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sourceDocument: Australia Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/intelligent-experiences

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    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Enable AI

ft:clusterId :

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---

# Discover and manage AI assets

# Discovering and managing AI assets {#ariaid-title1}

Release version: Australia  
Updated April 17, 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

Building and maintaining a comprehensive AI asset inventory is crucial for understanding and managing AI systems in your organization.
It allows for effective governance, monitoring, and measurement of AI impacts across various business units.
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## Key Features

* **Centralized AI Asset Inventory:** AI Control Tower provides a single inventory for tracking all AI systems, models, prompts, datasets, and MCP servers, mitigating risks associated with untracked or undocumented AI assets.
* **Management Capabilities:** The Inventory page allows you to manage AI assets by setting statuses, filtering by type or lifecycle stage, and manually adding untracked assets.
* **Automatic Discovery:** ServiceNow automatically discovers AI assets, such as skills, agents, and models, providing immediate visibility to governance teams without manual configuration.
* **External Asset Discovery:** Service Graph Connectors enable visibility of AI assets on external platforms, synchronizing them into your inventory for comprehensive management.
* **Hyperscaler Connections:** AI Control Tower supports connections to AWS, Azure, and GCP for asset discovery and trace collection, enhancing monitoring across multiple environments.

## Key Outcomes

By implementing AI asset discovery and management, organizations can achieve a complete understanding of their AI landscape, improve governance, reduce blind spots in risk management, and ensure compliance with operational frameworks. This leads to enhanced performance monitoring and informed decision-making regarding AI investments.  
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.

## Managing your AI assets {#aict-discovering-ai-assets__managing-assets}

You can view and manage AI assets across your entire portfolio or focus on a single asset.

The Inventory page gives you a complete picture of every AI system, model, prompt, dataset, and MCP server in your organization. From here, you set management status, filter by asset type or lifecycle stage,
manually add assets that aren't reachable by automated methods, and respond to recommendations that flag assets needing attention. See [Managing your AI asset inventory](https://www.servicenow.com/docs/3ZVjUL6I1LvmAqJ6Hjecgg "Track every AI system, model, prompt, and dataset across your enterprise and control which assets participate in governance, monitoring, and risk workflows.").

The asset record page is where you investigate and act on a single asset. Open a record to review its governance posture, evaluation scores, lifecycle progress, and value contribution, and to initiate 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/IMP4iJYdv_OXD3xVLe~CTw "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.").

## Discovering ServiceNow AI assets automatically {#aict-discovering-ai-assets__now-assist-discovery}

ServiceNow AI assets including skills, agents, and models running natively on your instance are discovered automatically. No manual configuration is required. As AI capabilities are activated on your
instance, the discovery process detects and registers them in the AI asset inventory, giving your governance team visibility into every ServiceNow AI system from the moment it is deployed.

Automatic discovery eliminates the risk of ServiceNow AI systems operating outside the governance framework. Every skill, every AI agent built in AI Agent Studio, and every model connection is tracked and available for lifecycle management, risk assessment, and performance monitoring.

## Discovering external AI assets with Service Graph Connectors {#aict-discovering-ai-assets__enterprise-discovery}

Most enterprises run AI on more than one platform. Service Graph Connectors extend AI Control Tower's visibility beyond ServiceNow by discovering AI assets running on external platforms and synchronizing them into the
inventory.

Each connector requires an AI connection --- a configured credential and endpoint that allows AI Control Tower to communicate with the external platform. Once configured, the connector runs on a schedule to discover new assets, update existing records, and synchronize metadata. Some connectors also collect
usage and execution data, enabling monitoring and value measurement for external AI alongside ServiceNow AI.

For organizations that operate AI across hyperscaler environments, AI Control Tower also supports hyperscaler connections for asset discovery and trace collection. These connections collect trace data from AWS, Azure, and GCP through a MID Server, without requiring SDK
instrumentation in agent code.