Six dimensions of AI readiness
A company can be ready for one low-risk workflow and unready for another high-impact one.
Assess readiness against a real use case
“Are we AI ready?” is too broad. Ask: “Are we ready to automate first-pass support triage from these approved sources, with this escalation path and this success metric?” The dependencies become concrete.
Turn gaps into prerequisites
| Gap | Readiness action |
|---|---|
| No reliable source data | Fix data access / quality first |
| No workflow owner | Assign accountability |
| No baseline metric | Measure current state |
| High-impact output | Add stronger controls / review |
| Integration unavailable | Re-scope or address system dependency |
Readiness should change the implementation plan
If readiness is weak, do not simply lower a score and proceed. Narrow the workflow, choose a different use case, fix the dependency or keep the action human-owned until evidence improves.
Frequently asked questions
What is AI readiness?
AI readiness is the practical ability to support a defined AI use case with suitable data, systems, ownership, controls, skills and measurement.
Do I need a company-wide AI maturity score?
Not necessarily. Workflow-level readiness is often more actionable because dependencies differ by use case.
What if we are not AI ready?
Fix the specific blocker, narrow the use case or choose a simpler opportunity. Low readiness does not require abandoning AI entirely.