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.
No jargon, no math
Readiness is a checklist of four gates. Data: can software actually read what you work with, or does it sit in binders, scans and inboxes? Process: is the target workflow documented, or does every senior operator carry it in their head? People: will users trust the system enough to correct it, and is there a named owner for when it errs? Governance: who signs off, who audits, who can switch it off? You score each gate honestly, and every “not yet” routes to remediation: digitise, document, train, assign. Where it breaks: skipping the gates because a demo looked easy, and discovering in production that your inspection reports were never digitised.
Data. Can the AI read what you actually work with? If not, digitise and clean first.
Process. Is the workflow written down and stable? Steps that live in one person's head kill automation.
Owner. Someone named must run the system, answer for it and be able to switch it off.
Remediate. Every “not yet” goes to fixing, not to a brave pilot.
Pilot. All gates green: now a pilot stands on solid ground.
An 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.
ReadAI 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.
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We design and ship AI systems with permissions, logs and human approval. It starts with a 30-minute scoping call.