The AI maturity model
Short answer
An AI maturity model ranks how much work a company trusts AI to do without a human approving each step. This one has six levels: 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. Two companies on the same model from the same vendor can sit at L1 and L3. The difference is whether the system can reach company data, whether its output is measured against a graded eval set, and how much it is allowed to do unsupervised.
What moves a company up a level is data access and evaluation, not a better model. Skipping a level is the most common way a programme stalls: autonomy granted before evaluation exists leaves nobody able to tell whether the system is doing the job or quietly getting it wrong.
What separates one level from the next?
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.
What are 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 the single next move from there. Everything runs in your browser; nothing is sent.
How should you use a maturity model?
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.
No evaluation: the fault behind the jumpEvaluation 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.
What a graded eval set involvesKnow 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.
hello@venian.aiReply within one business day, from an engineer.
What happens next
- 01
A reply within one business day
From an engineer who would work on it, not a sales team.
- 02
A 30-minute call
We map one process, name the metric it should move, and tell you plainly whether AI is the right tool for it.
- 03
A written scope
If it is, a one-page scope: the metric, how we baseline it, the target, and which engagement fits.
- For
- Teams with an LLM feature in or near production, or a pilot that stalled, and a number it should move.
- Not for
- Chatbot-on-a-website projects, or strategy decks with no build behind them.