AI inventory and discovery approach
A populated register of AI agents, models, skills and use cases, plus a repeatable method for finding the ones nobody declared — including embedded vendor AI.
ServiceNow AI Solutions
AI Control Tower Implementation and Governance
Stand up a single inventory of AI agents, models and use cases on ServiceNow, with the policy, approval and audit workflows that make it governable.
What it is
ServiceNow AI Control Tower is the platform’s command center for AI. In general terms, it is designed to give you one place to see the AI agents, models and use cases operating across the enterprise, apply policy and risk controls to them, and track their lifecycle and value from idea through retirement.
Our job is to make that real in your environment. That means populating the inventory with what you actually have — including AI that was bought, built elsewhere, or quietly enabled inside a SaaS product — then wiring the workflows that decide what gets approved, what gets reviewed, and what evidence gets kept.
An inventory nobody maintains is worse than no inventory, so we spend as much time on the operating routine as on the configuration: who updates records, what triggers a review, and which gate a new use case has to pass before it reaches production.
Who it is for
- Enterprises with AI use cases appearing faster than any central team can track
- Platform owners asked by risk, audit or legal for a defensible list of AI in use
- Organizations in regulated industries that need approval and evidence trails for AI decisions
- CIOs who need to know what AI spend is buying before approving the next wave
What is included
The deliverables below are the default scope. We adjust them to your situation during scoping and confirm the final list in writing.
Policy and risk workflow configuration
Risk tiering applied to use cases, with the review depth, controls and documentation each tier requires.
Approval and intake workflows
A single intake path for new AI use cases, routed to the right technical, security, privacy and business approvers, with decisions recorded.
Compliance and audit evidence
What is captured for each use case — purpose, data in scope, human oversight, approvals, testing, change history — so an auditor can follow it without interviewing you.
Lifecycle management
Stages from proposed through piloted, live, under review and retired, with the criteria and owner for each transition.
Value tracking dashboards
Adoption, usage, cost and outcome measures per use case, with baselines captured before go-live so the comparison means something.
Process and timeline
Indicative timings for a typical engagement of this type. Your scope, environment and availability of stakeholders will move them.
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Discover and baseline
Find the AI already in use, document owners and data flows, and record the current state honestly — including the use cases that are not working.
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Design the operating model
Risk tiers, roles, intake path, review board cadence and evidence requirements, agreed with risk, security, privacy and legal before anything is configured.
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Configure
Build the inventory structure, workflows, approvals, dashboards and notifications in a sub-production instance.
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Pilot on real use cases
Run three to five live use cases through the full path and fix what the pilot exposes. Governance that has never been used is still a hypothesis.
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Operationalize
Train the owners and reviewers, publish the routine, and hand over with a documented review calendar.
Outcomes we aim for
These are the goals we design the engagement around. They are objectives, not guarantees, and the result depends on decisions and effort on your side as much as ours.
- One answer to "what AI are we running, and who owns it" — a goal we design toward, not a guarantee
- A documented approval trail for each AI use case in production
- Earlier visibility of duplicate or abandoned AI efforts
- Cost and adoption data that makes the next funding conversation evidence-based
A note on accuracy. ServiceNow releases change quickly. Treat the capability descriptions here as a general guide and confirm current features, licensing and prerequisites against the official ServiceNow product documentation for your release.
Questions
About ai control tower implementation and governance
What is AI Control Tower?
It is ServiceNow’s control center for enterprise AI. Broadly, it provides a single place to inventory AI agents, models and use cases, apply governance and risk controls, manage their lifecycle, and report on adoption, cost and outcomes. Specific capabilities vary by release, so confirm the detail in the official ServiceNow documentation for the version you run.
Do we need it if we already use Now Assist?
They solve different problems. Now Assist delivers AI capability inside workflows; AI Control Tower governs and measures AI across the estate, including AI that did not come from ServiceNow. One or two Now Assist use cases may not justify a governance build. Once you have several, plus AI from other vendors, the inventory and approval problem is usually already real.
How long does governance setup take?
A focused implementation covering inventory, intake, approvals, risk tiering and initial dashboards typically runs ten to fourteen weeks, with a pilot inside that window. A large organization with many business units, or one with heavy regulatory evidence requirements, should expect longer and should phase it.
Can it track AI that is not built on ServiceNow?
That is much of the point. External models, third-party agents and AI features embedded in other SaaS products can be represented in the inventory and governed through the same intake and review path. How much telemetry you get back from an external tool depends on what that tool exposes.
What do you need from us to start?
A named owner on your side, access to a sub-production instance, and the right people from security, privacy and risk in the design sessions. Without those three, the configuration can be built but the governance will not hold.
Is ai control tower implementation and governance the right next step?
Tell us the symptom rather than the solution. If a different service fits better — or none of ours does — we will say so.