AI maturity model is a staged scale that places an organization somewhere between isolated experiments and AI embedded in core operations. Most versions use five levels across a handful of dimensions, typically strategy, data, technology, skills and governance, and are used to decide what to fix next.
What are the stages, and what actually distinguishes them?
The common progression runs from ad hoc experiments, through proofs of concept, to systems running in production, then AI inside core processes, and finally continuous improvement of those systems. What separates the levels is not how much AI you use but how little of it depends on individual enthusiasm. A pilot that only works while its champion is in the room is level one behavior no matter how good the demo is.
How do you use a maturity score without fooling yourself?
Every consultancy publishes a model, and they broadly agree, so the framework is not the scarce part: honest inputs are. Score by function rather than company-wide, and treat the lowest dimension as the constraint, because maturity is limited by whichever of strategy, data, skills or governance is weakest. In practice the answer is usually data, which is why data readiness for AI tends to be the first real workstream. Use the result to sequence an AI roadmap; use AI readiness to decide whether the next project can start, and keep both anchored to a stated AI strategy.
Frequently asked questions
How is a maturity model different from an AI readiness assessment?
Readiness asks whether you can start a specific piece of work. Maturity places your whole organization on a scale over time. Readiness is a gate before one project; maturity is a direction of travel across many.
Is a single company-wide maturity score useful?
Rarely. Averaging a strong engineering function with a department that has never used AI produces a middling number that describes nobody. Score by function or team, then act on the variance, which is usually where the blockers actually are.
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