AI Maturity

AI maturity measures how far an organisation has progressed from isolated AI experiments to AI as a managed part of everyday operations, usually assessed across dimensions like strategy, data, people, technology and governance.

Business & strategyAI maturity modelAI maturity assessment
§ 01DefinitionAI Maturity

No jargon, no math

In plain words, what is it?

Maturity models exist because ambition is not progress. An assessment scores your organisation across a fixed set of dimensions: strategy (is there a funded plan, or a slide?), data (is it labelled, reachable, owned?), people (do teams have the skills and the mandate?), technology (do platforms and models run in production?), governance (are there rules that actually run?). Each dimension gets a level, typically from initial to managed to embedded. The value is not the score, it is the gap analysis: progress is capped by your weakest dimension, and the weakest is rarely the model. Organisations love to buy technology while their data is a swamp, and the assessment says so plainly. The output is a sequenced roadmap: fix the capping dimension first, climb one level, measure again in six months. An assessment that ends in a badge instead of a roadmap is marketing wearing a methodology's clothes.

§ 02How it worksAssess → level → find the cap → fix → remeasure
  1. 01

    Assess. Score each dimension: strategy, data, people, technology, governance.

  2. 02

    Level. Place each dimension on the maturity scale.

  3. 03

    Find the gap. The lowest dimension caps all the others.

  4. 04

    Sequence. The roadmap fixes the capping dimension first.

  5. 05

    Remeasure. Reassess on a fixed cycle and check the level moved.

§ 04

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