Although Artificial Intelligence (AI) is a technological advancement, we should think of it in terms of a tool for us human beings to use to our advantage. But if this is the case, why are some companies afraid of using these new technologies to their advantage?

There are many factors, including company culture, business maturity, talent gap, change resistance, and more. We won’t be diving further into these topics, as this blog post will focus more on how you can keep it simple, start small, and hopefully reap the benefits of Atlassian Intelligence.

Looking for a TL;DR? Scroll down to the bottom for key takeaways that will hopefully bring you back up again to read the rest.

Things to keep in mind with Atlassian Intelligence

Emerging technologies like AI are most likely avoided because they:

  • Lack in-depth knowledge about the technology.
  • Lack domain knowledge at their own instance (e.g., they’re not sure where to begin).
  • Over-complicate ideas on how to implement it.
  • Have no clear vision of the benefits.
  • Are skeptical and experience too much hype (e.g., “AI is our savior” kind of discussions).
  • Fear costs and “losing control”.

But the good news is it doesn’t need to be like this. In this blog post, I will elaborate further on ways to adopt Atlassian Intelligence to your benefit. Whether you’re in a decision-making role or at the management level, service desk, or development team, you should always ask yourself if you should activate Atlassian Intelligence for your organization.

If you enjoy using automations today or are experiencing daily frustrations like the examples below, then you’re most likely in the right place to give it a try.

  • Repetitive tasks/replies involving Service desk ‘customers
  • Too much context switching (usually most frustrating for software developers) 
  • Not enough time to pile through larger texts or documentation (e.g., Confluence)
  • Many employees feeling JQL (Jira Query Language) is too complicated.

What is Atlassian Intelligence?

Before we move on, we need to understand that this AI isn’t just an empty shell being added into the Atlassian suite out of nowhere. Atlassian Intelligence uses Generative AI technology from OpenAI combined with the power and data from the Atlassian platform.

As Sherif Mansour (Product Manager for Atlassian and Head of Product for Atlassian Intelligence) states:

“With more than 20 years of data reflecting how millions of software, IT, and business teams plan, track, and deliver work, Atlassian Intelligence has a unique understanding of teamwork. This knowledge underpins the teamwork graph, which is modeled on the two most common types of teamwork:

  • Service-based work: teams accepting incoming requests for help and using custom workflows and data to drive resolutions for employees and customers.
  • Project-based work: teams managing projects from concept to delivery with roadmaps, plans, tasks, goals, and documentation.”

Read more at the Atlassian Intelligence Introduction April 2023.

Realize the strength in this. Atlassian is working hard on their mission to “Unleash the potential of every team”. I’m not telling you to “build castles in the sky”, but let’s work with what we have and see where we end up.

Current and upcoming features of Atlassian Intelligence

Available now:

Generate and transform content

  • Confluence
  • Jira Software
  • Jira Service Management
  • Jira Work Management
  • Bitbucket (Beta)
  • Trello (Beta)

Summarize content:

  • Jira Service Management
  • Confluence

Create automations

  • Confluence

Define terms

  • Confluence (Beta)

Search issues

  • Jira Software
  • Jira Work Management

Generate SQL queries

  • Atlassian Analytics

Search answers

  • Confluence (Beta)
  • Compass

Automate support interactions

  • Jira Service Management

Suggest request types

  • Jira Service Management

Coming soon:

Summarize content:

  • Jira Software
  • Jira Work Management
  • Confluence Mobile

Create automations

  • Jira

Summarize smartlinks

  • Confluence
  • Jira Software
  • Jira Service Management
  • 3rd party apps (e.g., Google Docs, MS Office, and Slack)

Review code

  • Bitbucket

Source: Explore Atlassian Intelligence features

What are the pros and cons of Atlassian Intelligence? 

This is a common question that comes up at an early stage when looking into new solutions. It’s a valid one, of course, but I would advise organizations to focus more internally on Atlassian Intelligence and think more in terms of where it can save them time from daunting repetitive tasks or reduce context switching in order to boost productivity.

Let’s start with summarizing some of the pros and cons of Atlassian Intelligence connected to possible scenarios you can relate to.


  • Boost productivity on an individual level
    Example: Use Atlassian Intelligence to summarize detailed reports, which will enable quicker understanding and preparation for meetings. (Read more about the Dominos Pizza scenario).
  • Reduced context switching & simplified Developer support
    Example: Atlassian Intelligence can provide AI-powered assistance in Slack. With the use of a virtual agent, you can reduce context-switching, helping developers get support without leaving their preferred tools. (Read more about the OVO Energy scenario)
  • Productive content creation
    Example: Atlassian Intelligence enables users to draft business-critical content rapidly within Confluence, boosting the speed and efficiency of content creation.
  • AI-powered summaries
    Example: Users can quickly get up to speed on any topic in Confluence by using AI-generated summaries–assisting in faster information consumption and decision-making.
  • Natural language automation (coming soon in Jira Software)
    Example: Natural language automation in Confluence enables users to automate actions quickly using plain natural language, which makes task automation more user-friendly.
  • Flawless access to applied knowledge
    Example: Atlassian Intelligence enables natural language to JQL in Jira Software, allowing users to find issues and dependencies easier and quicker. This provides an excellent approach to extracting applied knowledge and insights from project-related data.
  • Q&A search for deeper understanding (currently in Beta)
    Example: In Confluence, Q&A search allows users to move beyond simple search results and find applicable information by asking questions about the status of projects, workflows, policies, or processes.
  • Reduced workload with Virtual Agents
    Example: Virtual agents in Jira Service Management act as an AI-powered virtual teammate that responds to help requests on Slack. This feature helps to reduce human workload of teams while maintaining service quality.
  • Request type suggestions for admins
    Example: AI assists Jira Service Management admins in creating request types with just the input of a few words. This can simplify the setup of service projects for various needs.
  • Enhanced code and developer experience (coming soon)
    The upcoming Bitbucket code review feature will automate the review of pull requests differentials, providing suggested changes and allowing human reviewers more time to focus on critical changes. This speeds up code reviews and saves time.


