Framework
AI Readiness Assessment
A 20-question diagnostic for executive teams to evaluate organizational preparedness and surface the highest-impact gaps before committing serious capital to AI.
This assessment works best as a structured exercise, not a survey. Each member of the executive team scores all 20 questions independently, rating each on a 1–5 scale: 1 meaning the honest answer is “no” or “we don’t know,” 5 meaning you could defend a “yes” to your board with evidence in hand. Independent scoring matters: the goal is to surface where your leadership team’s perceptions diverge, because those gaps are usually where the organization’s real risk lives.
Then compare results in a working session. Don’t average away the disagreements — interrogate them. A question where the CEO scores a 4 and the CIO scores a 2 tells you more than any composite number. Sum each person’s ratings for a total between 20 and 100, note your weakest of the five domains, and use the interpretation bands below to decide where the next dollar and the next quarter should go.
The Diagnostic
Twenty questions across five domains
Rate each question from 1 (no, or we don’t know) to 5 (yes, and we can prove it). Answer for the organization you have, not the one on the roadmap. Your score is calculated as you go — nothing is required to see your results.
Strategy & Leadership
1. Can you name the three business outcomes your AI investments are accountable for this year?
2. Does your executive team share a single, written point of view on where AI will and will not change your business model?
3. Is there a named senior executive whose compensation depends on AI results, not AI activity?
4. When AI priorities conflict with quarterly pressures, is there a documented mechanism for deciding which wins?
Data & Technology
5. Could you deploy a model against your customer data tomorrow without a six-month cleanup project first?
6. Do you know, with evidence rather than opinion, which of your data assets are accurate enough to make automated decisions on?
7. Can your architecture support an AI system in production, with monitoring and rollback, or only in a demo environment?
8. Is there one accountable owner for each critical data domain, or does ownership dissolve across departments?
Talent & Culture
9. Could your organization staff a serious AI initiative this quarter without hiring a single person?
10. Do your managers know which roles AI will reshape in the next 18 months, and have they told those teams?
11. When a pilot fails, does your culture treat it as tuition or as a career-limiting event?
12. Are frontline employees bringing you AI use cases, or are all ideas flowing top-down?
Governance & Risk
13. If a regulator asked you today to list every AI system touching customers, could you produce that list within a week?
14. Is there a defined threshold above which an AI decision requires human review, and is it enforced in practice?
15. Do you have a tested plan for what happens when a model produces a harmful or public-facing error?
16. Can you explain, in plain language, how your highest-stakes model reaches its decisions?
Value & Execution
17. Can you state the measured return of your last AI initiative in dollars, hours, or risk reduced, not in enthusiasm?
18. Do you have a standing process for killing AI projects that are not paying back, and has it actually killed one?
19. Is there a repeatable path from pilot to production, or does every initiative negotiate its own way through the organization?
20. Are you funding AI as a portfolio with expected returns, or as a collection of unconnected experiments?
Interpreting Your Score
What your total tells you
The total sets your posture; the domain breakdown sets your agenda. Wherever you land, your lowest-scoring domain is the first item on it.
20–49
Build foundations first
Broad deployment now would compound existing weaknesses. Concentrate on the two lowest-scoring domains: assign clear ownership, close the most critical data and governance gaps, and run one narrow, well-instrumented pilot to build organizational muscle before expanding.
50–79
Ready to scale selectively
You have the fundamentals to move, in the right places. Scale AI where your domain scores are strongest, and treat your weakest domain as the binding constraint. Set explicit gates so initiatives can’t outrun your governance or data maturity.
80–100
Push toward enterprise-wide deployment
Your constraint is no longer readiness. It’s ambition and speed. Shift from proving value to compounding it: standardize your path to production, embed AI targets in operating plans, and re-run this assessment quarterly so confidence never drifts from evidence.
Twenty questions, one honest afternoon.
We facilitate readiness workshops where your executive team scores, debates, and leaves with a prioritized 90-day plan grounded in the results.
Schedule a Strategy Session