apps like Optimizer for Jira have this functionality built-in for ultimate clarity.
For example:
- reduce unused fields by 30%
- bring workflow complexity below a certain level
- reduce configuration warnings over time
This gives you structure, but structure alone often isn’t enough when you’re dealing with constant change.
How to harness AI as the proactive solution
This is where AI starts to shift from being part of the problem to part of the solution.
Rather than just generating more change, it can help interpret what’s happening and guide decisions.
With Optimizer’s actionable advice, powered by the Optimizer’s Rovo agent, the focus is on:
- identifying the highest-impact changes
- suggesting what to prioritise, in natural language
- and providing context-aware step-by-step recommendations
So instead of reviewing everything that could be improved, your Jira maintenance is guided toward what will actually move you closer to your goal.
Why this matters now
Jira isn’t getting simpler. As the pace of change increases, maintenance is becoming less about occasional clean-ups and more about ongoing control and strategy.
The teams that manage this well aren’t doing more work, they’re prioritising more effectively, tracking progress over time, and using AI to actually guide decisions, not just accelerate change.
If you’re thinking about how to approach this, we’ve put together a deeper breakdown of goal-based tracking and practical use cases here:
Or you can give it a go yourself with a free trial of Optimizer for Jira from the Atlassian Marketplace.