Source: [Original HTML page](https://stolenorbit.com/en/templates/ai-data-readiness/)

Language: English

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# AI data readiness worksheet

Assess data for a specific use case. A large archive is not automatically usable: you need representative examples, clear meanings and appropriate access to the relevant information.

[By Stolen Orbit](https://stolenorbit.com/en/about/) Revision 2026-09-24

## When to use it and what to produce

[Source for this section](https://stolenorbit.com/en/templates/ai-data-readiness/#template-purpose)

Before estimating an AI project or approving a trial involving documents, CRM records, support tickets or internal knowledge.

A source inventory with observed defects, remediation actions and an approved sample for the trial.

Fill in the fields below and download your document. The website does not send or save answers: closing the page may lose them. Examples are illustrative and should be replaced with your process data.

[Download the complete blank template (.md)](https://stolenorbit.com/downloads/data-readiness-en.md)

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## 1\. Identify the sources

[Source for this section](https://stolenorbit.com/en/templates/ai-data-readiness/#sources)

Start with information needed for the intended decision or action. Exclude irrelevant archives and record who understands the meaning of each source.

**See a worked example**

Illustrative example: technical procedures in a shared folder, reviewed quarterly by the support owner. Personal copies and unapproved drafts remain outside the trial.

**Source, format and owner**

Record the system, operational owner, available format and stable record identifier.

**Access and intended use**

Describe who may access the data, for which activity and who authorises its use in this project.

**Updates and retention**

Record update frequency, obsolete information and internal removal rules that need confirmation.

## 2\. Inspect a sample

[Source for this section](https://stolenorbit.com/en/templates/ai-data-readiness/#quality)

Select common and difficult cases. Record the selection method: a sample containing only clean files hides the conditions encountered in actual work.

**See a worked example**

Illustrative example: 40 documents cover digital PDFs, scans and tables. Five have conflicting versions: identify the authoritative version before constructing the evaluation set.

**Sample and coverage**

Describe period, size and variety: languages, formats, teams, rare cases and incomplete documents.

**Observed defects**

Count missing fields, duplicates, conflicting versions and records that cannot be matched.

**Meaning and correct reference**

Identify who can explain fields and establish the expected answer when information is ambiguous.

## 3\. Plan the preparation

[Source for this section](https://stolenorbit.com/en/templates/ai-data-readiness/#readiness)

Separate issues that prevent a trial from manageable limitations. Assign concrete actions without waiting to clean every record in the business.

**See a worked example**

Illustrative example: begin with one approved document family. Unreadable scans go to manual processing and are counted separately when results are reported.

**Remediation, owner and date**

For each gap state what changes, who acts and how completion will be checked.

**Excluded or restricted data**

Define categories, permissions and cases that must stay outside the initial trial.

**Entry criteria**

Specify which sources, labelled examples and authorisations must exist before the trial starts.

## Pitfalls to avoid

[Source for this section](https://stolenorbit.com/en/templates/ai-data-readiness/#template-pitfalls)

-   Confusing technical access to a file with permission to use it.
-   Assessing quality only on easy cases.
-   Launching an assistant with conflicting versions and no authoritative source.

[Decide where AI is useful before committing to implementation.](https://stolenorbit.com/en/services/ai-opportunity-assessment/)

[Readable reports built on numbers you can verify.](https://stolenorbit.com/en/services/controlled-ai-reporting/)

[RAG or fine tuning: first identify the error you need to fix.](https://stolenorbit.com/en/resources/rag-or-fine-tuning/)

[Prepare data for AI by checking one process, not the entire archive.](https://stolenorbit.com/en/resources/data-readiness-for-ai/)

[All templates and tools](https://stolenorbit.com/en/tools/)

[All guides](https://stolenorbit.com/en/resources/)

## Take the next step together.

Use this document to start a discussion about scope, evaluation and project ownership.

Review the text before sending. Only your email is needed alongside the message; other details are optional.

[Contact us](/en/contact/)

## Turn the question into a next step.

-   [Use what you have, connect it, or build the missing part?](https://stolenorbit.com/en/resources/native-automation-connectors-custom-integrations/)
-   [Automation failures and duplicates](https://stolenorbit.com/en/resources/automation-failures-and-duplicates/)
-   [System integration map](https://stolenorbit.com/en/templates/integration-map/)