1. Workflow readiness
Can the organisation name the workflows where AI could materially change cost, speed, quality, compliance, or customer experience? If the answer is vague, the organisation is not ready to buy deeply. Tool-first adoption works badly when the workflow case is weak.
2. Data and knowledge readiness
Are the underlying documents, records, process rules, and knowledge sources current, trusted, and permissioned well enough to support AI safely? If the organisational memory is broken, assistants and agents will disappoint even when the model is strong.
3. Governance readiness
Does the business know how to classify use cases by risk, who approves them, what controls apply, and when human review is required? Governance does not have to be heavy to be real, but it does have to exist.
4. Capability readiness
Who will own AI? Who will shape the use cases, test them, govern them, support them, and drive adoption? Capability readiness is where many organisations discover that they need more than one kind of AI talent.
5. Delivery readiness
Can the business actually move from experiment to production? This includes architecture, security, privacy, integration, evaluation, monitoring, change management, and support. A prototype is not evidence of delivery readiness.
6. Executive decision readiness
Finally, does leadership know what kind of AI organisation it wants to become? Many teams are tool-ready in a narrow sense, but not decision-ready. They can buy software. They cannot yet choose what matters.
Run an AI readiness review before you buy more tools
Metamorph-iT helps organisations assess use cases, data, governance, workforce capability, and path-to-production risks before they lock themselves into the wrong AI stack or the wrong vendor story.
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