Enhancing Jira Product Discovery with AI-driven features There is tremendous opportunity to leverage generative AI in Jira Product Discovery (JPD) to help product teams automate and streamline their product discovery efforts. Atlassian is already working on "Atlassian Intelligence" for their Cloud products such as Jira Software and Confluence, so there is an opportunity to integrate its AI and machine learning capabilities into JPD as well. Other product discovery/management tools - like Productboard, Airfocus, & Reveall - already have AI-driven features powered by Open AI's ChatGPT APIs or other AI tech. Here are some ideas and use-cases of AI in JPD: * Intelligent idea categorization: Automatically tag, organize, and categorize ideas added to JPD based on common themes and feedback sentiment analyzed from insights, comments, and other inputs. This streamlines the initial sorting/triaging process, helping product managers prioritize what needs attention first. * Insight analysis. Use AI to analyze JPD ideas, identify top problems and opportunities (pain points, needs, desires), create concise problem and opportunity statements (e.g. using AI to rewrite the description in an idea clearly and concisely outlining all of this info), and extract valuable feedback from lengthy conversations/insights/emails/etc submitted to JPD. * Intelligent validation. Generate design experiments to validate your ideas/opportunities/solutions in JPD. * Automated roadmap prioritization. Automatically suggest prioritized product roadmaps and create a JPD view for it. Considers insights/comments/feedback/goals. * Feature demand prediction: Predict needed features and solutions to problems and opportunities added to JPD. * Automated Roadmap Prioritization: Develop an AI-driven feature that takes into account various factors such as user feedback, market trends, and goals (i.e. in Atlas) to automatically suggest a prioritized product roadmap and create a view in JPD for it. * AI chatbot in JPD: Get instant answers to product-related questions in JPD using AI. For example, explore opportunities and solutions for a desired outcome, based on data inside JPD and other integrated products such as Jira Software, Confluence, etc. (Productboard AI has a nice visual example of this). With these features, JPD can facilitate better, faster, and more efficient product discovery for product teams.

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Jira Product Discovery + Atlassian Intelligence integration: AI-driven features in JPD

David Nadri

Enhancing Jira Product Discovery with AI-driven features

There is tremendous opportunity to leverage generative AI in Jira Product Discovery (JPD) to help product teams automate and streamline their product discovery efforts.

Atlassian is already working on "Atlassian Intelligence" for their Cloud products such as Jira Software and Confluence, so there is an opportunity to integrate its AI and machine learning capabilities into JPD as well.

Other product discovery/management tools - like Productboard, Airfocus, & Reveall - already have AI-driven features powered by Open AI's ChatGPT APIs or other AI tech.

Here are some ideas and use-cases of AI in JPD:

Intelligent idea categorization: Automatically tag, organize, and categorize ideas added to JPD based on common themes and feedback sentiment analyzed from insights, comments, and other inputs. This streamlines the initial sorting/triaging process, helping product managers prioritize what needs attention first.
Insight analysis. Use AI to analyze JPD ideas, identify top problems and opportunities (pain points, needs, desires), create concise problem and opportunity statements (e.g. using AI to rewrite the description in an idea clearly and concisely outlining all of this info), and extract valuable feedback from lengthy conversations/insights/emails/etc submitted to JPD.
Intelligent validation. Generate design experiments to validate your ideas/opportunities/solutions in JPD.
Automated roadmap prioritization. Automatically suggest prioritized product roadmaps and create a JPD view for it. Considers insights/comments/feedback/goals.
Feature demand prediction: Predict needed features and solutions to problems and opportunities added to JPD.
Automated Roadmap Prioritization: Develop an AI-driven feature that takes into account various factors such as user feedback, market trends, and goals (i.e. in Atlas) to automatically suggest a prioritized product roadmap and create a view in JPD for it.
AI chatbot in JPD: Get instant answers to product-related questions in JPD using AI. For example, explore opportunities and solutions for a desired outcome, based on data inside JPD and other integrated products such as Jira Software, Confluence, etc. (Productboard AI has a nice visual example of this).

With these features, JPD can facilitate better, faster, and more efficient product discovery for product teams.

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Yong Yang

Support!

Kevin Gray_8heqo0

Can you explain whether I can utilise this service but be assured that my content is never finding its way into any algorithm whether belonging to Atlassian or a third party service.

Our Discovery boards contain lots of fragments of potential IP and Patent content, I would not want any of that being absorbed into an algorithm as it is passed through the service in order to augment my Discovery experience.

Tanguy Crusson

Thanks for the suggestion @David Nadri . We're starting to dip our toes there, starting from the basics (and building the infrastructure for it). Can't share much more at this stage, but we're definitely going to experiment with this, things like "summarize what people are asking for based on what's in the insights section".

One thing I'm personally not convinced about is anything claiming "automated prioritization". Mainly because prioritization decisions are not only based on data, there's a lot more to it than that and it's highly collaborative. But hey, I'm sure someone will prove me wrong at some point.

David Nadri

@Tanguy Crusson - awesome, great to hear the JPD team is exploring with AI! Thanks Tanguy.

I agree with you - prioritization involves more than just data, including collaboration. To clarify my point, AI could assist in automatically surfacing relevant insights and data from various inputs provided to Atlassian/JPD (e.g. Goals in Atlas, JPD ideas/insights, etc) to use in collaborative decision-making discussions.

Excited to see how JPD evolves with these enhancements.

David Nadri

@Tanguy Crusson - following up since your last comment a few months...

Any updates you can share on when we can expect to have Atlassian Intelligence features available for Jira Product Discovery?

Atlassian Intelligence is already supported for Jira Software for things like generating, transforming, and summarizing content in issues, so it would be great to also have these features in Jira Product Discovery.

cc: @Finch Grace

David Nadri

@Tanguy Crusson @Finch Grace - any updates you could share about the Atlassian Intelligence integration into JPD since the last update about 6 months ago?

Sagithya

@Tanguy Crusson, any update on this? We are eager to explore the AI functionality on JPD.

Erin Caldwell

I would love to see Atlassian generate an "Idea short description" from the overall description in the body of an idea. Productboard does this well and it's a huge time saver. We're moving to JPD and that is a feature I am really going to miss.

Tere Pile

JPD AI a premium feature, when Atlassian Intelligence is available for Jira Standard, seems like a miss by Atlassian.