Exposed
Discover shadow AI, centralize governance, enforce the AI Acceptable Use Policy, block risky AI, establish a sanctioned workspace, and score the baseline.
ITS AI Maturity Model
Five stages of AI maturity, from exposed to intelligent.
Score where you stand. Know what it means. Measure outcomes.
Explore the model
Compare all five stages.
Managed AI stage
01
Exposed
AI use is invisible and ungoverned.
What this stage means
Employees are already using AI. Leadership just cannot see it.
You know you're here when
No one can name every AI model or AI site in use across the company. Real work moves through personal accounts and free tools. Work product accumulates in AI accounts the company does not own and cannot audit. There is no written AI policy, and nothing is enforced in the browser.
Implement the following
- Shadow AI discovery
- Central AI governance
- AI Acceptable Use Policy
- Risky AI blocked
- Sanctioned workspace
- Scored baseline
Measured outcome
Governance & Policy and Platform & Tooling improve.
The measurable path
Implement what moves the business to the next stage.
Stages move on process, policy, procedure, and the platform behind them.
Governed
Match catalog solutions to named roles, connect business tools, establish a governed knowledge repository, measure adoption, and partner with an AI Business Analyst.
Productive
Add managed MCP connections, human approval paths, assisted application connections, and cycle time tracking.
Automated
Add a managed RAG server, managed private LLM, prompt-level oversight, full catalog access, and quarterly re-scoring.
Intelligent
Re-score quarterly, name the weakest dimension, maintain knowledge coverage, keep prompt review current, and train internal champions.
Maturity is one part of Managed AI
See how the model fits into the full Managed AI environment.
Use maturity to understand the operating environment, then evaluate solution types, connections, architecture, and implementation around the business need.