Figure 1 Virtual AI Assistant Reference Architecture
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:
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.
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.
Start by setting up the architecture and data needed for the 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.
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.
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.
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.
This post is divided into three parts; they are: • Origination of the Transformer Model…
In this article, we will build step by step a movie recommender system in Python,…
By Ko-Jen Hsiao, Yesu Feng and Sudarshan LamkhedeMotivationNetflix’s personalized recommender system is a complex system,…
Amazon Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate…
Experian's enterprise AI framework offers valuable lessons for businesses seeking to scale beyond proof of…