The difference in one table
| Dimension | Opportunity assessment | Readiness assessment |
|---|---|---|
| Primary question | Where should AI be used? | Can we support AI well? |
| Unit of analysis | Workflows / use cases | Organization / capabilities |
| Main output | Ranked opportunity backlog | Readiness gaps and actions |
| Best timing | Before selecting projects | Before or alongside implementation |
Which should come first?
If leadership has no idea where AI belongs, start with opportunity discovery. If a priority use case is already clear but data, governance or systems are questionable, readiness may need to come first.
How to combine them without creating two consulting projects
For each high-priority opportunity, attach a small readiness check: data access, integration surface, workflow ownership, control requirements and skills. Escalate to a broader readiness program only if repeated gaps appear across use cases.
What a decision-ready output looks like
A useful combined output shows the workflow, current burden, proposed AI boundary, value range, key risks, readiness blockers, owner and next experiment.
Frequently asked questions
Can one assessment cover both opportunity and readiness?
Yes, especially for smaller organizations. The important thing is to keep the two questions visible rather than hiding readiness gaps inside an opportunity score.
Is AI readiness a one-time assessment?
No. Readiness can change as systems, policies, skills and use cases change.
Does low readiness mean we should not use AI?
Not necessarily. It may mean choosing a narrower use case, fixing a specific dependency first, or using an off-the-shelf tool with stronger controls.