Guide: Creating The Proper Data Infrastructure for AI-Driven Formulation & Measurement in R&D

Today, more than ever, the advancements in Artificial Intelligence (AI) and Machine Learning (ML) have redefined the ways in which R&D organizations are able to analyze, interpret, and leverage large volumes of data more efficiently and productively. However, a crucial precursor to properly and fully utilizing AI, is the meticulous structuring of laboratory data.

This white paper delves into the multi-faceted implications of structured lab data, popular data systems used in today’s lab environment, how to determine and prepare your organization for compatible and successful AI-driven development, how to prepare and find the right AI solution for your organization, and best practices for R&D organizations as a roadmap to guide the way for AI-driven formulation and measurement in R&D.

Key Objectives

• What to consider before deploying AI for your R&D efforts
• Best practices in structuring lab data for AI-driven R&D
• Comparing data systems/sources in the lab – examples, advantages, & disadvantages
• How to create a roadmap for AI-driven formulation & measurement
• Questions to ask when evaluating different vendors/providers

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