--- sourceDocument: Brazil Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/intelligent-experiences Release : - brazil ft:locale : - en-US ft:publication_title : - Brazil Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Discover and manage AI assets # Discovering and managing AI assets {#ariaid-title1} Release version: Brazil 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 Discovering and managing AI assets ServiceNow's AI Control Tower enables organizations to build and maintain a comprehensive inventory of all AI assets across enterprise business units. This centralized AI asset inventory is essential for effective governance, monitoring, and impact measurement of AI systems. It addresses common challenges such as shadow AI, untracked models, and undocumented integrations that create blind spots in risk management. Show full answer Show less ## Key Features * **AI Asset Inventory:** Provides a centralized view of every AI asset including systems, models, prompts, datasets, and MCP servers. Users can manage assets by marking them as managed or unmanaged, filter by type or lifecycle stage, manually add assets, and respond to governance recommendations. * **AI Asset Records:** Detailed records for each asset enable investigation and action, showing governance posture, evaluation scores, lifecycle progress, and value contribution. Users can initiate lifecycle reviews, change requests, or enable evaluations directly from the asset record. * **AI Gateway:** Governs connections between external AI tools and the ServiceNow platform through MCP server management. It tracks and monitors MCP server connections, transaction volumes, and success rates. Approval workflows can be enforced for MCP server connections, and AI stewards can manage server records, pause/resume transactions, and oversee global MCP clients. ## Key Outcomes By leveraging AI Control Tower and AI Gateway, ServiceNow customers gain: * A full and accurate inventory of AI assets across the enterprise, eliminating blind spots in AI governance and risk management. * Enhanced control and visibility into the lifecycle and evaluation of AI assets, facilitating better compliance and operational oversight. * Governed and monitored external AI integrations ensuring secure and approved interactions with the ServiceNow environment. Get a complete picture of every AI system in your organization by building and maintaining a comprehensive AI asset inventory. ## Why discovery matters {#aict-discovering-ai-assets__overview} Before you can govern, monitor, or measure the impact of AI, you need to know what AI assets exist in your organization. Many enterprises operate dozens or hundreds of AI systems across business units, with no single team holding a complete picture. Shadow AI, untracked models, and undocumented integrations create blind spots in governance and risk management. AI Control Tower addresses this by providing multiple discovery pathways that feed into a single AI asset inventory --- a centralized record of every AI system, model, prompt, dataset, and MCP server across your enterprise. See [Discovering AI assets](https://www.servicenow.com/docs/mGE2DGPWApOOgF6f505RZg "Populate your AI asset inventory automatically as AI Control Tower discovers AI assets across external platforms, hyperscaler traces, and your ServiceNow instance."). ## AI asset inventory {#aict-discovering-ai-assets__managing-assets} View and manage AI assets across your entire portfolio or focus on a single asset in the AI Control Tower inventory. The Inventory page is designed to give you a complete picture of every AI asset in your organization. From the inventory, you can make assets managed or unmanaged, filter by asset type or lifecycle stage, manually add assets that aren't reachable by automated methods, and respond to recommendations for assets that need attention. See [Managing your AI asset inventory](https://www.servicenow.com/docs/SI95UJOEtWfKFKBKFJlO3Q "Track every AI system, model, prompt, and dataset across your enterprise and control which assets participate in governance, monitoring, and risk workflows."). ## AI asset records {#aict-discovering-ai-assets__asset-records} The asset record is where you investigate and act on a single asset. In the asset record, you can review the asset's governance posture, evaluation scores, lifecycle progress, and value contribution. You can initiate asset-level actions such as starting a lifecycle review, submitting a change request, or turning on evaluation. See [Working with AI asset records](https://www.servicenow.com/docs/pMaOqXzfH9B7esKM3rKY3A "An asset record consolidates the state of a single AI asset and the actions you can take on it, so you can understand the asset's situation and respond from one page."). ## Connecting external AI through AI Gateway {#aict-discovering-ai-assets__connecting-ai-gateway} AI Gateway manages MCP (Model Context Protocol) server connections, providing a governed pathway for external AI tools to interact with your ServiceNow environment. Where Service Graph Connectors focus on discovering what AI exists externally, AI Gateway focuses on governing how external AI tools connect to and operate within your platform. When an MCP server is added through AI Agent Studio, AI Gateway tracks the connection, monitors transaction volumes and success rates, and enforces approval workflows if MCP server approval controls are active. AI stewards can review MCP server records, pause and resume transactions, and manage global MCP clients from the AI Gateway settings page. AI Gateway provides a comprehensive view of all connected MCP servers, showing name, total transactions, and success rate. Total transactions represent all tool calls (MCP method equal to "tools/call"), and the success rate reflects the proportion of those calls returning a successful HTTP status.