  • Learning phase
    Just like with any other new technology, many users may experience a learning curve while adapting or implementing the new AI features–especially parts involving natural language interfaces and automation.
  • Potential dependency on AI
    There is always the risk of teams becoming overly dependent on AI for certain tasks, which usually occurs if the AI encounters issues or users lack knowledge. This can mostly be avoided if users are well-proficient in traditional methods (which we can see today to some extent when it comes to automations).
  • Data and security concerns
    Implementing AI involves processing organizational data, which requires careful consideration of data privacy and security to address potential concerns.
  • Limited availability in Atlassian editions
    Atlassian intelligence is currently only available in the Premium and Enterprise editions of Atlassian’s cloud products.
  • Continuous improvement required
    As with any emerging or evolving technology, users may find that the capabilities of Atlassian Intelligence require continuous improvement over time to address feedback, enhance accuracy, and adapt to evolving user needs.
  • Resource allocation
    Organizations may need to allocate resources for training users, administrators, and support teams on how to effectively use and troubleshoot AI features. Having dedicated users with more in-depth knowledge about the Atlassian suite is ideal here. 

Simplicity leads to clarity

One recurring mistake when organizations are looking to adopt new technology is that they very often overthink it, overcomplicate it, or fear the worst.

Just keep it simple.

The only thing you should be aware of is that we’re in an AI hype cycle, and many of the innovations deserve some attention. We need to be aware of the limitations and risks of these systems.

Depending on what level of maturity your organization has in the Atlassian suite or for AI, you might want to ensure you have transparent and open communication between the users who might benefit from Atlassian Intelligence. This could involve key stakeholders like Jira administrators, management, developers, service desk teams, or any other team using Atlassian products (Jira Software, Jira Service Management, Confluence, Bitbucket, etc).

Once the discussion begins, the organization should start identifying where users are experiencing frustrations. This is where we keep it simple, focus on identifying and picking out a high-priority frustration within the organization, which will let you know which product to focus on.

This is why we start small, and once you have one product to start with, that is more than enough to get started. A possible scenario is the Confluence example, where users are on a tight schedule and finding it hard to plow through large documents before important meetings.
AI assistants can summarize data in order for the users to get up to speed before important meetings.

One recommendation given is that key users or administrators should have enough product knowledge before implementing AI. This, unfortunately, isn’t always the case, and Atlassian consultants like myself usually step in to help the organization for the long term.
Once you have your AI assistant up and running after a possible pilot period, users will most likely start reaping the benefits and be more productive in their daily tasks. It’s also worth mentioning that Atlassian intelligence does not require activation of all the products at once, and these can be removed at any time. See the picture below. 


To AI or not to AI? (and the key takeaways)

It all comes down to perspective. AI should be seen as a technology like any other, and we use technology to accomplish tasks. It’s easy to forget that technology should serve humans as an extension of our abilities and that we’re always in control.

Early adoption of these innovations will lead to significant competitive advantage and ease the problems associated with utilizing AI models within business processes.” - Afraz Jaffri, Director Analyst at Gartner.

Here are some key takeaways you should consider:

  • Get started
    • What are you waiting for? Get started, and in the worst case, you will remove it and try again in the near future.
  • Communication
    • Open up the internal discussions regarding AI between key stakeholders.
    • Do you have the necessary domain knowledge about the products you want AI to be involved in?
  • Transparency
    • Keep the doors open and stay transparent during communications. 
    • What does it mean, and how is your data affected?
  • Keep it simple
    • What direct impact and concrete improvement can AI make your users/teams in their daily tasks?
      That’s good enough, don’t overcomplicate it! Remember, we’re not building sandcastles.
  • Keep it clear
    • What needs to be done, and who is involved? 
    • With simplicity comes clarity (Can you hear your inner Zen monk by now?).
  • Start small
    • Identifying one prioritized challenge to solve leads to one product to activate AI in, which should be enough to get you going.
  • Set up an AI strategy for your Atlassian Suite or in general.
    • If you succeeded with the above (transparency, keeping it simple, and clarity), then you have the foundation for a decent strategy.
  • Focus on your mission
    • By taking advantage of Atlassian Intelligence, your users or teams can hopefully free themselves from time-consuming tasks and be more productive, focusing on the company mission.

Remember: Keep it simple! 

If you would like to read more about Atlassian Intelligence, you can read the latest article from 11/12/2023 by Atlassian here.

Published: Dec 21, 2023

Software developmentAtlassianCloud native