Most AI readiness checklists measure the wrong thing. They ask what tools you've bought and how much budget you've set aside. Neither predicts whether anything changes.

What predicts it: whether leadership has said out loud what happens when work gets faster, whether people have permission to spend time learning, and whether your data is somewhere a tool can reach it.

Twelve questions below. Give yourself 2 points for yes, 1 for sort of, 0 for no. Total at the bottom.

Leadership and direction

1. Can your leadership team name the specific outcome they want from AI? "More efficiency" is a 0. "Cut quote turnaround from 5 days to 1" is a 2.

2. Has someone said out loud what happens to the time AI saves? If nobody has answered "do I get that time back, or just more work?", your team already has. They assumed the worst.

3. Is there one person accountable for AI adoption? Not a committee. One name, with hours in their week for it.

People and capability

4. Do more than 3 people on your team use AI weekly for real work? Drafting an email counts. Asking it a trivia question doesn't.

5. Can someone on your team point to a workflow that changed because of AI? Not "we're exploring." Something that runs differently now.

6. Do people have explicit permission to spend work hours learning this? If learning only happens after hours, it doesn't happen.

7. Would your least technical employee say they know where to start? This is the one most teams fail. It's also the one that predicts adoption best.

Process and data

8. Could you describe your most painful repetitive workflow, step by step, right now? If you can't, that's your first project — mapping it, not automating it.

9. Do your core systems talk to each other? CRM, accounting, project management. If the answer is "someone exports a CSV," score 0.

10. Is the data an AI tool would need actually accessible? Locked in PDFs, one person's inbox, or a filing cabinet all score 0.

Guardrails

11. Do you have a written policy on what data can go into which AI tools? Written. Not "everyone knows not to paste client data."

12. If someone used AI on something sensitive today, would you know? Most organizations wouldn't. Scoring 0 here is common and worth fixing early.

Your score

0–8 · Start with the basics The gap isn't AI, it's foundations. Map one painful workflow end to end and get your systems talking. Adding AI now would sit on top of a process nobody has fixed. Read AI vs. Automation — most of what's hurting you is probably the automation half.

9–16 · Start with your people You have the raw materials and inconsistent use. A few people are ahead, everyone else is guessing. This is a training and guardrails problem, and it's the most common score. Read why AI adoption stalls.

17–24 · Start building Your team is using AI and your leadership knows what it wants. The bottleneck is capacity: the builds nobody has time to do. This is where custom automation pays for itself fastest.

What the score doesn't tell you

Which specific thing to do first. That takes looking at the actual work — the workarounds, the spreadsheet somebody maintains at 6am on Sunday, the approval step that exists because of one incident in 2019.

That's the part we do in discovery, and it's why every engagement starts there rather than with a tool.

Scored lower than you expected? That's the normal starting point. Tell us where you landed and we'll tell you what would actually help — training, automation, or fixing the basics first.