Most AI purchases fail before the model runs. The software is $20/month. Making it work with your data, your systems, and your compliance requirements costs 10-100x that. These questions tell you whether you’re ready.
Data readiness
1. Can you export your core data in a structured format today?
Not “we could if we wanted to” – have you actually done it?
2. How old is the newest data in your analytics system?
If the answer is “months,” your AI will learn from the past, not the present.
3. What percentage of your critical data lives in free-text fields, PDFs, or scanned documents?
Above 50% means extraction before automation.
4. Do you have a data dictionary?
If nobody can explain what each field means, a model won’t figure it out either.
System landscape
5. How many systems touch a single patient record or financial transaction?
Each system boundary is an integration point. Each integration point is a cost.
6. Do your systems expose APIs, or do you export CSVs?
APIs mean automation. CSVs mean someone’s job is copy-paste.
7. What’s your oldest system? Can it be replaced?
If the answer is “no,” AI has to work around it, not replace it.
8. Who owns the integration between systems?
If the answer is “nobody,” that’s your first hire, not an AI tool.
Compliance and governance
9. Can you describe your data residency requirements in one sentence?
If you can’t, your AI vendor can’t either.
10. Who approves new data processing workflows?
If the answer takes more than 10 seconds, the approval process is the bottleneck, not the technology.
11. Do you have audit logging for existing automated processes?
If not, adding AI without audit trails creates compliance debt.
12. What happens when the AI is wrong?
In healthcare, wrong means patient safety. In finance, wrong means regulatory action. “It rarely happens” is not an answer.
Organizational readiness
13. Who will own the AI system after deployment?
If nobody raises their hand, the system dies in six months.
14. Does your team have time to test and validate AI outputs?
AI doesn’t eliminate work. It changes what the work is.
15. Have you killed an IT project in the last two years?
If yes, what was the reason? If that reason still exists, AI won’t fix it.
Scoring
- 12-15 “yes” answers: You’re ready. The plumbing works. Focus on use case selection.
- 8-11: Fixable gaps. Address data and integration issues first. AI second.
- Below 8: The foundation isn’t there. AI on top of broken infrastructure accelerates failure.
What to do with the results
This checklist is diagnostic, not a sales pitch. If you scored well, most AI vendors can help you. If you didn’t, the work isn’t AI – it’s data readiness, system integration, and organizational alignment. That’s what I do.