AI Without Chaos: A Deep Dive into AI Control Tower
As enterprises increasingly adopt Artificial Intelligence across IT, HR, Customer Service, and Operations, the biggest challenge is no longer how to build AI, but how to govern, track, and operate AI responsibly at scale.
In many organizations, AI is introduced gradually—through agents, workflows, integrations, or vendor platforms. Over time, this leads to a fragmented AI landscape where visibility and control are limited. As AI adoption grows, teams often struggle to answer basic yet critical questions:
Which AI systems are running in production?
Who approved them?
What data do they use?
Are they compliant with regulations?
ServiceNow AI Control Tower is designed to answer these questions by providing a centralized, enterprise-grade framework for AI governance.
What Is AI Control Tower?
AI Control Tower is a centralized workspace on the ServiceNow platform that provides visibility, governance, and lifecycle management for all AI initiatives within an organization.
It does not build AI models or agents. Instead, it governs AI across its entire lifecycle—from the moment an idea is proposed to the point where the AI system is deployed, monitored, and eventually retired.
So, here’s the deal :
If CMDB helps you understand what applications you run,
AI Control Tower helps you understand what AI you run, why it exists, who owns it, and whether it is safe and compliant.
Key capabilities include:
A unified AI Asset Inventory
Structured AI lifecycle enforcement
Integrated risk and compliance workflows
Dashboards for adoption, value, and health
End-to-end auditability
Why AI Control Tower Is Needed
Without centralized governance, AI adoption often becomes uncontrolled and fragmented. Teams may deploy AI directly into production without a shared approval process, and governance teams are often involved only after issues arise.
Without AI Control Tower:
AI is deployed directly to production
Ownership is unclear
No approval or audit trail exists
Regulations cannot be proven compliant
This creates significant operational, legal, and reputational risks.
With AI Control Tower:
Every AI initiative is visible
Governance happens before deployment
Risks are assessed early
AI value can be measured
AI Control Tower enforces a simple but critical rule:
No AI should exist in production unless it is known, approved, and continuously monitored.
AI Asset Inventory — The Foundation of AI Governance
The AI Asset Inventory is the core foundation of AI Control Tower. It is a structured registry that stores metadata and relationships for everything related to AI across the enterprise.
It is important to clearly understand what the inventory represents—and what it does not.
AI Control Tower is not a data lake, model registry, or execution engine.
What the AI Asset Inventory DOES Store
Records describing AI assets
Relationships between AI components
Lifecycle phase and lifecycle status
Risk, value, and ownership metadata
This design allows AI Control Tower to govern AI without interfering with how AI is built or executed.
Types of AI Assets Tracked
- AI Use Cases
AI Use Cases capture business intent. They describe why AI is needed, what problem it solves, and what value is expected. Every AI initiative must begin here. - AI Systems / AI Agents
These represent operational AI capabilities such as Now Assist agents, AI Agent Studio agents, and autonomous agents. They define how AI interacts with users and systems. - AI Models
Models are the decision-making engines behind AI systems. Tracking model versions, providers, and documentation is essential for explainability, audits, and regulatory compliance. - Datasets
Datasets determine the risk profile of AI. Metadata about data sources, sensitivity (PII/PHI), ownership, and retention enables privacy and compliance governance. - Prompts
Prompts define AI behavior in generative systems. Because prompt changes can significantly alter outputs, they are treated as versioned and governed artifacts. - Skills, Workflows, and Integrations
Any workflow or skill that invokes AI logic becomes part of the AI footprint and must be tracked in the inventory. - AI Inquiries
AI Inquiries represent questions, clarifications, or information requests related to AI assets. They are used when guidance or confirmation is required, but no issue or incident has occurred. Tracking AI Inquiries in the AI Asset Inventory ensures transparency, traceability, and governance awareness around AI-related decisions and discussions.
AI Use Case — Why Everything Starts in Employee Center
An AI Use Case is the formal intake mechanism for AI governance. It is intentionally business-focused rather than technical.
