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.
No jargon, no math
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.
Assess. Score each dimension: strategy, data, people, technology, governance.
Level. Place each dimension on the maturity scale.
Find the gap. The lowest dimension caps all the others.
Sequence. The roadmap fixes the capping dimension first.
Remeasure. Reassess on a fixed cycle and check the level moved.
AI readiness measures how prepared an organisation's data, processes, people and governance are to run AI in production, and it is assessed before any model is deployed.
ReadAn AI strategy is the plan that turns AI from scattered experiments into a coordinated capability: which processes you automate, in what order, with what data, budget and governance, and when you stop.
ReadAI governance is the set of policies, roles and controls that decide how an organisation may build, buy and use AI: who approves a use case, what data a model may see, how incidents are handled, and who is accountable when something goes wrong.
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We design and ship AI systems with permissions, logs and human approval. It starts with a 30-minute scoping call.