AI Control Tower
Every AI agent and model in your enterprise on one inventory — owner, risk tier, policy, and cost, against EU AI Act and ISO 42001.
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. The same evidence base regulators are starting to ask for under the EU AI Act and ISO 42001.
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 behind them
- AI spend fragmented across SaaS contracts, model providers, and embedded features, with no per-use-case cost visibility
- Performance and drift of production AI use cases monitored ad-hoc, late, or not at all
- Approval, change, and incident workflows for AI sitting outside the platform that already 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. Every AI use case becomes 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 keeps growing faster than the team governing it.
[ How MainStack delivers ]
We start with an AI usage discovery exercise: catalogue every Now Assist skill, every ServiceNow AI 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, ISO 42001, and your internal AI policy, performance and cost telemetry from model providers, and the workflows that route AI-related approvals, change, and incidents 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 — and survives the next supervisory review.
[ What We Deliver ]
AI Usage Discovery & Baseline
Discovery of Now Assist skills, ServiceNow AI Agents, third-party copilots, embedded model calls in custom apps, and vendor-managed AI features in procurement records — 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 named control with a named owner.
Telemetry & Performance Monitoring
Usage, cost, latency, and quality telemetry from Now Assist, Workflow Studio, and external model providers, surfaced in operational dashboards tied to model owners and feeding drift alerts to the governance function.
Governance Workflows
Approval, change, incident, and exception workflows for AI use cases, running on the same ITSM, IRM, and procurement processes that govern the rest of the platform.
AI Governance Operating Model
Role design for the AI governance function, evidence packs for audit and supervisory review, training for the people who will own the control environment day-to-day, and a continuous-improvement cadence that survives executive turnover.
Ready to get started with AICT?
30-minute discovery call. No pitch deck. We'll tell you honestly if we're the right fit.
Book a Discovery Call