An AI Use Case:
Represents a business request for AI
Triggers governance and approval workflows
Acts as a portfolio and tracking anchor
It is not:
An AI agent
A model
A workflow
Employee Center is used for intake because it:
Standardizes AI requests
Allows non-technical users to propose AI
Immediately activates governance processes
Maintains a complete audit trail
Once submitted, the AI Use Case becomes the parent record for all AI assets created later.
The item can be accessed in the Employee Center under Technology Services > AI Assets.
What Happens When an AI Use Case Is Submitted
When a use case is submitted:
A record is created in the AI Asset Inventory
The lifecycle phase is set to New
AI Steward and AI Asset Owner are assigned
Risk and impact assessments are triggered
The use case appears in AI Control Tower dashboards
Development cannot proceed until approvals are completed
Conceptually, an AI Use Case functions like a project charter for AI initiatives.
AI Asset Lifecycle — How Governance Is Enforced
AI Control Tower enforces a structured lifecycle to ensure AI initiatives cannot bypass governance controls.
The lifecycle actively governs:
What actions are allowed
Which tasks must be completed
When AI can move to production
What appears on dashboards
Lifecycle Phases Explained
- New
The AI idea is captured and awaits AI Steward review. - Assess
Risk, impact, and data sensitivity are evaluated. Applicable policies and controls are identified. - Build and Test
AI is developed outside Control Tower, but evidence, documentation, and approvals are enforced within it. - Deploy
Final production approval is granted. Monitoring, adoption, and value tracking begin. - Operate / Monitor
AI behavior is continuously observed to detect drift, bias, or performance degradation. - Retire
The AI system is decommissioned, and the audit trail is preserved.
End-to-End Flow: From AI Use Case to Deployment
The end-to-end flow begins when a business user requests an AI Use Case and ends when the AI system is deployed and monitored in production.
Requesting an AI Use Case
Record Producer: Request an AI Use Case
Required role: sn_grc_ai_gov.ai_risk_and_compliance_business_user
This ensures AI initiatives originate from real business needs rather than ad-hoc experimentation.
Reviewing and Governing the Use Case
An AI Accountable Officer (typically aligned with AI Stewardship) reviews the request, assigns required assessments, and designates an AI Asset Owner.
Requested AI Use Case Lifecycle
Phase | Lifecycle Status | Description |
New | AI Steward Review | Intake validation |
Assess | In Review | Risk and impact evaluation |
Build & Test | Approved for Development | Concept approved |
Build & Test | Ready for Development | Assessments complete |
Deploy | Ready for Deployment | Final approval |
Deploy | Deployed | Live in production |
If rejected:
Lifecycle Status: Rejected
State: Cancelled
Roles & Responsibilities
AI Control Tower uses strict role-based access control, ensuring that each persona has clearly defined responsibilities and governance boundaries. This separation of duties is critical to maintaining accountability, compliance, and operational clarity across the AI lifecycle.
AI Steward
Role: sn_ai_governance.ai_steward
The AI Steward is the primary governance authority for AI Control Tower. This role is responsible for configuring the workspace, enforcing governance practices, and coordinating across teams.
Responsibilities include:
Configure the AI Control Tower workspace
Manage and govern the AI Asset Inventory
Drive adoption of AI governance practices
Define policies and approval playbooks
Manage the AI asset lifecycle end-to-end
Configure third-party LLMs and SLMs
Configure multi-instance governance
Activate and run hyperscaler discovery
Coordinate with Risk, Legal, and Security teams
AI Control Tower Workspace User
Role: sn_ai_governance_workspace_user
This role provides operational visibility into AI Control Tower for users who manage or contribute to AI initiatives.
Responsibilities include:
Access the AI Control Tower home page
View and manage assigned AI assets
Access the AI Portfolio tab
AI Asset Owner / Product Owner
Role: sn_ai_asset_mgmt.ai_asset_owner
The AI Asset Owner is accountable for the accuracy, lifecycle progression, and value realization of assigned AI assets.
Responsibilities include:
Maintain accurate AI asset data
Manage the AI asset lifecycle from intake to retirement
Create AI assets within AI Control Tower
Track value and adoption metrics
Complete deploy phase lifecycle tasks
Act as the accountable owner for assigned AI assets
AI Risk & Compliance Roles
These roles ensure that AI systems comply with internal policies, regulatory requirements, and risk management standards.
