ServiceNow AI Control Tower: one register for every AI asset, with an owner, a lifecycle and an off switch.
Most organizations can name their AI pilots.
Far fewer can list every AI system in the business, who approved it, what data it reaches and what permissions it holds. ServiceNow AI Control Tower turns that list into a managed inventory, with owners, risk classification, approvals and oversight on every entry.
Skills and agents on the platform, models in a cloud provider account, embedded AI inside a software product procured last quarter. The result is shadow AI, untracked models and integrations nobody documented.
An AI system usually has a builder. It often has no business owner, technical owner or risk owner who knows they hold that role.
One team runs a formal review with legal. Another ships a summarization skill in an afternoon, correctly, because the risk is low. What is missing is the rule that decides which path applies.
Assessed once, approved, then running for months while the data changes and the permissions stay as broad as day one.
We build and run AI agents on ServiceNow and Claude, in our own operations and in client environments.
So we design AI governance from the side that has to live with it: named owners, clear decision rights, approval routes that fit the risk, and a way to stop an agent when it matters.
Govern the use case, not just the model.
Most AI governance conversations start with the model: which one, which provider, which region. Real questions, but not where the risk sits.
A model that drafts a summary for a person to read carries almost no operational risk. Give the same model a tool that can update a customer record, a production credential and permission to act without review, and it is a different proposition.
Nothing about the model changed. Everything about the risk did.
Risk follows the data a system reaches, the permissions it holds, how much autonomy it has and whether a human is in the loop.
Design control levels around those and a low-risk use case clears quickly, while an autonomous agent touching financial data gets real scrutiny.
Design them around the model and you block everything or wave everything through. Classify in the first conversation and governance speeds delivery up.
This is also why we design for a multi-model enterprise. Claude is our preferred intelligence layer, but the right model for a drafting task is rarely the right one for an agent with production credentials.
An asset register for AI, with a lifecycle and an approval chain attached.
AI Control Tower is a workspace and a set of applications for governing an AI portfolio. It inventories AI systems, models, prompts, datasets and MCP servers across ServiceNow and external platforms. Each entry carries ownership, an intended use and purpose, a risk classification, a compliance posture, a lifecycle phase with routed approvals, quality and safety scores, and measured productivity, cost and adoption, tracked against your AI strategy and goals.
You cannot govern what you cannot see.
AI Control Tower manages assets through lifecycle playbooks. The phases are onboard, assess, build and test, and deploy, with change and offboarding requests for modification and retirement.
The phase model, the approval requests and tasks that drive it, the governance record that tracks state, and the option to add phases.
Which risk classes need which approvals, who sits in each step, how often systems are reassessed, how exceptions are granted and what evidence is sufficient.
Not sure what AI is running in your organization today?
We map it with you in one workshop.
Every agent needs an owner, a limit and an off switch.
AI Control Tower sets all three.
Classification, controls and the paper trail.
Risk classification covers AI systems, models and datasets, as low, medium, high or unacceptable, plus "to be determined" for anything unassessed.
Risk posture plots inherent or residual risk against control effectiveness, which shows what your controls are actually worth. Compliance is scored from control attestations, and each asset carries its own assessments, risks, controls, issues and exceptions.
Worth knowing: unassessed assets are excluded from the risk charts entirely, so a clean dashboard can mean a well-governed portfolio or one nobody has assessed yet.
Ownership is structural, not a label. ServiceNow installs an AI Steward role, an AI Asset Owner role and four levels of AI Risk and Compliance role.
Data location.
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These AI applications send data from your instance to a central ServiceNow environment, which can sit in another region and use a third-party cloud provider. Availability is restricted for in-country editions and regulated markets. We map the data flows in week one and design for your residency position before a security review tests it.
Excessive access.
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Every agent gets least-privilege roles, reviewed against the privileged and dormant agent lists.
Unregistered connections.
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Where agents reach external tools through Model Context Protocol, every MCP server is registered through AI Gateway and passes an approval playbook before an agent can use it. An unregistered connection is governed by nothing.
Need to establish your obligations under the EU AI Act first? That is our EU AI Act Readiness work.
It does not replace your risk function. It gives it something to govern.
Want to see where your governance gaps are before an audit finds them?
Inventory first. Ownership second. Automation last.
Active is not the same as effective. We review live instances where AI Control Tower is installed and fully current, with not a single MCP server published to it. The platform provides the inventory, the lifecycle, the routing and the audit trail. Decision rights, ownership, risk criteria and escalation paths are not configuration. They are decisions, and we work through them with you.
ServiceNow-native AI only, or external platforms and embedded software AI too. The answer changes the licensing conversation.
Discovery, connectors and a manual process for the rest. Then decide which assets move from unmanaged to managed, and in what order.
Business, technical and risk owner per asset, confirmed with the people named.
Where most programs stall, and not a technical task.
On what each system can do, not which model it uses.
Connect existing policies and controls to AI assets instead of writing a parallel AI rulebook nobody reads.
Lifecycle phases, approval routing per risk class, evidence requirements, exceptions and retirement, configured to your decision rights.
Quality and safety scoring where it earns its place, security signals, a review cadence and a named containment path.
Start with what you run today. Scope follows.
Every AI portfolio is different, so we do not start from a package. We start by understanding what you run, what it touches and who needs to decide, and shape the engagement around that.
We map what AI you run today, where ownership is missing and which assets to take through the lifecycle first.
You leave with a first cohort and the decisions it needs.
We take that first cohort through the lifecycle on your instance, with real owners and real approvals, and set up the roles, workflow and dashboards around it.
Scope and timeline follow from the workshop, not from a template.
After go-live, governance is weekly work: onboarding new use cases, running approvals, reviewing exceptions and producing evidence.
Your team runs it, or we run it with you through Governance as a Service.
The partner that builds AI agents and governs them.
Pulse & Patterns designs and implements AI Control Tower for organizations across Europe that are putting AI to work faster than they can govern it.
We are a ServiceNow Consulting & Implementation and Resell Partner and an Anthropic Claude Consulting & Implementation Partner. ServiceNow carries the workflows, approvals and system of record for your AI portfolio. Claude is the intelligence layer we build on.
The people who design your governance model are the people you meet first. No account layer, no bench. They bring instance reviews that document the gap between installed and effective, our own published AI governance gates, and a live risk register for AI running on a client platform.
Govern your first AI assets properly, then scale.
In one workshop we map what AI you run today, where ownership is missing and which assets to take through the lifecycle first. You leave with a clear first cohort and the decisions it needs.