For the last few years, the narrative around Generative AI in science has largely focused on administrative efficiency – writing grant proposals, summarizing dense papers, or debugging Python scripts.

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...