Proprietary experiment history outweighs purchased data in AI R&D

Research suggests that in AI-guided Research and Development (R&D), an organization’s proprietary experiment history holds greater value than purchased datasets. While the data economy allows companies to buy records of successful outcomes from other organizations, these datasets often lack accuracy when applied to a specific enterprise’s unique tools, processes, and candidates.

Because purchased data typically describes conditions already explored by others, it fails to account for the unique variables of a new user. Consequently, the value of R&D lies in the archive of an organization’s own failures, which provides the necessary guidance for subsequent experiments that fall outside the range of existing market datasets.