1 / 10
FOUNDATIONAL KNOWLEDGE
How widely is AI terminology understood across your teams?
1
A few enthusiasts read about it; most don't engage.
2
Some teams know what a prompt is, what a model is.
3
Shared vocabulary across roles; AI basics are in onboarding.
4
AI literacy is a documented hiring criterion, tested during onboarding.
2 / 10
FOUNDATIONAL KNOWLEDGE
How would you describe your leadership team's AI literacy?
1
Leadership talks about AI but doesn't use it personally.
2
Some leaders experiment; AI isn't part of the strategy yet.
3
Leadership uses AI tools regularly and discusses outcomes.
4
Leadership models AI-augmented decision-making; teams observe and learn.
3 / 10
APPLICATION SKILLS
How do people use AI in their daily work?
1
Browser-tab ChatGPT, ad hoc, no shared practice.
2
Regular use by some teams; output quality varies widely.
3
Versioned prompt libraries, standard tools across teams.
4
Multi-agent workflows running in production with governance.
4 / 10
APPLICATION SKILLS
How are prompts and AI workflows managed across teams?
1
Whatever people type in the moment.
2
A few people have saved their favourite prompts.
3
Shared, versioned prompt library reviewed by the team.
4
Prompts are managed assets with structured context, versioned and tested.
5 / 10
CRITICAL EVALUATION
When AI gives an answer, what happens next?
1
We usually accept it if it looks right.
2
Some of us double-check; many don't.
3
We have a validation rubric; outputs are graded before being acted on.
4
Automated evaluator patterns check outputs; humans audit drift.
6 / 10
CRITICAL EVALUATION
How do you catch when AI is wrong?
1
Honestly, we usually don't.
2
We notice obvious errors but miss subtle ones.
3
Structured validation — multi-perspective review, citation verification.
4
Automated evaluation pipelines flag anomalies before humans see them.
7 / 10
ETHICS & GOVERNANCE
What governance exists for AI usage in your organisation?
1
Vague concerns; no usage rules in writing.
2
A draft usage policy exists; enforcement is patchy.
3
Documented governance with sign-off gates for sensitive use.
4
Article 4 / NIST RMF compliance program, documented and audited.
8 / 10
ETHICS & GOVERNANCE
How do you handle data privacy and AI?
1
We assume people use good judgment.
2
We have rules but they're not consistently followed.
3
Documented rules and approval processes for sensitive data with AI.
4
Documented compliance framework with audit trails for AI/data interactions.
9 / 10
STRATEGIC INTEGRATION
Where does AI sit in your operating plan?
1
It doesn't — not yet.
2
We are running pilots; no measurable ROI claimed yet.
3
AI is in the operating plan with named owners and outcome measures.
4
We operate as AI-native; capability is a market differentiator.
10 / 10
STRATEGIC INTEGRATION
How do you measure AI's impact on the business?
1
We don't really; it's anecdotal.
2
Some pilot metrics exist but they're soft — time saved, sentiment.
3
AI initiatives have business KPIs tied to operating outcomes.
4
AI capability is in business strategy with multi-year roadmap and investment.
YOUR PLACEMENT
Tier 2 — Adopting
Your team is past pure exploration but hasn't yet operationalised. Pockets of practice exist; the discipline isn't shared. This is the most common spot — and the highest-impact tier to move out of.
Where you sit, dimension by dimension
1 — EXPLORING · 4 — SCALING
Foundational Knowledge
Tier 1
1.0
Application Skills
Tier 1
1.0
Critical Evaluation
Tier 1
1.0
Ethics & Governance
Tier 1
1.0
Strategic Integration
Tier 1
1.0
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