How to Get Your R&D Data Ready For AI

Most materials R&D teams sit on years of experimental data they can't actually use. This guide shows you how to structure, standardize, and activate it, so AI can do what it promises.

What's inside

A 6-step data standardization framework built for materials science labs
How to select the right ML algorithms for sparse, high-value datasets
Why teams unknowingly repeat 15–25% of experiments and how to stop it
The build vs. buy decision: when to use purpose-built platforms vs. custom infrastructure
ROI benchmarks: what to expect in year 1, 2, and 3 after AI implementation