The 20-Question R&D AI Readiness Assessment
AI can accelerate R&D work, but only when it operates on evidence teams can inspect, trust, and use in context. Before deploying AI for scientific retrieval, analysis, recommendations, or automation, R&D organizations need to understand whether their data, governance, and workflows are ready.
This 20-question assessment helps R&D, quality, product, IT, and security stakeholders evaluate that readiness together. It 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 remain 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.
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- Questions that reveal whether data is current, approved, exploratory, superseded, invalidated, missing, or conflicting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
Assess the foundations behind your AI strategy before asking AI to influence scientific, product, quality, or operational decisions.
FAQs
The R&D AI Readiness Assessment is a 20-question checklist designed to help R&D, quality, product, IT, and security stakeholders evaluate whether the organization has the data, governance, and workflow foundations needed for evidence-grounded AI. It is intended to support practical discussion before deploying AI for retrieval, analysis, decision support, or more consequential automation. The assessment uses three response options: yes, partly, and no. A “partly” response often identifies a clear improvement opportunity.
The assessment should be completed collaboratively by stakeholders who understand how R&D data is created, governed, interpreted, and used. This can include R&D and laboratory leaders, scientists, quality teams, product or product-lifecycle owners, IT, data teams, security, and regulatory stakeholders where relevant. Different groups will often see different parts of the same problem. A scientist may know whether two experiments are comparable, while IT may understand system access controls and quality may know which records are approved or authoritative.
Evidence-grounded AI refers to AI that can connect its answers, recommendations, or outputs to identifiable source records and relevant context. Users should be able to inspect the sources behind an AI answer and understand whether those records are current, approved, exploratory, superseded, or invalidated. The goal is not simply to produce plausible output. It is to make AI-supported work more inspectable, traceable, and useful in scientific and quality-sensitive environments.
Scientific and product decisions depend on understanding where evidence came from, what conditions applied, whether the data is current, and whether it is suitable for the question at hand. Without provenance, users may be unable to distinguish approved information from draft material, identify conflicting records, or determine whether an AI recommendation is based on relevant evidence. The assessment therefore asks whether users can see source records behind AI answers and whether data, transformations, and analytical decisions are documented for consequential models.
The assessment asks whether the data model represents the objects and relationships that give scientific records meaning. These can include materials; supplier, site, and lot information; formulas; methods; samples; process conditions; and results. It also examines whether users can distinguish target process conditions from actual execution conditions, determine whether two experiments are comparable, and connect results to product, market, customer, and quality context when needed.
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