AI Maturity Rubric
Last updated: 2026-08-14
The AI maturity rubric assesses five dimensions, data, delivery, governance, operations, and people, across five levels. The method is to mark current position, then mark the level the next three use cases require, and treat the gap between them as the work plan.
A maturity score on its own changes nothing. The rubric is useful when it is read as a gap against specific planned work, which converts an assessment into a sequenced investment case.
The template
Five dimensions, five levels. Assess where you are, then mark where your next three use cases require you to be. The gap is the work plan.
| Level | Data | Delivery | Governance | Operations | People |
|---|---|---|---|---|---|
| 1. Initial | Fragmented, no catalogue | Individual experiments | None | None | Isolated enthusiasts |
| 2. Emerging | Catalogue exists, quality uneven | Pilots delivered, few in production | Principles published | Manual monitoring | Small central team |
| 3. Defined | Governed sources, documented lineage | Repeatable path to production | Tiering and gates operating | Monitoring and alerting standard | Roles defined, training in place |
| 4. Managed | Quality measured, issues remediated on cycle | Delivery predictable, platform shared | Register complete, reviews on schedule | Incident process tested, retraining routine | Capability distributed to business units |
| 5. Optimising | Data products with owners and service levels | Portfolio managed against outcomes | Governance informs strategy | Automated detection and response | Internal faculty, capability self-sustaining |
Do not target level 5 across all dimensions. Most organisations need level 4 on Data and Operations and level 3 elsewhere. Targeting uniformly high maturity spends budget on capability that no current use case requires.
How to use it
- Mark the current level honestly on each of the five dimensions.
- Mark the level your next three use cases actually require. Not an aspirational target level.
- Treat the difference as the work plan, and fund the dimensions that block the planned work rather than the lowest-scoring dimension overall.
- Re-assess annually or when the delivery portfolio changes materially, not quarterly. Maturity moves slowly and frequent scoring invites gaming.
This template comes from the AI Use Case Discovery, Readiness and Maturity: A Practical Playbook, which sets out the reasoning behind it, the rules of thumb that govern its use, and the signals that tell you the approach is failing. The full playbook is also available as Markdown. See all AI governance templates.