AI use case and AI model element fields
The Elements grid displays dynamic fields when you add or edit an AI use case or AI model element in the Third-party Risk Management application.
Dynamic field behavior
Some fields in the Elements grid are static and appear for every element regardless of classification. For static fields, see Third-party element form. The fields in this topic are dynamic and appear only when you add or edit an AI use case or AI model element.
AI use case dynamic fields
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Field |
Description |
|---|---|
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Interaction type with end users |
How the AI system interacts with end users.
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AICT State |
The life cycle state of the AI use case.
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Area where the AI system is used |
The business area or domain where the AI system operates.
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System autonomy level |
The degree of autonomy the AI system has when making decisions or taking actions without human intervention.
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Intended outcome of the AI system |
The outcome the AI system produces.
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Level of human involvement |
The extent to which a human reviews or approves the AI system's outputs before they take effect.
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Data used by the system |
The type or category of data the AI system uses to generate its outputs.
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Type of output produced |
The kind of output the AI system generates.
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Asset type |
The type of underlying AI asset the use case is built on.
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People affected by the AI system |
The individuals or groups whose rights, safety, or opportunities may be affected by the AI system's use.
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Version |
The version number of the AI use case. |
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Description |
A description of the AI use case. |
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Provider |
The organization that provides the AI use case. |
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Supported locations |
The geographic locations where the AI use case applies or is supported. |
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Documentation |
Links to documentation describing the AI use case, such as technical specifications or governance records. |
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License details |
Information about the licensing terms that apply to the AI use case. Note: Check your entitlements to determine whether licensing terms apply to this AI use case. |
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Additional use & purpose |
Additional context about how or why the AI use case is used, beyond its primary intended outcome. |
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Department |
The department responsible for, or using, the AI use case. |
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Related assets: AI models |
Links the AI models the use case relies on. For more information, see Manage elements in the grid. |
|
Field |
Description |
|---|---|
|
Global AI model |
A global AI model can be linked to AI use case elements across multiple third parties. A local AI model (with Global AI model cleared) is available only within the third party where you created it. For more information, see Manage elements in the grid. |
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Deployment guidelines |
Guidance on how to deploy the AI model, such as configuration or environment requirements. |
|
Context window |
The maximum amount of input the AI model can process in a single request, typically measured in tokens. |
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Model card |
A link to or summary of the model card documenting the AI model's intended use, performance, and limitations. |
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Model size (in MB) |
The size of the AI model, in megabytes. |
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Training procedure |
The method used to train the AI model.
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Required infrastructure |
The hardware or infrastructure needed to run the AI model. |
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Model weights info |
Information about the numerical weights learned during training that influence how the AI model generates outputs. |
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Model parameters info |
Information about the AI model's parameters, such as the parameter count. |
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Evaluation metrics report |
A report describing how the AI model was evaluated and the results of that evaluation. |
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Related assets: Base models |
Links the base models the AI model relies on. A base model is a foundational AI model that has been pretrained on a large dataset and can be further fine-tuned for specific use cases. These models serve as a starting point for developing specialized AI models adapted to domain-specific data and requirements. |
|
Version |
The version number of the AI model. |
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Description |
A description of the AI model. |
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Provider |
The organization that provides the AI model. |
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Supported locations |
The geographic locations where the AI model applies or is supported. |
|
Supported languages |
The languages the AI model supports. |
--- title: AI use case and AI model element fields description: The Elements grid displays dynamic fields when you add or edit an AI use case or AI model element in the Third-party Risk Management application. locale: en-US canonical_url: https://www.servicenow.com/docs/r/australia/governance-risk-compliance/third-party-risk-management/tprm-tpe-ai-fields-form.html release: australia product: Third-party Risk Management classification: third-party-risk-management topic_type: reference last_updated: "2026-09-10" reading_time_minutes: 5 keywords: [AI use case, AI model, third-party element, Elements grid] breadcrumb: [Manage elements in the grid, Monitoring third-party elements, Monitor third-party risk, Third-party Risk Management, Governance, Risk, and Compliance] --- # AI use case and AI model element fields The Elements grid displays dynamic fields when you add or edit an AI use case or AI model element in the Third-party Risk Management application. ## Dynamic field behavior Some fields in the Elements grid are static and appear for every element regardless of classification. For static fields, see [Third-party element form](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/governance-risk-compliance/third-party-risk-management/tprm-tpe-form.md). The fields in this topic are dynamic and appear only when you add or edit an AI use case or AI model element. ## AI use case dynamic fields <table id="table_ai_use_case_fields"><thead><tr><th> Field </th><th> Description </th></tr></thead><tbody><tr><td> Interaction type with end users </td><td> How the AI system interacts with end users. - Not applicable - No direct interaction - Background support - Notifications & prompts - User facing recommendations - Chat based interaction - Interactive experience </td></tr><tr><td> AICT State </td><td> The life cycle state of the AI use case. - Draft - Deployed - Development </td></tr><tr><td> Area where the AI system is used </td><td> The business area or domain where the AI system operates. - Internal operations - Customer services - Sales & Marketing - Finance & Accounting - IT & Security - Supply chain - HR & Workforce - External partner ecosystem </td></tr><tr><td> System autonomy level </td><td> The degree of autonomy the AI system has when making decisions or taking actions without human intervention. - Not applicable - Assistive \(AI suggests\) - Semi automated - Condition based automation - Event triggered automation - Fully automated execution </td></tr><tr><td> Intended outcome of the AI system </td><td> The outcome the AI system produces. - Not Applicable - Efficiency Boost - Quality Enhancement - Decision Guidance - Automation of Tasks - Customer Experience Upgrade - Insight Generation </td></tr><tr><td> Level of human involvement </td><td> The extent to which a human reviews or approves the AI system's outputs before they take effect. - Not applicable - Full user control - User guided with AI support - Shared control - AI initiated with user approval - Fully automated workflow </td></tr><tr><td> Data used by the system </td><td> The type or category of data the AI system uses to generate its outputs. - Public or general info - Business operational data - Customer interaction data - Behavioral or usage data - Profile or account data - Sensitive business data </td></tr><tr><td> Type of output produced </td><td> The kind of output the AI system generates. - Simple alerts - Insight & summaries - Ranking & scores - Recommendations - Generated content - Automated decisions - System actions </td></tr><tr><td> Asset type </td><td> The type of underlying AI asset the use case is built on. - Agentic AI - Classic AI - Digital worker - Generative AI </td></tr><tr><td> People affected by the AI system </td><td> The individuals or groups whose rights, safety, or opportunities may be affected by the AI system's use. - Internal team - Specific customer groups - General customer base - External partners - Public or large audiences </td></tr><tr><td> Version </td><td> The version number of the AI use case. </td></tr><tr><td> Description </td><td> A description of the AI use case. </td></tr><tr><td> Provider </td><td> The organization that provides the AI use case. </td></tr><tr><td> Supported locations </td><td> The geographic locations where the AI use case applies or is supported. </td></tr><tr><td> Documentation </td><td> Links to documentation describing the AI use case, such as technical specifications or governance records. </td></tr><tr><td> License details </td><td> Information about the licensing terms that apply to the AI use case. **Note:** Check your entitlements to determine whether licensing terms apply to this AI use case. </td></tr><tr><td> Additional use & purpose </td><td> Additional context about how or why the AI use case is used, beyond its primary intended outcome. </td></tr><tr><td> Department </td><td> The department responsible for, or using, the AI use case. </td></tr><tr><td> Related assets: AI models </td><td> Links the AI models the use case relies on. For more information, see [Manage elements in the grid](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/governance-risk-compliance/third-party-risk-management/tprm-elements-grid-manage.md). </td></tr></tbody> </table>## AI model dynamic fields <table id="table_ai_model_fields"><thead><tr><th> Field </th><th> Description </th></tr></thead><tbody><tr><td> Global AI model </td><td> A global AI model can be linked to AI use case elements across multiple third parties. A local AI model \(with **Global AI model** cleared\) is available only within the third party where you created it. For more information, see [Manage elements in the grid](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/governance-risk-compliance/third-party-risk-management/tprm-elements-grid-manage.md). </td></tr><tr><td> Deployment guidelines </td><td> Guidance on how to deploy the AI model, such as configuration or environment requirements. </td></tr><tr><td> Context window </td><td> The maximum amount of input the AI model can process in a single request, typically measured in tokens. </td></tr><tr><td> Model card </td><td> A link to or summary of the model card documenting the AI model's intended use, performance, and limitations. </td></tr><tr><td> Model size \(in MB\) </td><td> The size of the AI model, in megabytes. </td></tr><tr><td> Training procedure </td><td> The method used to train the AI model. - Linear regression - Logistic regression - Decision trees - Random forest - Supervised learning - Unsupervised learning - Reinforcement learning - Transfer learning - Semi supervised learning - Instruction finetuning - Supervised finetuning </td></tr><tr><td> Required infrastructure </td><td> The hardware or infrastructure needed to run the AI model. </td></tr><tr><td> Model weights info </td><td> Information about the numerical weights learned during training that influence how the AI model generates outputs. </td></tr><tr><td> Model parameters info </td><td> Information about the AI model's parameters, such as the parameter count. </td></tr><tr><td> Evaluation metrics report </td><td> A report describing how the AI model was evaluated and the results of that evaluation. </td></tr><tr><td> Related assets: Base models </td><td> Links the base models the AI model relies on. A base model is a foundational AI model that has been pretrained on a large dataset and can be further fine-tuned for specific use cases. These models serve as a starting point for developing specialized AI models adapted to domain-specific data and requirements. </td></tr><tr><td> Version </td><td> The version number of the AI model. </td></tr><tr><td> Description </td><td> A description of the AI model. </td></tr><tr><td> Provider </td><td> The organization that provides the AI model. </td></tr><tr><td> Supported locations </td><td> The geographic locations where the AI model applies or is supported. </td></tr><tr><td> Supported languages </td><td> The languages the AI model supports. </td></tr></tbody> </table>**Related topics** [Third-party element form](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/governance-risk-compliance/third-party-risk-management/tprm-tpe-form.md) [Manage elements in the grid](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/governance-risk-compliance/third-party-risk-management/tprm-elements-grid-manage.md) [Monitoring third-party elements](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/governance-risk-compliance/third-party-risk-management/tprm-monitor-tp-elements.md)
| title | AI use case and AI model element fields |
| description | The Elements grid displays dynamic fields when you add or edit an AI use case or AI model element in the Third-party Risk Management application. |
| locale | en-US |
| canonical_url | https://www.servicenow.com/docs/r/australia/governance-risk-compliance/third-party-risk-management/tprm-tpe-ai-fields-form.html |
| release | australia |
| product | Third-party Risk Management |
| classification | third-party-risk-management |
| topic_type | reference |
| last_updated | 2026-09-10 |
| reading_time_minutes | 5 |
| keywords | AI use caseAI modelthird-party elementElements grid |
| breadcrumb | Manage elements in the gridMonitoring third-party elementsMonitor third-party riskThird-party Risk ManagementGovernanceRiskand Compliance |