The AI maturity model
Short answer
There are six levels between no AI and an AI-native operation. L0 no AI, L1 assistants, L2 workflow automation, L3 AI employees, L4 AI teams, L5 AI-native.
Levels are defined by control, not by tooling. What moves a company up is not a better model, it is data access, evaluation, and the amount the system is trusted to do without a human approving each step. Skipping a level is the most common way a programme stalls.
The axis is control
Each level trades human approval for machine autonomy. That trade, not the model, is what the six levels measure. Pick a level to read it.
L2 · 30% unsupervised
Workflow Automation
AI runs inside defined workflows with a human approving each step.
Next move. Add evaluation, then let the model act unsupervised where it scores highest.
The six levels
What each level looks like from the inside, and the one move that gets you to the next.
| Level | Name | What it looks like | Next move |
|---|---|---|---|
| L0 | No AI | Nothing in production. Possibly some individual ChatGPT use. | Pick one process with a measurable cost. Baseline it. |
| L1 | AI Assistants | Staff use general tools ad hoc. No company data, no governance. | Ground one assistant in your own documents with RAG, and measure answer quality. |
| L2 | Workflow Automation | AI runs inside defined workflows with a human approving each step. | Add evaluation, then let the model act unsupervised where it scores highest. |
| L3 | AI Employees | Agents own end-to-end processes and are measured like a team member. | Introduce a second agent and a shared task queue between them. |
| L4 | AI Teams | Multi-agent orchestration across departments, coordinating on shared state. | Consolidate evaluation, gateway routing, cost, and governance onto one internal platform. |
| L5 | AI-Native | New processes are designed for agents first, humans on exception. | You are ahead of your market. Protect it with proprietary data and evals. |
Where are you today?
Four questions place you on the ladder and name the next move. It takes twenty seconds, it runs entirely in your browser, and nobody sees your answers.
- L0
No AI
Nothing in production. Possibly some individual ChatGPT use.
- L1
AI Assistants
Staff use general tools ad hoc. No company data, no governance.
- L2
Workflow Automation
AI runs inside defined workflows with a human approving each step.
- L3
AI Employees
Agents own end-to-end processes and are measured like a team member.
- L4
AI Teams
Multi-agent orchestration across departments, coordinating on shared state.
- L5
AI-Native
New processes are designed for agents first, humans on exception.
Answer the four questions to see where you sit, and what the next level costs and returns.
How to use it
A ladder is only useful if it changes what you do on Monday.
Measure the level you are on, not the one you want
Most companies self-report a level higher than their evidence supports. The test is not what exists, it is what runs unsupervised and is measured. If nothing is measured, you are at L1 regardless of what is deployed.
Move one level at a time
The jump people attempt most often is L1 straight to L3: give an assistant real autonomy without building evaluation first. It fails, and it fails expensively, because there is no way to tell whether the agent is doing the job or quietly getting it wrong.
Evaluation is the gate on every level
Each step up trades human approval for machine autonomy. The only thing that makes that trade safe is a graded eval set. It is the reason L2 is where most programmes stop, and the reason it is worth building before you need it.
Know your level. Now pick the number.
A 30-minute call. We map one process at your current level, size the opportunity, and name the next move.