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Artificial Intelligence in Jira Service Management

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概述

This guide is for anyone getting started with artificial intelligence (AI) features in Jira Service Management. Use this as a resource to unlock intelligent experiences across the Jira Service Management platform that can help you accelerate productivity and deliver exceptional service to employees and customers.


Get started with Atlassian Intelligence

Atlassian Intelligence is a collection of AI-powered capabilities across Atlassian cloud that helps companies and teams accelerate productivity, drive action, and unlock insights. It leverages artificial intelligence developed internally and from OpenAI.

It uses the Teamwork graph, which is unique to your teams' project or service work, along with internal language models and OpenAI to deliver results specific to the your organization’s context.

Keep an eye out for the Atlassian Intelligence icon throughout your Atlassian products to discover new AI-powered experiences

Atlassian Intelligence mark

如何在事务编辑器中使用生成式 AI:

在事务编辑器中,可以通过两种方式访问生成式 AI:

  1. 在事务编辑器工具栏中选择 Atlassian Intelligence 图标。
  2. 在事务编辑器中输入 /ai 以打开 Atlassian Intelligence。

Virtual service agent

The virtual service agent in Jira Service Management automates support interactions right from within Slack to free up agent time and help teams deliver exceptional support at scale.

There are two primary ways to configure the Jira Service Management virtual service agent, depending on the type and complexity of requests you’re looking to automate: intent flows and Atlassian Intelligence answers (AI answers). You can use one or both of these to help deflect tickets and deliver fast support to your customers.

 

Virtual service agent intent flows

Virtual service agent intents represent a specific problem, question, or request that your virtual service agent can help resolve for your customers. Each intent includes a set of training phrases to help the virtual service agent recognize a help-seeker’s request, and a conversational flow that helps guide the help-seeker through their issue based on their responses to the virtual service agent. Intents are great for questions that:

  • Require guided work/troubleshooting
  • Require information collection and triaging
  • Require an automated action via web request

Examples: Software access requests, reporting an incident, new hardware, procurement requests, onboarding workflows

Intents can be easily configured with out-of-the-box templates and a low code/no code editor. The virtual service agent also uses generative AI to suggest relevant intents based on your team’s historical ticket data and actually populate some of the basic settings like a description and training phrases.

screenshot of The HR service management project template

AI answers in the virtual service agent

AI answers uses generative AI from Atlassian Intelligence to search across your linked knowledge base spaces and answer your customer questions. This feature is great for getting started quickly with the virtual service agent, as there is minimal setup involved, and is particularly powerful in deflecting help requests that:

  • Can be resolved by providing information or instructions
  • Are covered in (or can be easily added to) your existing knowledge base articles
  • Don’t usually need to be escalated to a human agent

Basic IT instructions like BYOD setup, VPN resets, and connecting to office WiFi

Sharing company policies like benefits, expenses, holidays, and more.
 

Slack window

Set up AI answers

To set up AI answers, you’ll first need to configure your virtual service agent intake channels. You can learn more about how to do this in our virtual service agent product guide.

Connect your self-service knowledge base

Once your intake channels are ready to go, you’ll need to ensure that you have a knowledge base linked to your project, either through Confluence or Jira Service Management’s native knowledge base. You can build out your knowledge base directly from Jira Service Management, or integrate existing FAQs and docs you already have in Confluence.

screenshot of Add or customize requests types in a service project
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Pro tip: Double check your knowledge base permissions settings

Your linked knowledge base space needs to be set to All logged-in users under Who can view.

Activate AI answers

Once your knowledge base is ready to go, it’s time to activate AI answers in the virtual service agent settings:

From the navigation on the left, select Project settings, then Virtual service agent. Select Settings, and then Basic settings if not already selected. Turn the toggle on next to Atlassian Intelligence answers, and then select Activate.

screenshot of Editing the employee onboarding request type

If you are using the virtual service agent in Slack, you can activate AI answers for specific Slack request channels. Navigate to Request channels in Settings. Turn the toggle on under AI answers next to the request channel you want to activate it for, and then select Activate.

screenshot of A form for new employee information
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Pro Tip: When structuring knowledge base articles for virtual service agent consumption, please note that AI answers does not currently extract information from images, and typically does best with copy that is not part of a table in Confluence.


