Prepare your data to power AI
A 10‑minute self‑assessment evaluating the strength, structure and suitability of your data for effective
Artificial Intelligence (AI) use.
AI Data Readiness Assessment
This checklist helps executives prioritize the data foundations needed for safe, compliant AI across the biopharmaceutical lifecycle. AI creates value only when data is structured, contextualized and governed from the moment it is created.
Who should complete this checklist
For an accurate and balanced assessment, complete this checklist with a small cross‑functional group. Include colleagues from scientific, quality or regulatory functions, as well as data or IT.
Scope of data
When working through this checklist, consider your entire current data landscape drawing on all the sources within your organization, not only an ELN or lab informatics platform. Take into account the full range of data types you generate: experiment, product and process data, including batch, method, instrument, sample and QC data.
What you will get
- A readiness score across 10 critical dimensions
- Identification of the two or three highest‑impact gaps
- A targeted 90-day action plan to build momentum and demonstrate progress
- A shared cross-functional view spanning business, scientific and quality teams
How to use the checklist
- Score each dimension from 0 to 3 based on your current capabilities.
- Add up the scores across all dimensions to calculate your total readiness score.
- Review the scoring interpretation to understand your overall level of readiness and recommended next steps.
Definitions
- Data product: A curated, documented and reusable dataset, along with its metadata, quality indicators and access policy, owned by a business domain.
- Governance signals: Tags and metadata that communicate quality, validation status, permitted use, consent or contractual restrictions and lineage; these persist into analytics and AI pipelines.
- Evidence pack: A compiled set of artifacts that demonstrates intended use, data suitability, validation status, lineage and ongoing monitoring for a specific regulated decision.
- Pattern library: A governed set of reusable, inspection‑ready templates that standardize data capture, context of use, governance, lineage, and evidence for repeatable AI use cases, enabling scale through reuse rather than reinvention.
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