The 20-question R&D AI readiness assessment
An R&D organization is ready for AI when AI can operate on evidence teams can inspect, trust, and use in context. Before deploying AI for scientific retrieval, analysis, recommendations, or automation, teams need to know whether their data, governance, and workflows are ready. This 20-question checklist helps R&D, quality, product, IT, and security stakeholders evaluate that readiness together.
The checklist focuses on five foundations for evidence-grounded AI: provenance, scientific context, governance and access, model and output quality, and workflow accountability.
Use it to identify where AI can deliver value now, where data relationships or controls need strengthening, and which use cases should stay narrow and closely governed while the organization builds a stronger foundation. A "partly" answer is not a failure. It is often the most useful place to begin.
How do you know if your R&D data is ready for AI?
Your R&D data is ready for AI when users can inspect the source records behind an AI answer and tell whether those records are current, approved, exploratory, superseded, or invalidated. The scientific context also has to be connected: materials, supplier, site, and lot information, formulas, methods, samples, process conditions, and results.
Readiness also depends on governance. Permissions, source authority, and controlled records need to apply to AI actions, and scientific, quality, and regulatory approvals still need to hold when AI supports consequential decisions. For a broader view of data foundations, try the R&D data maturity assessment.
Who this checklist is for
This checklist is for cross-functional teams: R&D and laboratory leaders, scientists, quality teams, product owners, IT, data, security, and regulatory stakeholders. Each group sees a different part of the same problem, so the checklist works best completed together. Teams can also try the online R&D AI readiness assessment tool.
For a one-page overview of how Uncountable approaches this, read the solution brief AI connectivity: connect the AI your teams use to your R&D data.
What's inside
Inside this checklist:
- 20 questions to evaluate R&D AI readiness across five critical categories
- How to assess whether users can inspect the source records behind AI answers
- Questions that reveal whether data is current, approved, exploratory, superseded, invalidated, missing, or conflicting
- How to test whether scientific context, including materials, supplier, site, lot, formulas, methods, samples, processes, and results, is connected
- Governance and access checks for permissions, source authority, controlled records, and traceable AI actions
- Questions for evaluating intended use, uncertainty, limitations, assumptions, monitoring, drift, and significant model change
- Guidance for preserving scientific, quality, and regulatory approval requirements when AI supports consequential decisions
- A scoring framework that shows whether to prioritize use cases, strengthen controls, or start with evidence architecture
Download the checklist to test the foundations behind your AI strategy before asking AI to influence scientific, product, quality, or operational decisions.
FAQs
The 20-question R&D AI readiness checklist helps R&D, quality, product, IT, and security stakeholders evaluate whether the organization has the data, governance, and workflow foundations for evidence-grounded AI. Each question uses three responses: yes, partly, and no. A "partly" response often points to a clear improvement opportunity.
An R&D AI readiness checklist should be completed collaboratively by the people who understand how R&D data is created, governed, and used: laboratory leaders, scientists, quality teams, product owners, IT, data teams, security, and regulatory stakeholders. A scientist may know whether two experiments are comparable, while IT understands access controls and quality knows which records are approved.
Evidence-grounded AI is AI that connects its answers, recommendations, or outputs to identifiable source records and relevant context. Users can inspect the sources behind an AI answer and see whether those records are current, approved, exploratory, superseded, or invalidated. The goal is AI-supported work that is inspectable, traceable, and useful in scientific and quality-sensitive environments.
Data provenance matters for R&D AI because scientific decisions depend on knowing where evidence came from, what conditions applied, and whether it is current. Without provenance, users cannot tell approved information from draft material, spot conflicting records, or judge whether an AI recommendation rests on relevant evidence. Provenance also means documenting data, transformations, and analytical decisions behind consequential models.
R&D AI should be able to use a data model that represents the objects and relationships that give scientific records meaning: materials, supplier, site, and lot information, formulas, methods, samples, process conditions, and results. It should also distinguish target process conditions from actual execution conditions and connect results to product, market, customer, and quality context.
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