AI Risk and Compliance Admin
Role: sn_grc_ai_gov.ai_risk_and_compliance_admin
This role configures and maintains the risk and compliance frameworks that govern AI usage.
Responsibilities include:
Configure risk and impact assessment frameworks
Define automation rules for assessments
Manage control frameworks and libraries
Configure AI case types
Delete AI systems when required
AI Risk and Compliance Manager
Role: sn_grc_ai_gov.ai_risk_and_compliance_manager
This role oversees AI risk and compliance activities across the organization.
Responsibilities include:
Access all AI systems
Initiate impact assessments
Initiate risk assessments
Initiate control attestations
Manage the compliance lifecycle of AI systems
AI Risk and Compliance Analyst
Role: sn_grc_ai_gov.ai_risk_and_compliance_analyst
This role executes assessments and attestations for assigned AI systems.
Responsibilities include:
Work on assigned AI systems
Perform risk and impact assessments
Execute control attestations
Update lifecycle status for assigned assets
AI Risk and Compliance User
Role: sn_grc_ai_gov.ai_risk_and_compliance_business_user
This role supports governance operations and participates in compliance activities.
Responsibilities include:
Create AI cases
Complete assigned compliance tasks
Perform control attestations
AI Risk and Compliance Reader
Role: sn_grc_ai_gov.ai_risk_and_compliance_reader
This role provides read-only access for audit and visibility purposes.
Responsibilities include:
Read-only access to AI systems
Read-only access to AI assessments
AI System Reader
Role: sn_grc_ai_gov.ai_risk_and_compliance_ai_system_reader
This role allows visibility into AI systems across governance workspaces.
Responsibilities include:
Read-only access to AI systems in:
AI Control Tower workspace
AI Risk & Compliance workspace
AI Case Management Roles
These roles support AI-related cases and inquiries raised within the organization.
AI Case Business User
Role: sn_ai_case_mgmt.ai_case_business_user
Responsibilities include:
Create AI cases and inquiries
AI Case Analyst
Role: sn_ai_case_mgmt.ai_case_analyst
Responsibilities include:
Work on assigned AI cases
Identify impacted risks and policies
Perform root-cause analysis
AI Case Manager
Role: sn_ai_case_mgmt.ai_case_manager
Responsibilities include:
View and manage all AI cases and inquiries
Assign cases and monitor resolution
AI Case Admin
Role: sn_ai_case_mgmt.ai_case_admin
Responsibilities include:
Configure AI case types and assignment rules
Delete AI cases
Perform full AI case administration
Generative AI Data Governance (Outside AI Control Tower)
Generative AI Data Steward
Role: sn_generative_ai.data_steward
This role governs data usage for Generative AI features such as Now Assist.
Responsibilities include:
Manage Now Assist and GenAI data sharing
Control privacy and opt-in/opt-out settings
Govern GenAI data processing policies
Dashboards — Measuring AI Success
Dashboards in AI Control Tower provide leadership and governance teams with real-time visibility into AI adoption and risk posture.
They help answer questions such as:
Are AI systems compliant?
Are they being adopted?
Are they delivering measurable value?
Are risks increasing or decreasing?
Dashboard categories include:
Governance and risk posture
Business value and ROI
Adoption and usage trends
Technical health and stability
These dashboards are essential for executive reporting and regulatory evidence.
Integrations
AI Control Tower integrates with both ServiceNow-native and external platforms to ensure comprehensive AI visibility.
Common integrations include:
AI Agent Studio
Now Assist
Hyperscalers (Azure, AWS, GCP)
Third-party LLM providers
Best Practices
Always start with an AI Use Case
Assign ownership early
Register AI assets before deployment
Parallelize approvals where possible
Track value from day one
Maintain human oversight for high-risk AI
Common Mistakes
Skipping AI Use Case intake
Allowing shadow AI
Ignoring lifecycle enforcement
Treating AI like basic automation
Not planning rollback or override mechanisms
Bringing It All Together
AI Control Tower transforms AI from uncontrolled experimentation into a governed, auditable, and value-driven enterprise capability.
When implemented correctly:
Every AI asset is visible
Every decision is traceable
Every risk is governed
Every outcome is measurable
