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Atlassian releases AI capabilities for Jira and Confluence

The new capabilities show the rising prominence of software development copilots and lay the foundation for better knowledge management in an organization.

Atlassian on Monday released generative AI capabilities within Atlassian AI, its generative AI assistant, to improve collaboration and productivity among software developers.

The new tools include Generative AI in Editor, which speeds up individual productivity in various Atlassian platforms. It can help form an idea for the first step for a test plan in Confluence, alter the tone of a response to a customer in Jira Service Management or make user stories in Jira Software tickets.

More new Atlassian AI individual productivity capabilities include AI-powered summaries, which quickly summarize content in Confluence and incidents in Jira Service Management, and Natural Language Automation, which lets users automate actions in Confluence by typing instructions in natural language.

Atlassian also released in beta Q&A search in Confluence, which lets users inquire about project statuses, workflows, policies and processes, as well as Q&A search in Compass, which enables users to ask about components and deployments of the technology stack in natural language.

The rise of TuringBots

These capabilities are akin to multiple types of TuringBots, which are generative AI copilots for software development. Forrester Research coined the term TuringBot, so named for computer science forefather Alan Turing. A TuringBot performs or assists in performing tasks in the software development lifecycle: analysis, design, coding, testing and delivery.

"Atlassian has technology that cuts across many of these stages," Forrester analyst Diego Lo Giudice said.

Using GenAI for coding

The part of the software development lifecycle that is most crowded with offerings similar to Atlassian's is coding, according to Giudice.

While GitHub Copilot is more for the actual code generation, we're starting to see now that Jira has some of these capabilities as well for their own platform.
Will McKeon-WhiteAnalyst, Forrester Research

Similar coding platforms include GitHub Copilot, a collaboration between GitHub and OpenAI.

Atlassian also unveiled Natural Language to JQL, which lets users discover issues and their dependencies using natural language in Jira Software and Jira Work Management, as well as Natural Language to SQL, which lets team members better understand insights in Atlassian Analytics using natural language.

It might be just a matter of time until Atlassian infuses more coding abilities into its AI, especially with the release of Natural Language to SQL, which is a standard language for developers, Forrester analyst Will McKeon-White said.

"While GitHub Copilot is more for the actual code generation, we're starting to see now that Jira has some of these capabilities as well for their own platform. So it's going to be mostly the Jira developer ecosystem that this is initially built out to," McKeon-White said. "But I don't see why this won't be expanded in the future."

By integrating Atlassian AI capabilities across multiple tools, the vendor provides a way for organizations to extract deeper insights from their data, "so they can make effective decisions across their company," said Sherif Mansour, Atlassian Intelligence head of product.

"Previously, it would take them hours to mine this data, depending on what it is, and make sense of it all -- AI just compresses that time significantly," he said.

Groundwork for better information management

Atlassian also unveiled another Atlassian AI capability called AI definitions, which defines words and acronyms that are particular to an organization, for internal use. This capability is now available in beta for Confluence and is coming to Jira Software and Jira Service Management in the future.

This definition capability is a key step forward to standardize materials and clarify concepts shared throughout an organization, McKeon-White said.

"Language is very, very hard," he said. "One of the things that has always struck me is just how variable and nonstandard language is when used in organizations, even if it's a term or an acronym."

The capability is also a good example of how generative AI can be used to better organize and share company information. Overall, smarter information management tools will be helpful to companies, especially since useful language models require accurate, up-to-date training materials, according to McKeon-White.

But organizations have "traditionally undervalued information management," he said.

As a result, many organizations have accumulated thousands of various sources of information. Even companies that have well-managed knowledge bases often have some portion that is unaccounted for, whether it's sitting in emails, PowerPoints or Word documents, McKeon-White noted.

Capabilities such as Atlassian's AI definitions highlight how generative AI can be used to retrieve information and classify it, which will be a key support for centralizing knowledge and giving workers a single source of truth. It also forces organizations to consider how information will be stored and who will organize it.

"That's why what Atlassian is doing is really interesting," McKeon-White said. "They're doing this for the Atlassian ecosystem and then can expand out from there."

Atlassian AI's new capabilities will be included in Atlassian Premium and Enterprise Cloud at no additional cost.

Mary Reines is a news writer covering customer experience and unified communications for TechTarget Editorial. Before TechTarget, Reines was arts editor at the Marblehead Reporter.

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