AI Maturity Model

What to teach depends on where the organisation is starting.

The Five Levels

Level	Pattern	What People Need	What Not To Lead With
0. Unaware or blocked	AI is discussed but not used meaningfully	Executive framing, risk literacy, safe starter use cases	Agent builds, technical architecture
1. Experimenting	Individuals use public tools inconsistently	Prompt foundations, data handling, human review, tool selection	Complex automation
2. Standardising	Teams want shared practices	Team playbooks, approved workflows, champion network, metrics	Full enterprise platforms before use cases are clear
3. Operationalising	AI is embedded in recurring work	Workflow redesign, integrations, RAG, evaluation, governance cadence	One-off generic training
4. Scaling and transforming	AI changes operating models	Portfolio management, agent supervision, AI product thinking, internal capability transfer	Basic awareness sessions

Maturity Assessment

The assessment should be lightweight enough to run quickly, but concrete enough to drive a training plan.

Dimension	Questions
Leadership intent	Is there a clear business reason for AI adoption? Who owns it?
Tool access	What tools are already approved or used unofficially?
Data risk	What data can and cannot be entered into AI tools?
Workflow fit	Which repetitive, text-heavy, analytical, or decision-support tasks are visible?
Capability	Who are the current power users? Who needs foundations?
Governance	Are policies helpful, current, and understood?
Measurement	Does the business know what good adoption looks like?

Training Prescription

If the Organisation Is…	Start With	Then Move To
Anxious or policy-constrained	Executive briefing plus safe-use policy clinic	Small approved use-case lab
Curious but inconsistent	Foundations workshop for staff and managers	Prompt library and champion programme
Already using AI informally	Team workflow labs	Governance, measurement, and shared playbooks
Ready to integrate AI	Use-case prioritisation and technical discovery	RAG, automation, and evaluation workshops
Scaling AI across functions	Portfolio and operating model design	Internal academy and train-the-trainer

Acceptance Criteria

Every participant can identify what maturity level their team is at.
Leaders can name which AI behaviours are encouraged, restricted, or prohibited.
Teams leave with practical next-step training rather than a generic course list.
The programme avoids teaching advanced automation before governance and review habits are in place.

Outputs

Maturity scorecard.
Recommended curriculum path.
Priority use-case shortlist.
Governance gaps and immediate guardrail actions.
Champion candidates.