Creating The Proper Data Infrastructure for AI-Driven Formulation & Measurement in R&D
Discover what you need and how to prepare your business' R&D organization for proper AI-driven formulation and measurement
AI and machine learning have changed what R&D organizations can do with their data, but there is a catch that trips up most teams: AI only works on structured, connected data. Run it on spreadsheets stitched together by hand, and the results never arrive.
This white paper lays out how to build the data foundation that AI-driven formulation and measurement actually require. It covers what to consider before deploying AI, best practices for structuring lab data, how common lab data systems compare, how to build a roadmap, and the questions to ask when evaluating providers.
Build the data foundation AI in the lab depends on.
FAQs
Models can only learn from data they can read. Without structured, connected data across formulation, process, and results, AI has nothing reliable to work from, which is why structure comes first and AI second.
Data that is structured, linked across experiments and results, and queryable by content rather than by file name. That is the difference between an archive and a foundation.
Start by assessing your current data systems and gaps, structure and connect your data, then layer analysis and modeling on top. The guide walks through each step and the questions to ask vendors along the way.
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