Capture AI use case data

The license package Enterprise Portfolio Management is required to work with AI use cases. The use case AI Portfolio Management must be activated.

An AI use case describes the capabilities and technologies used to achieve a defined business goal in the enterprise using artificial intelligence. Examples of AI use cases include enabling a customer support chatbot and agentic approvals. When applying AI governance, the AI use case is the level typically used to drive approval of AI usage in the organization.

  1. Go to AI Portfolio Management > AI Use Cases and click New > AI Use Case. Define a unique name and basic attributes and click OK.

    Per default, the data workbench displays only a set of basic attributes. You can add more columns to capture other attributes directly in the data workbench or you can navigate to an AI feature's content area and define it in more detail there.

  2. Specify data in the data workbench or click the Navigate  Navigate button next to an AI use case to open its content area. Specify the AI use case's attributes as well as the relationships that the AI use case has to other assets in the repository.

Try to capture as much information as possible about the AI use case because complete data considerably improves the results of business questions and other analytics.

Go to the AI use case's content area > Overview.

Define the AI model's basic data. All mandatory fields must be defined to create the AI use case and save it.

Specify the locations where the AI use case is/is not allowed. Specify the locations where the AI use case is permissible in the Geographic Limitations > Allowed Locations field. Specify where the AI use case may not be used in the Disallowed Locations field.

You can bundle multiple AI features in the use case.

  1. Go to the AI use case's content area > Overview > AI Features.
  2. Click the New plus sign button > Create AI Feature for This Use Case. You can also select an AI technology from the repository via Add Existing AI Feature for This Use Case.
  3. Specify the following in the data workbench:

The risk level of each AI feature will contribute to the overall risk level of the AI use case and whether the AI use case will be approved or not.

Go to the AI use case's content area > Overview page.

  1. Review the risk levels of the AI features assigned to this use case to specify the risk of the AI use case in the AI Features page.
  2. Specify the AI Risk Level attribute of the AI use case. Based on the risk levels of the AI features, specify the AI Risk Level attribute of the use case. The risk level of the AI use case should have a value that is higher or equal to the risk level value of any of its AI features. If this is not the case, a data quality violation will be triggered and the AI Risk Level field will be highlighted.
  3. If a violation occurs, go to the Architecture Scope page to understand risk level and approval status conflicts in the AI architecture.

Responsible users should typically assess whether the use case meets requirements regarding AI regulations. This process can be managed by a workflow that triggers workflow activities for the users responsible for the assessment.

Review whether the AI use case is approved in terms of its architecture, legality, data privacy, and security. View the comments left by the responsible roles about their assessments. Go to the AI use case's content area > Approvals page.

The Approval Responsibilities sections shows who is responsible for approving various aspects of the AI use case. Each responsible person should specify their aspect of the approval process in the Approvals section. The reasons for their decisions should be captured in the Approval Details section.

If a data quality issue is triggered when you set the AI use case status to Approved, you should explore what the stopgaps are in the AI architecture to see if they can be resolved or if alternative AI architecture is needed.

Go to the AI use case's content area > Architecture Scope page. The Risk Level and Approval Analysis report visualizes a network diagram of the AI portfolio for the selected use case. The diagram shows the AI use case  AIUseCase on the left pointing to the AI features  AIFEature . The AI features are connected to the AI models  AIModel and AI technologies  AITechnology . Click the  ThreeDots_Black 3-dots button > Show Legend to understand the symbols and color coding of the AI architecture elements.

  • Review the dependencies in the AI use case. Find unused AI technologies and consider whether they are relevant to the AI portfolio.
  • Understand where AI architecture has not been approved. The AI use case should not be approved if any of its AI features, AI models or AI technologies are not approved. Point to the exclamation mark symbol to see a tooltip regarding conflicting approval statuses.
  • Look for issues where the risk level is not aligned between the AI use case and its AI features. The AI use case should not have a risk level value that is lower than the risk level of its AI features. Use the legend to understanding the color-coding for such conflicts.

A preconfigured workflow is available to approve AI use cases. Your enterprise may have modified the workflow or configured other workflows for your company's needs.

When applying AI governance, the AI use case is the level typically used to drive approval of AI usage in the organization. Once you have captured the necessary data for the use case including a risk assessment and the locations where it may or may not be used, you can trigger a workflow to request approval of the use case from responsible users.

Go to the AI use case's content area. In the upper right corner, click Workflow < Approve AI Use Case . The workflow will be triggered and the relevant workflow activities sent to the relevant responsible users. They will see the workflow activity in the Workflows section of the left navigation pane where they can perform their workflow activities.

The following business questions rely on AI use case data: