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The Seven Dimensions of AI & Data Maturity
AI readiness is measured across seven dimensions, organisation, IT infrastructure, data management, ethics, privacy & security, people & skills, and performance management, each scored on five maturity levels, rather than judged by how many AI tools are already in use somewhere in the business. Tool adoption and genuine organisational readiness are frequently quite different things, and the assessment is specifically designed to separate the two.
The seven dimensions, from Baseline to Leading
| Dimension | Baseline looks like | Leading looks like |
|---|---|---|
| Organisation | No clear owner for AI decisions or outcomes | Named decision rights and accountability for every AI application |
| IT Infrastructure | AI tools bolted onto legacy systems ad hoc | Infrastructure designed to support AI deployment at scale |
| Data Management | Data scattered, inconsistently defined, ungoverned | Governed, well-defined data pipelines feeding AI reliably |
| Ethics | No framework for evaluating AI decisions or bias | Established, applied ethical review for consequential AI use |
| Privacy & Security | Ad hoc handling of data privacy and access risk | Privacy and security built into AI deployment by design |
| People & Skills | AI literacy limited to a handful of enthusiasts | Role-specific AI capability built across the organisation |
| Performance Management | No way to measure whether AI use is delivering value | Clear metrics tying AI use to measurable business outcomes |
Why tool count is a misleading proxy for readiness
A company can have dozens of employees using AI tools individually while having no organisational capability to deploy AI at scale, no data governance, no clear ownership, no way to evaluate whether a given AI application is actually safe to put into production. The seven dimensions test for that underlying capability directly.
Which dimensions typically lag furthest behind
Data management and organisation (clear ownership and decision rights) are the two dimensions where most companies score lowest, even when their technology and people dimensions look reasonably strong, which is exactly the kind of imbalance a single overall "AI maturity" score would hide.
How the assessment actually gets scored
In a working session with the client, each dimension scored against detailed level descriptions rather than a self-completed survey, so the resulting score reflects a shared, checked understanding rather than whichever number feels most comfortable to report.
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