# AI readiness checklist: is your data ready?

- **Published:** 2025-11-25
- **Updated:** 2026-10-12
- **Category:** Enterprise Adoption
- **Author:** Sinthetix
- **URL:** https://sinthetix.com/blog/ai-readiness-checklist-your-data

> A practical checklist for assessing whether your data is ready for an AI project: inventory, quality, access, ownership, and measurement.

Models are available by subscription; your documents, records and permissions are not. Before choosing a vendor or a use case, run this checklist with the people who own the systems. It shows where the first project should and should not go.

**Key takeaways**

- Pick one use case first, then assess only the data it needs.
- For document-based AI, retrieval quality depends on the state of the source documents.
- Ownership, access rules and a test set matter as much as the data itself.
- Use a recognized framework, such as NIST's voluntary AI RMF, to structure governance.

## Why data readiness comes first

Retrieval-augmented generation, the most common way to point a model at company knowledge, works by retrieving passages from your documents and having the model answer from them [1]. If the right passage is missing, outdated or buried in an unreadable scan, the answer suffers. Research also links confident wrong answers to models guessing when uncertain [3], so supplying good source material and permitting "I don't know" are your levers.

## The checklist

### 1. Scope

- \[ \] One specific use case with a named business owner.
- \[ \] A list of the questions or tasks the system must handle, written in the users' own words.
- \[ \] A definition of "good enough," such as the share of answers that must be correct and cited.

### 2. Inventory

- \[ \] Every system holding relevant data is listed: file shares, email, ticketing, ERP, CRM, spreadsheets.
- \[ \] Each source has an owner who can answer questions about it.
- \[ \] You know which sources are authoritative when two disagree.

### 3. Quality

- \[ \] Documents are current; superseded versions are labeled or removed.
- \[ \] Scans and images have been checked for legible text.
- \[ \] Duplicates and drafts are identified.
- \[ \] Key fields (dates, customer names, part numbers) are consistent across systems.

### 4. Access and sensitivity

- \[ \] You know where personal, financial or confidential data sits.
- \[ \] Access rules exist per source, and the AI system can respect them per user.
- \[ \] You have decided what must never leave your environment, and which vendors may see what.
- \[ \] A named owner has reviewed the use case, its data flows and its failure modes.

### 5. Structure and freshness

- \[ \] Metadata exists (document type, date, owner, product, customer) to help filter retrieval.
- \[ \] There is a plan for how new and changed documents reach the AI system, and how often.
- \[ \] Deleted documents are removed from the index.

### 6. Measurement

- \[ \] A test set of real questions with approved answers, built with the people who do the work.
- \[ \] A baseline: how long and how accurate the task is today, without AI.
- \[ \] Logging so outputs and sources can be reviewed later.

### 7. Governance

- \[ \] A named person accountable for the system after launch.
- \[ \] A review process for high-stakes outputs.
- \[ \] A framework to organize risk work. NIST's AI Risk Management Framework is intended for voluntary use, and NIST published a Generative AI Profile (NIST-AI-600-1) in July 2024 that covers risks specific to generative AI [2].

## How to read your results

| Result | What it suggests |
| --- | --- |
| Mostly checked in sections 1-3 | Data is a reasonable starting point; proceed to a pilot |
| Gaps in section 4 | Resolve access and sensitivity before any pilot touches real data |
| Gaps in section 6 | Build the test set first; you cannot judge a pilot without it |
| Gaps across sections 2-3 | Start with a smaller, cleaner data source |

## Practical implication

Start with the narrowest use case whose data you can fully inventory, secure and test, then expand. A small pilot on clean, well-owned data teaches more than a broad one on data nobody can vouch for.

## Sources

1. [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401), arXiv (Lewis et al.), NeurIPS 2020, 2020-05-22
2. [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework), NIST, 2023-01-26
3. [Why Language Models Hallucinate](https://arxiv.org/abs/2509.04664), arXiv (Kalai, Nachum, Vempala, Zhang), 2025-09-04

## How Sinthetix helps

**Generative AI & LLM**: Integrate powerful AI models to turn unstructured data into insights and power chatbots.

- RAG systems
- LLM-powered chatbots
- Document analysis

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