AI summaries

Instead of reading through long descriptions and numerous comments on a Jira Service Management issue, you can use Atlassian Intelligence to quickly summarize this information for you. Easily loop in new stakeholders, transition tickets to a new agent, or get up to speed on an issue.

screenshot of A form for new employee information

To use AI summaries:

  1. From your Jira Service Management project, navigate to your desired issue.
  2. Scroll down to the Activity section.
  3. Select Comments, and then Summarize.

    1. The summary generated by Atlassian Intelligence will only be visible to you, and will disappear when you navigate away from the issue. You can summarize an issue’s details as many times as you like.
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Pro Tip: The AI summaries feature works great alongside the virtual service agent. For complex intent flows where the virtual service agent asks multiple questions to gather information from the help seeker before opening a ticket, AI summaries can help the assigned agent quickly digest any issue context the virtual service agent has captured.


Generative AI in the issue editor

screenshot of The help center displaying featured portals

Atlassian Intelligence will also help agents create and improve responses to customers, ensuring clear and thoughtful communication between stakeholders. Generative AI in the issue editor can help agents craft better responses, adjust their tone to be more professional or empathetic, summarize a lengthy knowledge base article to provide concise instructions, and much more.

screenshot of Queues in Jira Service Management

Use cases for generative AI:

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Brainstorm

Not sure how to start a customer response? With the brainstorm feature, Atlassian intelligence analyzes user inputs and generates suggestions for customer responses to inspire and speed up issue resolution.

Memo

Make shorter

With the make shorter feature, Atlassian Intelligence allows you to generate concise summaries of longer responses to customers. This can be useful when you need customers to quickly understand the key points or main ideas.

Memo

Summarize

The summarize feature helps agents condense lengthy content into a concise summary, making it easier to understand and digest. Atlassian Intelligence analyzes the input text and identifies the most relevant and important points. It takes into account factors such as the frequency of certain words or phrases, their context within the text, and any associated sentiment or importance.

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Improve writing 

The improve writing feature in Atlassian Intelligence helps agents enhance their writing skills by providing suggestions. These suggestions may include grammar corrections, word choice recommendations, formatting, and more. Alongside the suggested improvements, Atlassian Intelligence offers explanations and reasoning behind each suggestion as well.

Writing on tablet

Fix spelling & grammar 

The fix spelling and grammar feature in Atlassian Intelligence helps you identify and correct spelling and grammar mistakes in your customer responses. These suggestions are based on common grammatical rules and contextual analysis of the surrounding text. You have the option to accept a suggestion by clicking on it, or you can manually make changes as needed.

Writing on tablet

Change tone

The change tone feature in Atlassian Intelligence allows you to modify the tone of your customer response, allowing agents to adjust the style or mood of the text according to their needs. Available tones include casual, educational, empathetic, neutral, and professional to meet the needs of a variety of customer situations.

How to use generative AI in the issue editor: 

Generative AI in the issue editor can be accessed in two ways:

  1. Select the Atlassian Intelligence icon in the issue editor toolbar.
  2. Type /ai in the issue editor to bring up Atlassian Intelligence.

Generative AI for knowledge base articles

In addition to generative AI for the issue editor, Atlassian Intelligence empowers agents to create knowledge base articles directly from a Jira Service Management issue. Easily brainstorm content for a new article, ensure your spelling and grammar are correct, and make your article sound professional and empathetic for your customers in just a few simple steps.

screenshot of Service level agreement (SLA) settings

How to create a knowledge base article using generative AI

From the Jira Service Management issue view:

  1. From your project sidebar navigation, select Knowledge base.
  2. Select Create article.
  3. Select the knowledge base space you want to create your article in and select Next.
  4. Bring up Atlassian Intelligence via the toolbar or by typing /ai in the editor.
  5. Write your desired prompt.
  6. Atlassian intelligence provides a draft that you can use as a starting point.

