AI Readiness & Maturity Framework

How well people understand where AI can help their work, and whether the culture treats AI as augmentation rather than threat. Awareness combines visibility of AI's impact with the psychological safety to discuss it openly.

How well AI is woven into real workflows rather than used as a disconnected side tool. Integration looks at whether process-heavy, manual, or coordination-laden work has been redesigned to let AI reduce friction end to end.

How well the organisation sees, guides, and oversees AI use — including unsanctioned 'shadow AI'. Governance covers policy, inventory, roles, and the visibility needed to keep AI use responsible without stifling it.

How well the organisation assures the quality, accuracy, and trustworthiness of AI outputs. Integrity covers verification, human review, provenance, and the discipline to catch errors, bias, and fabricated content.

How well the organisation builds AI-relevant capability and aligns learning to a clear skills taxonomy. Learning covers role-based development, applying new skills in real work, and keeping capability current.

How well the organisation manages the most serious AI-related security and safety exposures — data leakage, prompt-based attacks, unsafe integrations, and misuse. Egregious Risk focuses on the high-severity failure modes that could cause outsized harm.

How well the organisation turns AI intent into measurable outcomes through disciplined delivery. Execution covers prioritisation, application sprints, outcome measurement, and the ability to scale what works.