nav[aria-label="Primary Navigation"] { padding: 0; & ul { list-style: none; width: 100%; display: flex; flex-direction: row; justify-content: start; align-items: start; gap: 30px; padding: 0; & li { margin: 0; } & ul li { list-style: none; } } }

How to Prepare Your Atlassian Environment for AI

Gina Paciulli - XALT

Artificial Intelligence is rapidly becoming a core part of the Atlassian platform. Between Atlassian Intelligence, Rovo, and the growing use of AI-powered applications, organizations have more opportunities than ever to improve productivity, knowledge discovery, and decision-making.

However, many organizations are eager to adopt AI without first addressing the underlying challenges within their Atlassian environment.

One reality remains true:

AI is only as effective as the data, processes, and governance behind it.

Before enabling AI capabilities, organizations should evaluate whether their Jira and Confluence environments are prepared to support meaningful AI-driven outcomes.

1. Improve Data Quality

AI relies heavily on the information available within your Atlassian products.

If your environment contains:

Inconsistent issue types
Duplicate projects
Poorly written work items
Outdated Confluence pages
Inaccurate ownership information

AI-generated responses will be less accurate and less useful.

Start by reviewing:

Custom field usage
Project naming standards
Work item quality
Documentation accuracy
User and team ownership

The cleaner your data, the better the AI experience.

2. Establish Governance Standards

Many organizations spend years allowing teams to configure Jira independently.

While flexibility has benefits, it often leads to:

Hundreds of custom fields
Inconsistent workflows
Duplicate configurations
Conflicting terminology

AI performs best when information follows predictable patterns.

Organizations should establish standards for:

Work item types
Workflow design
Project templates
Naming conventions
Documentation structure

Standardization improves both user experience and AI effectiveness.

3. Review Security and Permissions

One of the most overlooked aspects of AI readiness is access control.

Before expanding AI usage, organizations should understand:

Who can access sensitive information
Which projects contain regulated data
How permissions are inherited
Whether historical content is appropriately restricted

AI can only respect security boundaries that have already been established.

If permissions are overly broad today, AI may expose information in ways that create additional governance concerns.

4. Invest in Confluence Knowledge Management

Many organizations focus primarily on Jira while neglecting Confluence.

This becomes a significant limitation when introducing AI-powered search and knowledge discovery.

Ask yourself:

Are pages current?
Is content organized logically?
Are page owners identified?
Is outdated content archived?

The value of AI-powered knowledge discovery depends on the quality of the knowledge being discovered.

5. Define Ownership

AI initiatives often fail when nobody owns the platform strategy.

Successful organizations typically have clear ownership for:

Governance decisions
Platform standards
Security reviews
AI adoption strategy
User enablement

Without ownership, AI adoption can become fragmented and inconsistent across teams.

6. Focus on Business Outcomes

The goal should not be to "implement AI."

The goal should be to solve business problems.

Examples include:

Reducing time spent searching for information
Improving service desk efficiency
Accelerating onboarding
Enhancing executive visibility
Increasing documentation quality

Organizations that focus on measurable outcomes typically see greater value from their AI investments.

Final Thoughts

AI has the potential to transform how teams work within Jira and Confluence. However, successful adoption starts long before the first AI feature is enabled.

Organizations that invest in governance, data quality, security, and knowledge management will be far better positioned to realize the full value of Atlassian's AI capabilities.

As Atlassian continues to expand AI across its platform, the question becomes less about whether organizations will adopt AI and more about whether their environments are prepared to support it.

How is your organization preparing its Atlassian environment for AI, and what challenges have you encountered along the way?

Comments

Prashanth

Hi @Gina Paciulli - XALT

A very relevant topic, especially with the amount of excitement around AI right now.

One thing we quickly realized during our own Cloud ERP discussions is that AI cannot be treated as a starting point. It has to come much later in the journey.

Our approach has been to first understand how departments actually operate today, how work flows between them, where approvals sit, who owns decisions, what information is consumed and produced, and where the current inefficiencies are.

Only after understanding those foundations does it make sense to discuss automation, Rovo, or AI-assisted workflows.

What makes AI different from many other technology initiatives is the scale at which it can amplify both good and bad practices.

If Marketing has poor data, Marketing suffers.

If HR has inconsistent processes, HR suffers.

But when AI starts operating across Marketing, Sales, HR, Finance, Compliance, and Technology, poor data quality, weak governance, inconsistent terminology, and fragmented knowledge stop being departmental problems and become enterprise-wide problems.

