Today’s organizations face a critical challenge with the fragmentation of vital information across multiple environments. As businesses increasingly rely on diverse project management and IT service management (ITSM) tools such as ServiceNow, Atlassian Jira and Confluence, employees find themselves navigating a complex web of systems to access crucial data.
This isolated approach leads to several challenges for IT leaders, developers, program managers, and new employees. For example:
- Inefficiency: Employees need to access multiple systems independently to gather data insights and remediation steps during incident troubleshooting
- Lack of integration: Information is isolated across different environments, making it difficult to get a holistic view of ITSM activities
- Time-consuming: Searching for relevant information across multiple systems is time-consuming and reduces productivity
- Potential for inconsistency: Using multiple systems increases the risk of inconsistent data and processes across the organization.
Amazon Q Business is a fully managed, generative artificial intelligence (AI) powered assistant that can address challenges such as inefficient, inconsistent information access within an organization by providing 24/7 support tailored to individual needs. It handles a wide range of tasks such as answering questions, providing summaries, generating content, and completing tasks based on data in your organization. Amazon Q Business offers over 40 data source connectors that connect to your enterprise data sources and help you create a generative AI solution with minimal configuration. Amazon Q Business also supports over 50 actions across popular business applications and platforms. Additionally, Amazon Q Business offers enterprise-grade data security, privacy, and built-in guardrails that you can configure.
This blog post explores an innovative solution that harnesses the power of generative AI to bring value to your organization and ITSM tools with Amazon Q Business.
Solution overview
The solution architecture shown in the following figure demonstrates how to build a virtual IT troubleshooting assistant by integrating with multiple data sources such as Atlassian Jira, Confluence, and ServiceNow. This solution helps streamline information retrieval, enhance collaboration, and significantly boost overall operational efficiency, offering a glimpse into the future of intelligent enterprise information management.
This solution integrates with ITSM tools such as ServiceNow Online and project management software such as Atlassian Jira and Confluence using the Amazon Q Business data source connectors. You can use a data source connector to combine data from different places into a central index for your Amazon Q Business application. For this demonstration, we use the Amazon Q Business native index and retriever. We also configure an application environment and grant access to users to interact with an application environment using AWS IAM Identity Center for user management. Then, we provision subscriptions for IAM Identity Center users and groups.
Authorized users interact with the application environment through a web experience. You can share the web experience endpoint URL with your users so they can open the URL and authenticate themselves to start chatting with the generative AI application powered by Amazon Q Business.
Deployment
Start by setting up the architecture and data needed for the demonstration.
- We’ve provided an AWS CloudFormation template in our GitHub repository that you can use to set up the environment for this demonstration. If you don’t have existing Atlassian Jira, Confluence, and ServiceNow accounts follow these steps to create trial accounts for the demonstration
- Once step 1 is complete, open the AWS Management Console for Amazon Q Business. On the Applications tab, open your application to see the data sources. See Best practices for data source connector configuration in Amazon Q Business to understand best practices
- To improve retrieved results and customize the end user chat experience, use Amazon Q to map document attributes from your data sources to fields in your Amazon Q index. Choose the Atlassian Jira, Confluence Cloud and ServiceNow Online links to learn more about their document attributes and field mappings. Select the data source to edit its configurations under Actions. Select the appropriate fields that you think would be important for your search needs. Repeat the process for all of the data sources. The following figure is an example of some of the Atlassian Jira project field mappings that we selected
- Sync mode enables you to choose how you want to update your index when your data source content changes. Sync run schedule sets how often you want Amazon Q Business to synchronize your index with the data source. For this demonstration, we set the Sync mode to Full Sync and the Frequency to Run on demand. Update Sync mode with your changes and choose Sync Now to start syncing data sources. When you initiate a sync, Amazon Q will crawl the data source to extract relevant documents, then sync them to the Amazon Q index, making them searchable
- After syncing data sources, you can configure the metadata controls in Amazon Q Business. An Amazon Q Business index has fields that you can map your document attributes to. After the index fields are mapped to document attributes and are search-enabled, admins can use the index fields to boost results from specific sources, or by end users to filter and scope their chat results to specific data. Boosting chat responses based on document attributes helps you rank sources that are more authoritative higher than other sources in your application environment. See Boosting chat responses using metadata boosting to learn more about metadata boosting and metadata controls. The following figure is an example of some of the metadata controls that we selected
- For the purposes of the demonstration, use the Amazon Q Business web experience. Select your application under Applications and then select the Deployed URL link in the web experience settings
- Enter the same username, password and multi-factor authentication (MFA) authentication for the user that you created previously in IAM Identity Center to sign in to the Amazon Q Business web experience generative AI assistant
Demonstration
Now that you’ve signed in to the Amazon Q Business web experience generative AI assistant (shown in the previous figure), let’s try some natural language queries.
