Enzai calibrates review depth to actual risk rather than treating every AI use case the same. Low-risk use cases run a 24-hour fast-track approval; customer-facing decision agents enter the cross-functional deep review. Capacity goes where it matters.

Yes. Reviews and approvals push into Jira, ServiceNow, and Slack natively. Inventory data syncs with AWS, Azure, GCP, Databricks, Watsonx, and Snowflake. Model governance covers OpenAI, Anthropic, and proprietary internal models through the same platform.

Shadow AI surfaced through Enzai's discovery is brought back into governance via a one-click fold-in path that runs a retroactive intake without treating the user as a violator. The intake process gets used the next time, rather than routed around.

Live dashboards aggregate every intake, approval, and inventory entry as it happens. Leaders see use cases by stage, by risk tier, by business unit, by time-to-decision, updated continuously rather than reconstructed at the quarterly steering committee.

Enzai tracks adoption velocity, time-to-approval, fast-track success rate, Shadow AI surface rate, and cross-functional reviewer load. Trend lines show whether the programme is accelerating, bottlenecks are forming, or governance is keeping pace with the rate of adoption.

Most teams see meaningful gains within the first quarter. Time-to-approval drops as the platform learns your risk patterns; Shadow AI discovery surfaces in week one; leadership visibility goes live as soon as the initial inventory is populated, typically inside 30 days.