Request type suggestions

Request type suggestions can help take the guesswork out of creating your service desk by intelligently suggesting request types based on how you describe the kind of work your team manages. Atlassian Intelligence can suggest request types across a range of use cases, from IT and HR to dog grooming and catering, and then add them to your service desk with just a few clicks.

Once a request type has been created using Atlassian Intelligence, you can add additional forms and fields to capture all relevant details from your customers and also adjust the workflow if necessary.

screenshot of Invite your team to use Jira Service Management

How to use request type suggestions:

  1. Navigate to Project settings > Request types.
  2. Select Suggest.
  3. Describe the type of work your team manages.
  4. Select a request type from the list of AI suggestions and then select Create.
  5. Confirm the request type’s name, description, icon, and issue type.
  6. Select Next and add the request type to a portal group.
  7. Select Create.

Jira Service Management 中的其他智能化体验

除 Atlassian Intelligence 之外,Jira Service Management 的每个部分都融合了基于数据驱动型算法和强大机器学习技术的其他智能化体验。

类似请求和事件

启用“类似请求”面板之后,您可在服务项目中轻松找到与您当前正在处理的事务类似的事务。“类似请求”面板可以显示相似的请求、事件、问题、变更,甚至事后审查,让支持人员能够确定是否存在可以关闭的重复事务,是否存在可以帮助他们更快地解决事务的既往工作单,或者是否有需要进行重大事件上报的类似事件。

帮助中心内显示的知识文章的屏幕截图
信息图标

“类似请求”面板使用自然语言处理 (NLP) 技术,提供标题或描述与您当前正在查看的请求类似的最新请求列表。

对于类似的事件,结果也由 AI 得出。为了帮助改进结果,您可以通过回应 👍 或 👎 来为结果提供反馈。

若要启用或禁用“类似请求”面板,请执行以下操作:

  1. 在您的服务项目中,转到项目设置
  2. 选择功能
  3. 打开/关闭“类似请求”面板开关。

支持智能技术的帮助中心搜索

您的客户在帮助中心内获取信息、提出请求。在帮助中心内,他们可以查看自己有权访问的每个服务项目的门户、搜索请求表单和知识库文章,并查看他们在一段时间内提出的请求。

虚拟支持人员的意图

在搜索帮助中心时,Jira Service Management 会提供一个由强大智能技术支持的搜索栏,允许您利用数据驱动算法和机器学习技术,对整个项目组合进行高级搜索。

虚拟支持人员的意图

帮助中心搜索可识别用户最近的行为及其搜索上下文,从而专门分享与其相关性最高的选项,包括您的知识库和服务门户网站上的请求表单中提供的相关自助资源。最重要的是,随着时间的推移,智能技术会通过学习来为客户优化这些预测结果,从而提高他们的效率,让他们能更快地获得帮助。

要自定义帮助中心,请执行以下操作:

  1. 在您的服务项目中,转到项目设置
  2. 选择门户设置
  3. 选择自定义您的帮助中心部分中的链接。

相关知识文章

除帮助中心之外,智能技术还能直接在事务视图中推荐知识库文章,供支持人员与客户共享。相关的知识文章也基于事务上下文和用户行为,就像在帮助中心内一样。

虚拟支持人员的意图

与当前事务相关的知识文章将出现在事务视图的详细信息部分中,一键即可与客户共享。如果未看到相关文章,您还可以选择手动搜索文章,或直接基于事务创作新文章。

预测式支持人员分配和 @ 提及

最后,协作就是要在正确的时间找到合适的人员来完成项目或向前推进项目。借助 Jira Service Management 中的预测式用户选择器,通过了解您经常合作的人员以及您当前所从事的工作,Smarts 会推荐一份要纳入到事务中的人员列表。通过选择“经办人”字段快速为事务分配支持人员,或使用 @ 显示可能帮助解决事务的用户列表。

预测式分配
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通过从过去的行为中汲取经验,Smarts 可以预测五名最有可能的经办人,且准确率达到 86%。

入门

Enterprise Service Management

提示和技巧

表单设计