Data and AI
AI-ready data: how to tell whether your data can support an AI project
Kriv AI helps teams check a dataset against one AI use case before building. This page gives the questions we ask and what to do with the answers.
AI-ready data is data that an AI system can use for a specific job without producing wrong or unsafe results. We have not found a single standard that fits every case, so readiness is judged against one use case at a time. The questions are practical: can you find the data, is it complete and current, is its meaning agreed, do you know who owns it, may the AI use it, and can you trace an answer back to it. If the answer to any of them is no, fix that gap first. A short review of one use case is a better start than a plan to clean everything.
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What does AI-ready data mean?
It means the data is fit for the job an AI system will do. Data that is fine for a monthly report can be unfit for an assistant that answers staff questions, because the assistant reads every record and cannot tell which ones are out of date or disputed.
We do not use a score or a pass mark, because we have not found one that fits every case. Readiness depends on the use case, so we write the test for the use case in front of us.
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Six questions to ask of your data
Ask these of the data behind one use case. A no is not a failure. It tells you what to fix before building.
1. Can we find it?
Do we know which systems, folders and inboxes hold the data, and is there a list?
2. Is it complete and current?
Are fields missing, are there duplicate records, and when was each source last updated?
3. Is its meaning agreed?
Do two teams use the same word, such as customer or active, to mean different things?
4. Does someone own it?
Is there a named person who can say a record is right and fix it if it is not?
5. May the AI use it?
Do your policies and agreements allow this data to go to this AI service, and have the compliance and IT owners agreed?
6. Can we trace an answer back?
If the AI gives an answer, can a reviewer see which records it relied on?
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What to do when the answer is no
Each gap has a usual first fix. These are starting points, not a full plan.
| If the answer is no | A usual first step | Who decides |
|---|---|---|
| Cannot find it | Write a source list for the use case. | The data owner and IT |
| Incomplete or stale | Add checks that stop bad records, and a refresh schedule. | The data owner |
| Meaning not agreed | Write one definition for each term the AI will use. | The business owners involved |
| No owner | Name one before the build starts. | Your leadership |
| Not cleared for AI use | Take the question to compliance, and exclude the data until it is answered. | Your compliance team |
| Cannot trace answers | Log the records read for each output. | IT, with the reviewers |
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Start with one use case, not all your data
A plan to make all your data AI-ready can run for a long time and prove nothing. A review of the data behind one use case is small enough to finish, and it shows the team what the gaps look like in practice. The next use case then starts from a better position.
The building work that follows, such as pipelines and document preparation, is described on our AI data engineering page. The rules for what data your AI may use are on our AI data governance page.
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Where Kriv helps
We run the AI Readiness & Governance Assessment as a fixed-scope engagement, so you know exactly what you get and when you get it. Work is scoped and priced after a short discovery call. See also our data and AI consulting page.
We do not clean your data for you in the review, and we do not decide what your compliance team permits.
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What this page does not cover
This page gives a practical checklist, not a standard or a certification. We cite no readiness statistics, because we did not find a source we could read and quote on 10 November 2026.
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Frequently asked questions about AI-ready data
What is AI-ready data?
It is data that an AI system can use for a specific job without producing wrong or unsafe results. Because we have not found one standard that fits every case, we judge readiness against one use case at a time.
How do I know if my data is AI-ready?
Ask six questions of the data behind one use case: can we find it, is it complete and current, is its meaning agreed, does someone own it, may the AI use it, and can we trace an answer back to it.
Does data have to be perfect first?
No. It has to be fit for the use case in front of you. Start with one use case, fix the gaps the review finds, and let the next use case build on that.
Who should own AI data readiness?
Each dataset needs a named owner who can say a record is right and fix it if it is not. Your leadership decides who that is, and your compliance team decides what the AI may use.
How long does a readiness review take?
It depends on how many systems the use case touches and how available their owners are. We give a plan after a short discovery call, once we know the use case.
Talk to the team that would do the work
Bring your requirements to a working session with the person who'll actually deliver.
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