Exploring AI Control Tower
Learn how AI Control Tower provides centralized visibility and governance across your organization's AI portfolio, connecting strategic planning, asset discovery, monitoring, compliance, and value measurement in a single workspace.
AI Control Tower overview
As your organization deploys AI systems across business units, from generative AI assistants and agentic workflows to classical machine learning models, the challenge shifts from building AI to governing it. AI Control Tower addresses this by providing a centralized workspace where cross-functional teams can manage the full AI lifecycle.
- Maintain a complete, current inventory of AI systems, models, prompts, and tools across ServiceNow and external platforms including AWS, Azure, and Google Cloud.
- Detect quality and safety regressions before they escalate, using automated scoring and trend analysis for AI interactions in production.
- Track regulatory compliance posture against frameworks like NIST AI RMF and EU AI Act across your entire AI portfolio.
- Quantify the business value of AI investments with productivity, cost savings, and adoption metrics tied to individual AI systems.
- Coordinate governance work across cross-functional teams with lifecycle playbooks, approval routing, and AI-generated recommendations.
AI Control Tower users
| User | Description |
|---|---|
| AI steward | Oversees the state of AI across all inventory, including value and adoption insights. Reviews quality and safety scores, monitors risk and compliance posture, investigates cases and inquiries, and coordinates with AI system owners to maintain governance standards. |
| Administrator | Activates and configures AI Control Tower plugins, manages data sharing and multi-instance settings, configures AI model providers and data routing, sets up playbook templates, and maintains users and roles. |
| AI asset owner / Product owner | Manages the AI assets they own, views value and adoption metrics for their systems, tracks lifecycle status, and creates or responds to AI cases and approval requests. |
| Risk and compliance user | Monitors risk classifications, reviews compliance posture against authority documents and policies, manages risk assessments, and tracks regulatory changes affecting AI assets. |
AI Control Tower workflow
The following workflow shows how different users work together in AI Control Tower to take an AI asset from strategic planning through ongoing value measurement.
- Plan AI strategy. Strategic planners and AI stewards define AI priorities, set measurable goals, and track planning items and costs through integrations with Goal Framework, Strategic Planning, and PPM Standard.
- Build the AI asset inventory. AI stewards bring AI assets into the inventory through three discovery and registration mechanisms. Discovery automatically syncs ServiceNow AI assets — including skills, models, prompts, datasets, agents, and tools — into AI Control Tower. Hyperscaler connections find AI assets across external platforms like AWS Bedrock, Azure AI Foundry, Copilot Studio, and Google Cloud Platform Vertex AI. AI stewards register Model Context Protocol (MCP) servers through AI Gateway, where each registration is routed through an approval playbook before the server joins the inventory and benefits from AI Gateway's governance, observability, and security controls.
- Manage assets through the lifecycle. AI stewards and asset owners take each asset through lifecycle playbooks — onboard, assess, build and test, and deploy — with the right approvals routed to the right reviewers at each phase. Lifecycle playbooks are how governance, risk, and compliance checks get applied operationally as an asset progresses toward deployment.
- Govern AI assets. Risk and compliance users classify assets against frameworks like NIST AI RMF and EU AI Act and track regulatory posture across the inventory. AI stewards maintain AI security and privacy controls, including agent identities, access controls, privileged agent reviews, and dormant system reviews. Governance activities continue throughout an asset's lifecycle, not just at onboarding.
- Monitor AI in production. The evaluation engine scores AI interactions automatically against quality and safety metrics. AI stewards review scores and trends, investigate regressions in low-scoring sessions, and coordinate with AI system owners to remediate issues by adjusting agent configuration, prompts, or tools.
- Measure value and adoption. AI asset owners and AI stewards track value realization — productivity gains, cost savings, and user adoption — across the AI portfolio, then identify optimization opportunities to improve return on AI investments.
AI Control Tower benefits
| Benefit | Feature | Users |
|---|---|---|
| Review your AI portfolio at a glance — top priority actions, inventory composition, governance posture, regulatory risk, realized value, and outstanding tasks — from a single landing page. | Home | AI steward, AI asset owner |
| Track governance work across your team — your assigned items, team workload, unassigned queue, and AI-generated recommendations — in one workspace. | Activity Center | AI steward, AI asset owner |
| Focus on the highest-priority governance work automatically surfaced across the product, and resolve routine issues with supervised or unsupervised AI agents. | Recommendations and AI insights | AI steward, AI asset owner |
| View and update every aspect of an AI asset — details, lifecycle phase, risk and compliance posture, security findings, monitoring scores, and realized value — from a single record page. | Working with AI asset records | AI steward, AI asset owner |
What to explore next
To learn more about each area of AI Control Tower, see the following topics: