It seems like every tool we use is getting an AI upgrade these days, and our project management platforms are no different. AI is popping up everywhere, promising to handle the tedious tasks that slow us down. Atlassian has, of course, jumped in with Atlassian Intelligence, a set of features baked into its cloud products. One of its most interesting tricks is the ability to create automation rules using simple, natural language.
Instead of fiddling with complex menus and dropdowns, you can just tell Jira or Confluence what you want to happen, and it builds the workflow for you. Sounds pretty great, right? But how well does it actually work in the real world? This guide will give you a practical look at Atlassian Intelligence AI Automation Rules, covering what they can do, where they fall short, and how they stack up against more specialized AI automation platforms.
What are Atlassian Intelligence AI Automation Rules?
So, what's the big idea here? Think of Atlassian Intelligence AI Automation Rules as simple "if this, then that" workflows that you create just by writing a sentence. You describe the rule you want in plain English, and the AI translates your description into a functional automation that you can review and enable. This feature is integrated directly into Atlassian’s Cloud suite, so you’ll find it in products like Jira, Jira Service Management, and Confluence.
The main goal is to make automation more accessible to everyone, not just the tech-savvy folks. You don't need to be a Jira admin or a power user to get started. If you can describe a process in a sentence, you can probably automate it. It’s a handy tool for teams looking to streamline repetitive tasks without a steep learning curve.
Key features and uses for Atlassian Intelligence AI Automation Rules
What can you actually do with these AI-powered rules? Let's break down some common scenarios you might run into across the Atlassian ecosystem.
Atlassian Intelligence AI Automation Rules in Jira and Jira Service Management
In any project management or support environment, shaving off a few manual steps from every ticket can save a surprising amount of time over a week or a month.
Automatically sorting and routing tickets
Getting a new ticket to the right person can feel like half the battle. You can set up rules to handle this automatically based on what the ticket says. For example, you could type, "When a high-priority bug is created, assign it to the on-call engineer." The AI will build a rule that triggers whenever a new issue with the "Bug" type and "High" priority appears, then assigns it to the right person without anyone having to lift a finger.
Keeping an eye on SLAs
To help your team stay on top of service level agreements, you can create a rule like, "When an issue breaches the 'Time to Resolution' SLA, change its status to Escalated." This makes sure that critical tickets get the attention they need immediately, rather than waiting for someone to manually check the queue.
Helping agents work faster
While not a direct automation rule in the same "if/then" sense, Atlassian Intelligence also has features to help agents get through their work more quickly. It can summarize long, complicated ticket threads into a few key points or even suggest ways to tweak the tone of a customer response to sound more empathetic. It’s all part of the same toolkit designed to make the daily grind a bit smoother.
It's worth pointing out a key limitation here: these automations mostly work with standard system fields. Atlassian has mentioned that natural language prompts don't support custom fields yet, which might be a dealbreaker if your workflows rely heavily on custom data you've set up.
Atlassian Intelligence AI Automation Rules in Confluence
For teams that practically live in Confluence, AI automation can help manage the constant flow of information and keep documentation from getting out of hand.
Managing your content
You can keep your knowledge base from becoming a digital junkyard by setting up a rule to "archive pages that haven't been updated in six months and email the author." This is a simple way to prevent your spaces from getting cluttered with outdated information that might confuse people.
Sharing knowledge automatically
You can also automate communication for important updates. For instance, you could create a rule like, "When a new page with the 'Meeting Notes' template is published, generate a summary and email it to the project leads." This keeps everyone in the loop without adding another manual task to someone's plate.
Connecting docs to tasks
Turn your documentation directly into an action plan. A simple prompt like, "find all unchecked action items on this page and create a new task for each in the 'PROJ' Jira project," can bridge the gap between planning in Confluence and doing the work in Jira.
These features are genuinely useful for teams that are fully bought into the Atlassian suite. However, their value drops off if your company's knowledge is spread across other platforms like Google Docs or Notion, as the AI can't see or act on any of that external information.
Where Atlassian Intelligence AI Automation Rules fall short
While the natural language interface is a great starting point, digging a little deeper reveals some practical limitations that might become roadblocks for teams with more complex needs.
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Limited actions and triggers: The AI can't generate rules for some of the most powerful automation components. You can't use it to create rules that send web requests to external services, use an incoming webhook as a trigger, or even perform simple actions like deleting an issue. This makes it tough to build workflows that need to communicate with other critical business systems.
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A walled garden: Atlassian Intelligence is designed to operate exclusively within the Atlassian ecosystem. It’s like a helpful assistant who has never been allowed to leave the building. It can’t access or learn from data in your external knowledge bases, your CRM, or your e-commerce platforms. If you need an AI that can answer a question based on a Google Doc or a past conversation in Zendesk, you're out of luck.
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No way to test or forecast: You can preview the structure of a rule before you enable it, but you can't run a simulation on your historical data. This means there’s no way to know how an automation will actually perform in the real world or forecast its impact on your resolution times and costs. For high-stakes environments, deploying automations without that kind of insight is a pretty big risk.
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Not much customization: The simplicity of the natural language prompt comes at the cost of control. You can’t fine-tune the AI's personality, define a specific tone of voice, or give it custom abilities like looking up an order status in Shopify. It’s a one-size-fits-all approach that may not fit your specific brand or operational needs.
