Before you hire · Free tool

AI Readiness Assessment

Six questions, one score, and an honest read on whether an AI project will take hold here or quietly die in month three.

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AI Readiness Assessment

Score your business readiness for AI in 6 questions. Get a personalized action plan and readiness score.

1. How much of your team's work is repetitive?

Under 20%
20-40%
40-60%
60-80%
Over 80%

2. Is your business data digital?

Mostly paper
Mixed
Mostly digital
Fully digital

3. How does your team handle new technology?

Resistant
Cautious
Open
Enthusiastic

4. Monthly budget for AI tools?

Under $500
$500-$2K
$2K-$10K
$10K+

5. Do you have a clear problem AI should solve?

Not really
Somewhat
Yes, clearly

6. Have you used AI tools before?

Never
Occasionally
Regularly
Extensively

Why readiness predicts outcomes better than budget

An MIT report widely cited through 2026 found roughly 95% of enterprise generative-AI pilots produced no measurable profit-and-loss return. That figure gets quoted carelessly, so here is the honest version: it measured P&L impact within six months, mostly in sales and marketing — the weakest category in the study. It is not evidence that AI does not work. It is good evidence that pilots get launched into businesses that were not ready for them.

Readiness is mostly not about technology. It is about whether repetitive work exists to automate, whether the data describing it is in a usable state, and whether anybody's job is genuinely allowed to change as a result.

What the six questions are actually testing

QuestionWhat it is really asking
How repetitive is the work?Is there enough volume for automation to pay back the effort of building it?
Is your data digital?Can a system reach the information at all? Paper and people's inboxes are both "no".
How does the team handle new tech?The strongest predictor of whether the tool survives past month three.
Monthly budgetNot whether you can afford it — whether you can afford to iterate after v1 disappoints.
Is there a clear problem?"We should be doing AI" is not a problem. It is the most expensive sentence in this field.
Prior AI experienceCalibration. Teams who have used the tools have realistic expectations of them.

Reading your score

A low score is a useful result, not a bad one. It costs nothing to discover you are not ready. Discovering it four months into a paid engagement costs a great deal.

What to do with a middling score

The most common outcome is somewhere in the middle, and the most common mistake is treating that as permission to proceed at full scale. Fix the weakest of the six answers first — it is almost always data or team readiness, and both are cheaper to address before an implementation than during one. Then run the 90-day planner, which builds a roadmap around your specific obstacle.

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