IT leaders: You’re an IT leader and your team is working on a critical project that needs to hit the market quickly. You can now ask questions in natural language to Amazon Q Business to get answers based on your company data.
Developers: Developers who want to know information such as the tasks that are assigned to them, specific tasks details, or issues in a particular sub segment. They can now get these questions answered from Amazon Q Business without necessarily signing in to either Atlassian Jira or Confluence.
Project and program managers: Project and program managers can monitor the activities or developments in their projects or programs from Amazon Q Business without having to contact various teams to get individual status updates.
New employees or business users: A newly hired employee who’s looking for information to get started on a project or a business user who needs tech support can use the generative AI assistant to get the information and support they need.
Benefits and outcomes
From the demonstrations, you saw that various users whether they are leaders, managers, developers, or business users can benefit from using a generative AI solution like our virtual IT assistant built using Amazon Q Business. It removes the undifferentiated heavy lifting of having to navigate multiple solutions and cross-reference multiple items and data points to get answers. Amazon Q Business can use the generative AI to provide responses with actionable insights in just few seconds. Now, let’s dive deeper into some of the additional benefits that this solution provides.
- Increased efficiency: Centralized access to information from ServiceNow, Atlassian Jira, and Confluence saves time and reduces the need to switch between multiple systems.
- Enhanced decision-making: Comprehensive data insights from multiple systems leads to better-informed decisions in incident management and problem-solving for various users across the organization.
- Faster incident resolution: Quick access to enterprise data sources and knowledge and AI-assisted remediation steps can significantly reduce mean time to resolutions (MTTR) for cases with elevated priorities.
- Improved knowledge management: Access to Confluence’s architectural documents and other knowledge bases such as ServiceNow’s Knowledge Articles promotes better knowledge sharing across the organization. Users can now get responses based on information from multiple systems.
- Seamless integration and enhanced user experience: Better integration between ITSM processes, project management, and software development streamlines operations. This is helpful for organizations and teams that incorporate agile methodologies.
- Cost savings: Reduction in time spent searching for information and resolving incidents can lead to significant cost savings in IT operations.
- Scalability: Amazon Q Business can grow with the organization, accommodating future needs and additional data sources as required. Organization can create more Amazon Q Business applications and share purpose-built Amazon Q Business apps within their organizations to manage repetitive tasks.
Clean up
After completing your exploration of the virtual IT troubleshooting assistant, delete the CloudFormation stack from your AWS account. This action terminates all resources created during deployment of this demonstration and prevents unnecessary costs from accruing in your AWS account.
Conclusion
By integrating Amazon Q Business with enterprise systems, you can create a powerful virtual IT assistant that streamlines information access and improves productivity. The solution presented in this post demonstrates the power of combining AI capabilities with existing enterprise systems to create powerful unified ITSM solutions and more efficient and user-friendly experiences.
We provide the sample virtual IT assistant using an Amazon Q Business solution as open source—use it as a starting point for your own solution and help us make it better by contributing fixes and features through GitHub pull requests. Visit the GitHub repository to explore the code, choose Watch to be notified of new releases, and check the README for the latest documentation updates.
Learn more:
For expert assistance, AWS Professional Services, AWS Generative AI partner solutions, and AWS Generative AI Competency Partners are here to help.
We’d love to hear from you. Let us know what you think in the comments section, or use the issues forum in the GitHub repository.
About the Authors
Jasmine Rasheed Syed is a Senior Customer Solutions manager at AWS, focused on accelerating time to value for the customers on their cloud journey by adopting best practices and mechanisms to transform their business at scale. Jasmine is a seasoned, result oriented leader with 20+ years of progressive experience in Insurance, Retail & CPG with exemplary track record spanning across Business Development, Cloud/Digital Transformation, Delivery, Operational & Process Excellence and Executive Management.
Suprakash Dutta is a Sr. Solutions Architect at Amazon Web Services. He focuses on digital transformation strategy, application modernization and migration, data analytics, and machine learning. He is part of the AI/ML community at AWS and designs Generative AI and Intelligent Document Processing(IDP) solutions.
Joshua Amah is a Partner Solutions Architect at Amazon Web Services, specializing in supporting SI partners with a focus on AI/ML and generative AI technologies. He is passionate about guiding AWS Partners in using cutting-edge technologies and best practices to build innovative solutions that meet customer needs. Joshua provides architectural guidance and strategic recommendations for both new and existing workloads.
Brad King is an Enterprise Account Executive at Amazon Web Services specializing in translating complex technical concepts into business value and making sure that clients achieve their digital transformation goals efficiently and effectively through long term partnerships.
Joseph Mart is an AI/ML Specialist Solutions Architect at Amazon Web Services (AWS). His core competence and interests lie in machine learning applications and generative AI. Joseph is a technology addict who enjoys guiding AWS customers on architecting their workload in the AWS Cloud. In his spare time, he loves playing soccer and visiting nature.