Atlassian Intelligence AI Automation Rules pricing
So, how much does this all cost? Atlassian Intelligence isn't sold as a separate product. Instead, its features are bundled into the Standard, Premium, and Enterprise plans for Atlassian Cloud products. This means that if you're already on a paid plan, you likely have access to these AI capabilities.
Here’s a look at the pricing for Jira Software as an example. Keep in mind that prices can change, so it's always a good idea to check the official Atlassian pricing page for the latest numbers.
| Plan | Price (per user/month, annual billing) | Key AI Feature Access |
|---|---|---|
| Free | $0 | No Atlassian Intelligence features |
| Standard | $6.25 | Atlassian Intelligence features included |
| Premium | $16.00 | Atlassian Intelligence features included |
| Enterprise | Contact Sales | Atlassian Intelligence features included |
A more powerful alternative to Atlassian Intelligence AI Automation Rules: Unifying knowledge with eesel AI
This is usually the point where teams start looking for something with a bit more horsepower. When the limitations of a built-in tool start to get in the way, you need a dedicated AI platform that offers more power and flexibility. This is where a solution like eesel AI comes in, designed specifically to overcome the challenges of siloed knowledge and rigid automation.
Go beyond the Atlassian ecosystem
Unlike Atlassian's native AI, eesel AI connects to all of your company's knowledge, wherever it lives. It can learn from documents in Confluence, Google Docs, and Notion, as well as past tickets from help desks like Zendesk or Jira Service Management. This gives the AI a complete picture of your business, allowing it to provide far more accurate and helpful resolutions.
This infographic shows how eesel AI unifies knowledge from various platforms, overcoming the limitations of single-ecosystem Atlassian Intelligence AI Automation Rules.Take control with a customizable workflow engine
eesel AI's AI Agent breaks free from the action limitations of built-in tools. It supports custom API calls, which means your AI can perform real-time actions like looking up order information in Shopify or updating a customer record in an external CRM. You also get full control over the AI's tone, personality, and the exact types of tickets it should handle, so it always feels like a natural extension of your team.
This image displays the eesel AI interface for setting up custom rules and guardrails, a key advantage over the less flexible Atlassian Intelligence AI Automation Rules.Test with confidence using simulation
Before you let an automation loose on live customer interactions, you need to know it actually works. eesel AI lets you run a simulation on thousands of your past support tickets in a safe environment. This gives you a clear and accurate forecast of your potential automation rate, cost savings, and overall ROI. You can build and deploy new workflows without all the guesswork.
This screenshot of the eesel AI simulation dashboard highlights a feature that Atlassian Intelligence AI Automation Rules lack, showing predicted automation performance.Get started in minutes, not months
Getting started with powerful AI shouldn't require a massive implementation project. eesel AI is a truly self-serve platform. You can connect your help desk or other tools with a single click and launch your first AI agent in just a few minutes, all without needing to book a sales call or wait for a lengthy onboarding process.
Atlassian Intelligence AI Automation Rules: Start simple or build for power?
So, what's the verdict on Atlassian Intelligence AI Automation Rules? It really depends on what you need.
If your team is already deep in the Atlassian ecosystem and you just want to automate simple, internal tasks, it's a great entry point. It lowers the barrier to entry and makes basic workflow creation accessible to everyone, which is a big win.
However, its limitations become clear once your needs grow. It's confined to the Atlassian walled garden, can't connect to external systems, and lacks a robust framework for testing and simulation. For teams that need to unify knowledge from multiple sources, perform actions in other apps, and deploy automations with confidence, a more specialized platform is the way to go. A tool like eesel AI provides the power, flexibility, and control required to truly transform your support and IT workflows from the ground up.
Ready to see what a truly connected and customizable AI agent can do? Sign up for eesel AI for free and build your first agent in minutes.
Frequently asked questions
Atlassian Intelligence AI Automation Rules allow you to create "if this, then that" workflows using simple, natural language sentences. The AI translates your description into a functional automation within Atlassian's Cloud suite, making automation accessible without complex coding.
In Jira and Jira Service Management, these rules can automatically handle the sorting and routing of tickets, monitor SLAs to escalate critical issues, and assist agents with task summaries. They streamline repetitive tasks to save time and ensure timely responses.
Yes, they can. For Confluence, you can use these rules to manage content lifecycle, such as archiving outdated pages, automating summaries and sharing of new meeting notes, or converting unchecked action items into Jira tasks.
Their limitations include restricted actions (like no web requests or incoming webhooks), a confinement to the Atlassian ecosystem, and a lack of robust testing or forecasting capabilities. They also offer limited customization options for tone or specific external integrations.
Atlassian Intelligence AI Automation Rules are not sold separately but are bundled into the paid Standard, Premium, and Enterprise plans for Atlassian Cloud products. This means if you're on a paid plan, you likely have access to these features without additional cost.
No, Atlassian Intelligence AI Automation Rules are primarily designed to operate within the Atlassian ecosystem. They cannot access or learn from data in external knowledge bases, CRMs, or other business systems, limiting their use in cross-platform workflows.
