AI readiness is the ability to do two things: find the workflows where AI would deliver measurable value, and govern its use without creating unmanaged risk. This three-minute assessment scores those two abilities separately, places you in one of four profiles, and names the three things worth fixing first. Your result appears on the page — no email gate.
Twelve statements, about three minutes. You will get two scores — whether you can find the workflows where AI pays off, and whether you can govern its use — plus the three things worth fixing first.
These are not scored. They let us report anonymous benchmarks by role and company size once enough organizations have taken the assessment.
Six statements measure value readiness — whether you can name where time is lost, whether that work repeats the same way every time, whether people know which system holds the correct data when two disagree, whether you could write down what a good result looks like, whether you know where human judgment enters, and whether you have real numbers for how the work performs before AI touches it. Six more measure governance readiness — whether you know which AI tools are actually in use, whether staff know what is permitted, whether someone owns review, whether important outputs get checked by a person, whether concerns can be traced afterwards, and whether adoption is judged by whether the work improved rather than by licences sold.
They fail independently, and the failures look nothing alike. High value readiness with low governance readiness is how shadow AI spreads: useful tools adopted faster than anyone can see them. The reverse — strong controls, weak discovery — produces organizations that approve pilots chosen by intuition and quietly lose confidence when they underperform. A single blended score would hide which one is your actual bottleneck.
| Value | Governance | Profile |
|---|---|---|
| High | High | Ready to scale with evidence |
| High | Low | Opportunity ahead of control |
| Low | High | Governed but under-discovered |
| Low | Low | Establish the foundation |
This assessment measures what your organization believes about itself. That is useful, and it is not the same as what happens on the screens. In our experience the two diverge most on the questions people are most confident about: which workflows really consume the time, and which AI tools are really in use. Self-reported inventories of AI usage are consistently shorter than observed ones, because people report the tools they were asked about.
That gap is the reason Capolla exists. We observe how work actually happens — on the device, with consent, raw data staying local — and turn it into the evidence version of this same picture. If your score feels either too flattering or too harsh, that instinct is worth testing against data. You can request an evidence pilot or read how the readiness audit works in practice.
AI readiness means an organization can do two things: identify the workflows where AI would deliver measurable value, and introduce AI into them without creating unmanaged risk. Most readiness models measure only technology and skills, which is why organizations pass them and still stall — the missing pieces are usually workflow visibility and oversight, not tooling.
About three minutes. Twelve statements, four answer options each, then two optional context questions. Your result appears immediately on the page.
No. The full result — both scores, your profile, and the three things to fix first — is shown without any contact details. You only share an email if you choose to talk to us about the result.
Your answers are stored anonymously and reported only in aggregate. We keep a hashed IP address briefly for rate-limiting, never the address itself, and we never receive your name or email unless you submit the contact form yourself.
Neither. It is a directional self-assessment. Until enough organizations have completed it we show no percentile, and we deliberately avoid presenting the score as a standard. It tells you where to look first, not where you rank.
Because they fail independently. Teams that map opportunities without oversight ship shadow AI; teams that govern without discovery approve pilots chosen by intuition. A single blended score hides which of the two is actually holding you back.
What an AI readiness audit looks like when it runs on desktop workflow evidence — the observed-data counterpart to this self-assessment.
Shadow AI is the new shadow IT — why the usage-visibility statement is the one most organizations score lowest on.
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