Capture AI use case data

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   button next to an AI use case to open its content area. Specify the AI feature's attributes as well as the relationships that the AI use case has to other assets in the repository.

• Name: (Mandatory) Enter a unique name for the AI use case. The name should help others easily understand the purpose of the AI use case.
• Status: This is an approval status. The AI use case should only be set to Approved when the statuses of the associated AI features, AI models, and AI technologies are approved. If this is not the case, a data quality violation will be triggered and highlight the the Status field and request you to review the AI features, AI models, and AI technologies. Possible status values are:
  • Draft: The AI use case has only mandatory data defined.
  • Under Review: The AI use case is documented and being reviewed. An AI model with this release status cannot be deleted.
  • Approved: The AI use case has been approved by the responsible stakeholders.
  • Declined: The AI use case has not been approved.
  • Retired: The AI use case is no longer valid.
• Enabled Business Capabilities: Specify the business capabilities that the AI use case supports.

  business capability

  A business capability is a high-level description of what is done in a company to meet its business objectives. Market development, product development, and support and services are examples of business capabilities.

• AI Risk Level: Assess the risk levels of the AI features assigned to this use case to specify the risk of the AI use case. The risk level should have a value that is lower than the risk level value of any of its AI features. If this is not the case, a data quality violation will be triggered and highlight the the AI Risk Level field and request you to review the risk levels of the AI features and adjust the risk level of the AI use case accordingly. Note that the AI Use Case Has Highest Risk Level indicator will display No if the risk level of the AI use case is too high.
• AI Use Case Has Highest Risk Level: Indicates whether the AI Risk Level attribute of the AI use case is as high as the highest AI feature risk level. The value is automatically generated based on the AI Risk Level attributes of the AI features assigned to the AI use case. If set to No, go to the Risk Level/Approval Analysis page to understand risk level and approval status conflicts of the AI architecture.
• Enabling AI Architecture Approved: Indicates whether the AI features, AI models, and AI technologies associated with the AI use case have been assessed and approved. The value is automatically generated based on the Status attributes of the AI architecture associated with the AI use case. If set to No, go to the Risk Level/Approval Analysis page to understand risk level and approval status conflicts of the AI architecture.
• Description: Enter a meaningful description that will clarify the purpose of the AI use case.

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.

1. Go to the AI use case's content area > Overview > AI FEatures.
2. Click the  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:
   • Status: This is an approval status. The associated AI use case should only be set to Approved when the statuses of the associated AI features are approved.
     • Draft: The AI feature has only mandatory data defined.
     • Under Review: The AI feature is documented and being reviewed.
     • Approved: The AI feature has been approved by the responsible stakeholders.
     • Declined: The AI feature has not been approved.
     • Retired: The AI feature is no longer valid.
   • AI Risk Level: Specify the level of the risk if the AI feature were enabled. The risk level of each AI feature will contribute to the overall risk level of the AI use case and whether it will be approved or not.
   • Is Enabled: Specify whether the AI feature is enabled for the applications that provide it.
   • AI Use Case: Specify the AI use cases that the AI feature belongs to. The use case describes the larger aim of the AI feature. This is typically done after AI use cases are already added to the repository.

     AI use case

     An AI use case describes the capabilities and technologies used to achieve a defined business goal in the enterprise using artificial intelligence. Examples 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.

   • Enabling AI Model: Specify the AI model that the AI feature is based on. Typically, this is done once your AI models are already added to the repository.

     AI model

     An AI model is a program trained on data to recognize patterns, make predictions, or perform tasks autonomously without needing explicit human instructions for each decision. Typical AI models are Chat GPT, Claude, or Gemini. An AI model provides various AI technologies.

   • Enabling AI Technologies: Specify the AI technologies that the AI feature offers. Typically, this is done once your AI technologies are added to the repository.

     AI technology

     An AI technology is a technical tool or method used to perform a task to enable an AI feature. For example, the AI feature Natural Language Processing requires such technologies as Tokenization, Speech Tagging, Text Classification, and Machine Translation.

   • Providing Application: Specify the application providing the AI feature.
   • Providing Component: Specify the component that supports the application providing the AI feature.