Summary
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
