Artificial Intelligence is moving faster than most governance frameworks can keep up with. Many organisations have already deployed Generative AI, Agentic AI, machine learning models, and AI-powered workflows across departments. However, a common challenge remains: how do you govern all these AI assets consistently, securely, and transparently?


This is exactly where ServiceNow AI Control Tower comes in. Rather than treating governance as an afterthought, AI Control Tower provides a centralised workspace that helps organisations manage AI systems, risks, compliance requirements, lifecycle reviews, ownership, and business value from a single location.


In this guide, we'll walk through how to configure the key components of AI Control Tower using simple language and practical steps.


Why AI Control Tower Matters


Many organisations know how many servers they own because they maintain a CMDB. However, ask the same organisation:

  • How many AI systems are currently running?
  • Which AI models are high-risk?
  • Which AI assets are non-compliant?
  • Who owns each AI system?
  • What business value is being delivered?

The answers are often unclear. AI Control Tower solves this challenge by creating a single source of truth for AI governance, similar to how CMDB provides visibility into IT assets.


Step 1: Access and Configure the AI Control Tower Dashboard


The first step is gaining visibility into your AI estate.


What the Overview Dashboard Shows


The AI Control Tower Overview tab provides an executive-level view of:

  • AI systems currently registered
  • AI systems awaiting review
  • Risk classifications
  • AI providers
  • Compliance status
  • Open AI governance cases
  • AI adoption and productivity metrics


This gives AI Stewards, Risk Teams, and Executives immediate visibility into the organisation's AI posture.


Configuration Steps


  1. Navigate to Workspaces
  2. Select AI Control Tower Workspace
  3. Open the Overview Tab
  4. Review:
  • AI Systems
  • Compliance Widgets
  • Risk Classifications
  • AI Cases
  • Productivity Metrics

5. Configure dashboard permissions based on user roles


The dashboard becomes the command centre for monitoring AI governance activities across the enterprise.

ServiceNow AI Control Tower dashboard showing compliance scores, risk classifications, and governance metrics overview.

Step 2: Configure AI Governance Roles


One of the biggest mistakes organisations make is giving users too much—or too little—access. AI Control Tower uses role-based governance to ensure the right people can perform the right tasks.


Key Roles to Configure


AI Steward


The AI Steward is the primary governance administrator.

Responsibilities include:

  • Registering AI assets
  • Managing lifecycle reviews
  • Configuring governance frameworks
  • Reviewing compliance
  • Viewing the complete AI estate


How to Assign



  1. Navigate to:
  • User Administration
  • Roles

2. Search for:

  • AI Steward Role

3. Assign the role to governance personnel



AI Product Owner


The Product Owner manages specific AI systems. Responsibilities include:

  • Monitoring assigned AI assets
  • Managing AI lifecycle activities
  • Tracking asset performance

Unlike AI Stewards, Product Owners only see AI systems they own.


How to Assign

  1. Navigate to Roles
  2. Search for AI Asset Owner
  3. Assign to responsible AI product teams


Risk and Compliance Users


These users focus on:

  • Compliance reviews
  • Regulatory assessments
  • Risk monitoring
  • Audit readiness


Best Practice


Avoid assigning AI Steward access to every user. Maintain separation of duties between governance teams and AI product teams.

Comparison table showing AI Steward, Product Owner, and Risk & Compliance User roles with access levels across six capability categories.

Step 3: Build Your AI Inventory Using CMDB


One of the most powerful capabilities of AI Control Tower is the AI Inventory. Think of it as a CMDB for AI.

Instead of tracking servers and applications, you're tracking:

  • AI Systems
  • AI Models
  • AI Datasets
  • AI Prompts


Understanding the Four Core AI Assets


AI System

An AI System represents the business use case.

Examples:

  • AI Incident Classification
  • AI Customer Service Assistant
  • AI Knowledge Search


AI Model

The model powering the AI system.

Examples:

  • OpenAI GPT
  • ServiceNow Now LLM
  • Azure AI Models


AI Dataset

The information used to train or evaluate AI models.

Examples:

  • Customer Records
  • Financial Data
  • Support Tickets


AI Prompt

Instructions used by Generative AI systems.

Examples:

  • Incident Summarisation Prompt
  • Knowledge Search Prompt
  • HR Policy Assistant Prompt


How to Create AI Assets


Step-by-Step

  1. Open AI Control Tower
  2. Navigate to AI Assets
  3. Select Create Asset
  4. Choose:
  • AI System
  • AI Model
  • AI Dataset
  • AI Prompt

5, Enter metadata:

  • Owner
  • Provider
  • Risk Classification
  • Business Function

6. Save and submit for review

This creates a governed AI record that can be monitored throughout its lifecycle.

Diagram showing AI Inventory integrated with ServiceNow CMDB, Business Services and Operations, featuring governance, visibility, and discov

Step 4: Configure AI Discovery and Populate the Inventory


Manually registering every AI asset is not scalable. AI Control Tower supports discovery and integrations to automatically identify AI assets.


Supported Discovery Sources

Examples include:

  • AWS Bedrock
  • Azure AI Foundry
  • ServiceNow AI Agents
  • SharePoint
  • Employee Centre Requests
  • External Data Sources


Configuration Steps


Configure AI Discovery

Navigate to:

  • AI Control Tower
  • Configurations
  • AI Discovery

Create a New Connection

Select Provider:

  • AWS
  • Azure
  • Other Supported Platforms

Enter:

  • Authentication Details
  • API Credentials
  • Discovery Schedule

Run Discovery

Review Imported Assets

The discovered assets automatically populate the AI Inventory, creating a more complete governance view.


Step 5: Establish AI Lifecycle Management


Once assets exist in the inventory, governance begins. AI Control Tower supports lifecycle management from intake through retirement.


Typical Lifecycle Stages


  1. New Asset Registration
  2. AI Steward Review
  3. Risk Assessment
  4. Compliance Validation
  5. Approval
  6. Deployment
  7. Ongoing Monitoring
  8. Retirement


Every AI system follows a documented governance process before entering production. This ensures governance becomes part of deployment rather than an afterthought.

AI governance workflow diagram showing 7-step process from registration to monitoring with continuous governance framework.

Moving Beyond AI Visibility to AI Accountability


Many organisations have already started their AI journey. Far fewer have established the governance structures needed to scale AI safely.

ServiceNow AI Control Tower helps organisations move beyond isolated AI projects by providing:

  • Centralised AI visibility
  • Governance and compliance controls
  • AI lifecycle management
  • Risk monitoring
  • AI inventory management
  • Business value tracking

For organisations pursuing Generative AI and Agentic AI at scale, AI Control Tower provides the governance foundation required to drive innovation while maintaining control.


Three Key Takeaways


1. AI Control Tower acts as a CMDB for AI

It provides a central inventory of AI systems, models, datasets, and prompts, giving organisations complete visibility into their AI estate.


2. Governance starts with ownership and roles

AI Stewards, Product Owners, and Risk & Compliance teams each play a distinct role in ensuring AI systems remain compliant and well-governed.


3. Discovery and lifecycle management are critical

Automatically discovering AI assets and managing them through structured governance workflows helps organisations scale AI responsibly and confidently.