Save knowledge from chaos with Atlassian AI
The problem
Valuable information is often stored in different formats and repositories. Especially on long duration projects, finding and actioning information can become problematic: people spend a lot of time looking, they have to memorize/keep track of different formats and location and maybe contend with content written by somebody no longer available to clarify or disclose it’s there!
While content search has been available since years, earlier technology can only surface things ‘as they were left’. For each piece on information to retrieve every worker looking for it has to:
- Possess a decent knowledge of search techniques
- Have some kind of awareness on what to look for (i.e.: author, key words)
- Break the work flow to dive on a different context before being able to progress
- Contend with outdated repositories, unintuitive navigation trees, unfamiliar formats
The solution
Atlassian new Ai tools adds natural language processing ‘where it matters’, lowering the effort and challenges to access information.
The are two tiers of AI:
- Atlassian intelligence – additional service inside Jira, Confluence and Atlassian products
- Rovo – Standalone product on top of Atlassian AI able to work with external repositories (SharePoint, Google drive, etc.)
The result of applying one or both layers are different, with incremental benefits
The improvement – Atlassian Ai
By adding and Atlassian AI to Premium and Enterprise licence tier, Atlassian has improved several ‘traditional’ features, basically everywhere natural language processing can play a role in an action.
Among the new features, search has received an upgrade both in Jira and Confluence.
In Jira, it’s possible to activate ‘Ai Queries’ directly in the search box, and directly input what is requested in natural language. Of course this is not equal to asking your favourite JQL ninja, but lots of most common queries can be covered quite well!
Where Confluence is involved, instead, the power of answers can skyrocket:
A dedicated Ai button present in every Confluence editor can retrieve and manipulate information as the users type in their work!
In JSM support portals, this combines in the most impactful effect: Ai agents can feed from a knowledge base in Confluence and package the results for the portals customers looking for support.
As portals answers are usually among the most time-consuming points of a support workflow, any gain in this point can pile up in big savings!
The big boost – Rovo
Rovo is an independently licensed product designed to work on top of the base Atlassian intelligence.
Among its features, it can add a lot regarding knowledge search:
- Connect to any Jira/confluence source and scout their elements as ‘atoms’ of information
- Interact with users in a chat format
- Elaborate and repackage information
- Connect to external repositories to integrate significantly more data: Google Drive, SharePoint, Slack, GitHub, Microsoft Teams, Dropbox, Notion, Box, Asana, Outlook Mail, Smartsheet and custom websites.
These additions further amplify the impact over knowledge-related workflows.
The ability of Rovo to tap into various repositories is a real game changer for collaborative work, especially when it comes to breaking down silos that can pop up between different departments. This feature paves the way for a more integrated approach to sharing information and working together, helping to foster a culture of openness and teamwork that can really boost productivity and spark innovation.
On top of that, Rovo’s chat format is refreshingly straightforward and direct compared to other communication methods. This is a big deal, as it helps speed up decision-making and lightens the cognitive load for everyone involved. While it might seem like a small tweak, those few saved minutes here and there can really add up, leading to significant time savings over hours and days, and ultimately enhancing the team’s overall efficiency.
Moreover, the bubble feature in Rovo is a fantastic addition, making it easier for users to find information quickly while also encouraging the reuse of valuable data. This means that knowledge isn’t lost or overlooked, allowing teams to build on what they already know rather than starting from scratch every time.
Finally, the capabilities Rovo offers can serve as a solid foundation for future developments and changes within the organization, like the potential introduction of Rovo agents. This possibility for growth underscores Rovo’s role not just as a tool for immediate improvements but as a catalyst for long-term transformation in how teams collaborate and share information.
The benefits
Incorporating natural language processing into knowledge management workflows can trim costs at every interaction point. This exciting development not only smooths out processes but also boosts efficiency across a variety of tasks. Plus, artificial intelligence has this fantastic knack for ‘salvaging’ significant value from older information repositories, which is a game changer for long-term projects and programs. By tapping into AI, organizations can uncover hidden insights and make the most of their historical data.
On top of that, AI is key in tackling people-turnaround issues that pop up due to the different storage habits and format variations within an organization. By helping to discover data across these diverse systems, AI can pave the way for a more cohesive and integrated approach to data management. This is particularly crucial for breaking down the silos that often exist between departments, like Development/IT and Business sectors, which have traditionally relied on different tools and methodologies.
Moreover, weaving AI into knowledge workflows not only addresses these challenges but also frees up valuable energy within teams. This newfound ability nurtures a culture of knowledge sharing, promoting collaboration and innovation throughout the organization. As a result, teams are empowered to work more effectively together, leading to better outcomes and a more vibrant work environment.
Not a magic bullet – what’s needed to succeed
The new Atlassian tools will speed up extremely some of the aspects but won’t remove other key aspect of a successful work setup.
To reap the benefits two key actions are still needed: data access due diligence, and content creation.
Due diligence on data access approach
The highest barrier to AI adoption (costs aside) is the fact that to work, the Ai system needs to access and process all the data subject to search. For the results to be useful, the access must be broad and comprehensive, so doors must be opened and permission restriction kept to the minimum needed – broad ‘lock everything just in case’ will kill the adoption.
Atlassian has prepared clear information on how they access and process data, and they have balanced the need of access with privacy and security concern. You can also use a partner like knowmad mood to ‘implement things properly’.
Still, the general approach and the founding philosophy of the tool, as all the Atlassian platform design, remain ‘Open Team access and sharing’. Please confront your work culture with this when considering adoption, as it will be an important success/friction point.
Writing content
Given Atlassian Ai supports content generation, good, valuable knowledge is still in the hand of workers. The solution described streamlines and remove access costs to information but will not create information that is impactful by itself.
To thrive with this new tool, it’s critical to invest time in creating good content, having workers to write down procedures, schematics, ideas, processes and anything else that will enhance collaboration.
If you’re interested in this solution, and much more around Atlassian and Ai, get in touc h with us! Our consultant will be at your disposal to help!
Author: Roberto Bacchini