Practical guide
The purpose
An AI readiness assessment should connect institutional objectives to the people, information, policies, technology and operating processes required to achieve them. It should reduce uncertainty and produce a sequenced route—not a generic catalogue of tools.
The most important question is not “How advanced are we?” It is “Which outcomes matter, which uses are appropriate, what must be protected and what evidence will justify the next investment?”
Six dimensions to assess
1. Mission and outcomes
Identify the decisions, services, research, workflows or creative outcomes AI may support.
2. Information
Review approved sources, sensitivity, permissions, retention, jurisdiction and quality.
3. Governance
Clarify accountability, acceptable use, human review, escalation, procurement and documentation.
4. Infrastructure
Consider public, private, hybrid and sovereign-aware environments based on workload.
5. People
Assess leadership understanding, role-based capability, champions, training and support.
6. Measurement
Define value, quality, adoption, risk and operating metrics before a pilot begins.
How to prioritize use cases
Rank opportunities using mission value, employee time saved, information sensitivity, data readiness, implementation complexity, human-review requirements, risk and measurable outcomes. The strongest first pilot is rarely the most dramatic idea; it is the one that can produce useful evidence without creating uncontrolled exposure.
What the assessment should deliver
- A small portfolio of prioritized opportunities
- A risk and governance map
- A capability and information baseline
- An infrastructure and tool decision view
- A workforce learning plan
- A phased roadmap with owners and decision gates
What happens next
The assessment may lead into a Solution Workshop, governance workshop, Copilot readiness assessment, sovereign AI decision session, Academy cohort or bounded implementation pilot.