--- sourceDocument: Australia Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences Release : - australia ft:locale : - en-US ft:publication_title : - Australia Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # AI Inventory Intelligence Agent # AI Inventory Intelligence Agent {#ariaid-title1} Release version: Australia Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of AI Inventory Intelligence Agent The AI Inventory Intelligence Agent in ServiceNow automatically analyzes AI asset inventories to identify assets with incomplete metadata and generates enrichment recommendations to improve data quality and governance readiness. It operates on a scheduled basis, evaluates metadata completeness for each asset, and provides actionable suggestions directly on asset records for AI Stewards to accept or dismiss. Show full answer Show less ## Key Features * **Inventory Data Completeness Score:** Measures how fully critical metadata fields are populated for each AI asset, expressed as a percentage. This score is visible on both the AI asset inventory list and individual asset records, helping AI Stewards prioritize assets that need attention. * **Enrichment Recommendations:** Provides field-level metadata suggestions using two strategies: * **Deterministic enrichment:** Rules-based suggestions derived from existing metadata like vendor, model name, and category. * **AI-powered enrichment:** Large Language Model (LLM) generated suggestions for fields such as description, use cases, and documentation, marked with an AI indicator. * **Actionable Recommendations:** AI Stewards can review, accept (which updates the asset), or dismiss suggestions. Accepted recommendations update metadata and improve the completeness score. * **Configurable AI Enrichment:** AI-generated enrichment is off by default and can be enabled by AI Stewards via ServiceNow Otto admin settings. ## How It Works The agent scans managed AI assets regularly, identifies missing or incomplete critical metadata fields, and uses deterministic rules and LLM inference to generate enrichment recommendations. These appear in the Enrichment Recommendations tab on the asset record. AI Stewards manage these recommendations, influencing the completeness score dynamically. ## Enabling and Configuration * Requires installation of the ServiceNow Otto for AI Control Tower plugin (com.snaictgenai). * AI-generated enrichment can be toggled on/off by AI Stewards under Settings \> Rules and templates \> AI skills. * Only users with the AI Steward role can modify AI enrichment settings. * AI-generated enrichment availability depends on selected ServiceNow product tiers. ## Benefits for ServiceNow Customers This agent empowers customers to maintain high-quality, complete metadata in their AI asset inventories, supporting better governance, risk management, and lifecycle workflows. By automating metadata enrichment and providing confidence-scored recommendations, it reduces manual effort and helps ensure assets are ready for governance and compliance processes. AI Inventory Intelligence Agent automatically analyzes the AI asset inventory, identifies assets with incomplete metadata, and generates enrichment recommendations to improve data quality and governance readiness. When the AI Inventory Intelligence Agent is active, it scans the inventory on a scheduled basis and evaluates each asset's metadata completeness. It presents actionable recommendations directly on the asset record. AI Stewards can accept a recommendation to update the asset automatically or dismiss it if the suggestion isn't applicable. ## Inventory data completeness score {#inventory-intelligence-agent__inventory-data-completeness-score} The inventory data completeness score measures how completely each managed AI asset's critical metadata fields are populated. The score is calculated as the percentage of required fields that contain values. A higher completeness score indicates that an asset has sufficient metadata to support governance, risk assessment, and lifecycle management workflows. Assets with low completeness scores are prioritized in enrichment recommendations. The completeness score appears on the AI asset inventory list and on individual asset records, giving AI Stewards a quick view of which assets require attention. Note: The completeness score formula definition is available as a tooltip on the score field. The formula counts the number of critical fields populated out of the total required fields for that asset type. ## Enrichment recommendations {#inventory-intelligence-agent__enrichment-recommendations} Enrichment recommendations are field-level suggestions generated by the AI Inventory Intelligence Agent for assets with incomplete or missing metadata. Recommendations are sourced from two strategies: * Deterministic enrichment: Rules-based suggestions derived from existing asset metadata such as vendor, model name, manufacturer, and category. * AI-powered enrichment: LLM-generated suggestions for fields such as description, use cases, and documentation. AI-generated fields are marked with an AI indicator on the recommendation. Each recommendation includes a confidence score and rationale, so AI Stewards can evaluate the suggestion before accepting it. Accepting a recommendation updates the asset field directly. Dismissing a recommendation leaves the field unchanged. ## How the AI Inventory Intelligence Agent works {#inventory-intelligence-agent__how-inventory-intelligence-agent-works} The agent operates as follows: * The agent scans managed AI assets in the inventory on a scheduled basis. * For each asset, it evaluates which critical metadata fields are missing or incomplete. * It generates field-level enrichment recommendations using deterministic rules and LLM inference. * Recommendations appear on the Enrichment Recommendations tab on the asset record. * The inventory data completeness score is calculated and displayed for the asset. * AI Stewards review recommendations and select accept (asset updated) or dismiss (no change made) for each suggestion. * Accepted recommendations update the asset field; dismissed recommendations leave the field unchanged. * The completeness score updates to reflect accepted changes. ## Enabling the AI Inventory Intelligence Agent {#inventory-intelligence-agent__enabling-inventory-intelligence-agent} To add LLM-generated suggestions for fields such as description, use cases, and documentation, turn on AI-generated enrichment. AI-generated enrichment is off by default, so the agent uses only deterministic enrichment until an AI Steward turns it on. Agent configuration settings, including model provider and skill activation, are managed in ServiceNow Otto admin. Note: To use the AI Inventory Intelligence Agent, install the ServiceNow Otto for AI Control Tower (com.sn_aict_genai) plugin. To turn on AI-generated enrichment, navigate to SettingsRules and templatesAI skills and set the AI-generated enrichment toggle to Active. The toggle sets the sn_ai_governance.enrichment.generative_enabled system property to true, so enrichment runs both deterministic and generative (LLM-based) strategies. To use only deterministic enrichment, set the toggle to Inactive. Only users with the AI Steward role can change this setting. AI-generated enrichments are available with selected ServiceNow product tiers. For more information, see [ServiceNow product tiers](https://www.servicenow.com/docs/r/intelligent-experiences/ai-native-sku-overview.html). *[\>]: and then