For us, that is probably the biggest lesson so far.

The conversation has been less about AI capabilities and more about operational maturity, standardization, ownership, and governance.

Get those right and AI becomes an accelerator.

Get those wrong and AI simply helps you make mistakes faster.

Coincidentally, I wanted to discuss the same before we started our Cloud ERP journey with AI. And posted some discussions

https://community.atlassian.com/forums/Atlassian-AI-Rovo-discussions/What-does-your-Rovo-pre-activation-permission-audit-actually/td-p/3242644

https://community.atlassian.com/forums/Jira-questions/Enterprise-wide-operational-platform-on-Jira-Cloud-Rovo/qaq-p/3239383

Received some valuable insights and today we are half way through our project, thus far test results are really good.

Gina Paciulli - XALT

@Prashanth

Thank you for the thoughtful response and for sharing your experience.

I completely agree that AI should not be the starting point. One of the biggest misconceptions I see is organizations viewing AI as a solution before fully understanding the processes and data that support the business.

Your observation about AI amplifying both good and bad practices is especially important. Historically, poor governance, inconsistent terminology, and fragmented knowledge might have remained isolated within individual departments. With enterprise-wide AI capabilities such as Rovo, those issues can quickly become organizational challenges that affect the quality and reliability of AI-generated insights across the entire company.

I also appreciate your point about operational maturity. In many of the environments I work with, the most valuable AI conversations are not about the technology itself but about ownership, standardization, knowledge management, and data quality. Those foundational elements often determine whether AI becomes a force multiplier or simply accelerates existing inefficiencies.

It's great to hear your Cloud ERP initiative is showing positive results so far. I'll definitely take a look at the discussions you linked, as permission models, governance, and knowledge accessibility are areas that deserve far more attention before organizations begin enabling AI at scale.

Out of curiosity, as you've progressed through the project, have you found that data quality, governance, or change management has been the biggest challenge to overcome?

Matthew Joslin_AppFox_

Really well-framed piece, @Gina Paciulli - XALT! And Prashanth's line that AI "helps you make mistakes faster" is exactly the risk we keep seeing.

The point that most resonates is your framing that AI is only as effective as the data, processes and governance behind it. Once you add agents like Rovo (or external agents reading your instance over MCP), that stops being a productivity question and becomes a trust question: no human intervention that would normally catch common sense slip-ups.

We ended up organising the same territory you cover into five pillars, partly so teams have a shared vocabulary for it:
- Tidy (can an agent trust your Jira structure?)
- Current (is your Confluence content actually true today?)
- Governed (can the wrong person or agent see the wrong thing?)
- Accountable (who signed off on what an agent repeats?)
- Legible (can an agent even read your diagrams and screenshots?)

It maps surprisingly closely onto your data-quality, governance, security and knowledge-management sections.

We wrote it up in an article here, along with a short interactive scorecard - 15 questions that give you a readiness score by pillar and show where to start first.

Curious which pillar people find hardest in practice. My money's on Current, since stale content rarely announces itself.

Yatish Madhav

Thanks @Gina Paciulli - XALT - this is a brilliant article.

This is very well said: "As Atlassian continues to expand AI across its platform, the question becomes less about whether organizations will adopt AI and more about whether their environments are prepared to support it." - we have not enabled AI/Rovo to date due to compliance and privacy verifications which we are in the process of but the underlying data is often misinterpreted.

It also talks to AI maturity. As @Prashanth mentioned, AI is all the hype and tools and services are using/implementing it left right and center Many organizations do not realise this due to the sheer amount of information. In our case, we were acquired in 2020 and when I took ownership of the instance, there were about 17 org admins for just under 300/400 users and I am still working on cleaning up the instance ... Yay.

I also agree that AI is not the starting point as many people are thinking it is. I notice personally and professionally, people saying "just ask AI" like it is the be all and end all. Granted, It has some truth to it, but at Enterprise level is where this playing field changes.

With things like this, it really shines the spotlight [brightly I'd say] on loopholes and things that are overlooked in processes or core business decisions that may need review since, like AI, that constantly needs evolving as well, doesn't it?

Thanks
Yatish

Welcome!

It looks like you're new here. Sign in or register to get started.

Quick Links

Find more posts tagged with

confluence

jira

enterprise

best-practice

governance

knowledge-management

atlassian-intelligence

rovo