The artificial intelligence (AI) landscape is evolving at a rapid pace, making it nearly impossible to keep up with every new development. While staying informed is essential, today’s C-suite executives don’t have time to sift through the endless amount of AI content—and they can’t afford to place their bets on overhyped solutions that fail to deliver.
That’s where Praecipio comes in, especially when it comes to the Atlassian ecosystem. For organizations relying on the Atlassian platform to power their operations, there are several ways to integrate AI to enhance productivity and achieve tangible business outcomes.
In this article, we explore the practical ways you can connect AI with your Atlassian tools and how you can leverage this technology to drive real results for your business.
Know Your Options for AI Integration with Atlassian
The first step in adopting AI is to cut through the hype. With so much noise in the market, it’s essential to know your options and ask the right questions before starting your AI journey. This applies across the board, whether you’re implementing AI within the Atlassian ecosystem or elsewhere in your organization.
If your business relies on the Atlassian platform to get s*&t done, here are the ways to integrate AI into your ecosystem:
- Atlassian Intelligence
- Atlassian Rovo
- Non-Atlassian AI products that consume Atlassian data
- AI beyond the Atlassian Platform
1. Atlassian Intelligence
Atlassian Intelligence is a built-in set of features using proprietary AI designed specifically for the Atlassian suite. It’s available at no additional cost for Cloud Premium and Enterprise Products. Enabling Atlassian Intelligence is straightforward–simply activate it from your Cloud Admin Panel to give users access.
Leveraging the power of large language models (LLMs), Atlassian Intelligence has several GenAI features that support:
- Coding
- Summarizing text, images, video etc.
- Sentiment analysis
- Text categorization
- Creative writing (emails, communications, marketing slogans)
- Brainstorming
- Answering questions
2. Atlassian Rovo
Atlassian unveiled Rovo earlier this year at their annual Team event. This AI-powered tool is designed to give teams enterprise intelligence by consolidating and providing visibility into data scattered across various tools, helping them make smarter decisions..
Rovo, like similar tools, focuses on delivering timely, contextually relevant answers tailored to specific business needs. This addresses a common limitation of foundational AI models, which often struggle in business contexts because they are trained exclusively on publicly available data and are rarely updated due to the high cost of training large language models (LLMs).
Think of Rovo as enterprise knowledge discovery supercharged by AI, with the added ability to take decisive actions. It’s essentially enterprise search on steroids. By connecting to and indexing multiple data sources, the tool empowers users to generate meaningful answers to complex questions. Recently launched, Rovo is available as a separate licensed product.
Garbage In, Garbage Out: Building a Strong Foundation for Data-Driven Decision-Making
All cool features aside, the success of any AI implementation depends on the quality of your data. Before connecting AI with your data infrastructure, evaluate your organization’s repositories—whether it’s Confluence, Google Drive, or SharePoint. Are these environments logically structured, and populated with up-to-date and accurate information? Or do they resemble a data dumpster fire of outdated and incorrect information?
It’s important to remember that AI systems don’t perform magic. They are as good as what you put in them, meaning they rely on high-quality data to produce valuable and accurate insights that drive better decision-making. Without a strong data foundation, even the most advanced AI tools will struggle to deliver meaningful results.
3. AI Beyond The Atlassian Platform
So, what if the goal is to expand AI capabilities to your entire organization and not just for those working with the Atlassian suite?
There are plenty of AI solutions designed to work with data from a variety of systems. These tools can operate as standalone platforms or browser extensions and can connect to any system with an API to retrieve information. Many of them already list Jira and Confluence as compatible data sources.
However, it’s worth noting that many managers and executives don’t spend much time in Jira. Instead, they typically operate within email or productivity apps like Word and Excel. For these users, having AI accessible as a browser extension makes far more sense, as it organically integrates with their ways of working.
Additionally, while tools like Rovo or Atlassian AI are excellent for teams—such as development or service teams—using Jira, Confluence, or similar platforms, they may not be the best fit for organization-wide AI deployment. To serve the broader needs of an enterprise, we recommend solutions that cater to diverse roles and workflows across the organization.
For example, leveraging existing solutions like Ask AI or Microsoft Copilot could be a smarter investment. By extending a tool you already use, rather than adopting an entirely new one, you simplify the process and make it easier to justify the cost.
4. Custom AI Solutions
In some cases, out-of-the-box SaaS solutions may not meet an organization’s unique requirements, making a custom solution the only viable choice. The primary advantage of building your own system is flexibility. You can select the most suitable LLM, whether on-premises or cloud-based, customize infrastructure and features, and tailor the data sources to your specific needs. Investing in the “private AI” approach ensures greater control and security over your data.
On the flip side, building a custom solution comes with its own set of challenges. It’s risky, involves hard work, and could potentially be expensive to maintain, especially given the volatile nature of AI technology. That said, there are alternatives to building a fully custom system.
For most organizations, leveraging solutions like this is a good middle ground compared to developing AI systems from scratch. The process of pre-training LLMs and building AI software systems is complex, resource-intensive, and often better left to the experts.
Preparing Your Organization for GenAI
You’ve probably noticed a common theme in this article: data is king and it rules over the GenAI landscape. If your data sucks, then so will your AI initiatives–and your business strategy for that matter. Here are some steps to prepare your organization for GenAI:
- Prioritize data readiness: Your organization may need to overhaul its knowledge management practices to prepare for AI adoption. If you want to align your data with your AI initiatives, register for our webinar, where you will learn how to create the reliable data foundation needed to achieve better business outcomes.
- Start small: Begin with a pilot program in a specific team or function, such as a service desk. AI solutions for service teams are often the easiest to implement and measure.
- Evaluate and iterate: Test the AI solution, track its performance, and refine your approach based on the results.
There’s a lot of noise out there. If you need guidance navigating the wild wild west of AI, Praecipio can help. Contact our team about implementing practical, AI-driven solutions that propel your business forward.
