AI Control Tower
Inventory, govern, and measure every AI agent and model running across your enterprise — on ServiceNow.
Book a Discovery CallAI Control Tower is the governance layer for the AI estate your business is already accumulating: Now Assist skills, ServiceNow AI Agents, third-party copilots, embedded model calls in custom apps, and the shadow tooling product and engineering teams haven't told IT about yet. MainStack implements AI Control Tower as the single record of every AI use case running on or through your platform — what it does, who owns it, what it costs, how it performs, and which policy it sits under.
Common Challenges We Solve
- No single inventory of AI use cases, models, or agents running across the enterprise
- EU AI Act, ISO 42001, and internal AI policy obligations with no evidence trail to prove compliance
- AI spend fragmented across SaaS contracts, model providers, and embedded features, with no usage-level cost visibility
- Performance and drift of production AI use cases monitored ad-hoc or not at all
- Approval, change, and incident workflows for AI sitting outside the platform that governs every other technology decision
[ Why it matters ]
Every enterprise has more AI in production than its CIO can list. Skills inside Now Assist, agents inside Workflow Studio, copilots wired into Slack and Teams, embedded model calls in custom-built applications, and an unknown quantity of vendor-managed AI features inside SaaS tools the business bought without IT involvement. None of it sits in a single inventory. Legal cannot prove which use cases comply with the EU AI Act. Security cannot evidence which models touch sensitive data. Finance cannot reconcile what is being spent on AI capability or measure return.
AI Control Tower closes that gap on ServiceNow. It treats every AI use case as a governed configuration item with a defined owner, a classified risk tier, a measurable performance baseline, and a policy attached. The same platform that runs IT operations, risk, and procurement becomes the system of record for AI usage — which is the only way governance scales when the model estate doubles every quarter.
[ How MainStack delivers ]
We start with an AI usage discovery exercise: catalogue every Now Assist skill, every ServiceNow agent, every third-party copilot, every embedded model call we can find inside custom apps, and every vendor-managed AI feature surfaced through procurement records. The output is a baseline inventory and a risk-classified prioritisation of which use cases need policy, monitoring, and governance attached first — typically the ones touching personal data, financial decisions, or customer-facing decisioning.
Implementation covers AI Control Tower configuration, integration with Now Assist Admin Console and Workflow Studio, policy and risk-tier modelling aligned to the EU AI Act and your internal AI policy, performance and cost telemetry from model providers, and the workflows that route AI-related approvals, change, and incident through the same governance the rest of the platform uses. Handover includes a trained AI governance function, documented policy artefacts, and the measurement cadence that proves the control environment is working.
[ What We Deliver ]
AI Usage Discovery & Baseline
End-to-end discovery of Now Assist skills, ServiceNow agents, third-party copilots, embedded model calls, and vendor-managed AI features, captured as a governed baseline inventory.
Policy & Risk-Tier Modelling
AI policy framework, risk classification aligned to the EU AI Act and ISO 42001, and policy-to-use-case binding so every AI capability sits under a documented control.
Telemetry & Performance Monitoring
Usage, cost, latency, and quality telemetry from Now Assist, Workflow Studio, and external providers, surfaced in operational dashboards and tied to model owners.
Governance Workflows
Approval, change, incident, and exception workflows for AI use cases, integrated with the same ITSM, IRM, and procurement processes that govern the rest of the platform.
AI Governance Operating Model
Role design for AI governance, evidence packs for audit and regulatory review, training for the function that owns the control environment, and a continuous-improvement cadence.
Ready to get started with AICT?
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