AI in Analytical Chemistry: Why Data Quality Is the Game-Changer

Ayelet Ofarim, Arad-Ophir/CAS, Ramat Hasharon, Israel (ayelet@arad-ophir.co.il)

As artificial intelligence becomes increasingly integrated into analytical chemistry, the quality of underlying data has emerged as a critical factor in determining the success of AI-driven insights. Analytical workflows - from spectroscopy and chromatography to reaction modeling - generate vast and complex datasets. AI models are only as powerful as the data they are trained on - making data quality, structure and harmonization foundational to success.

In a field defined by complex, high-volume chemical and spectral datasets, the ability to curate and integrate clean, consistent, and comprehensive data is a strategic imperative.

This presentation will explore how expertly curated, high-quality scientific data fuels AI-driven breakthroughs. We will examine the challenges posed by data silos, inconsistencies, and lack of standardization and demonstrate how robust data governance and integration strategies can overcome these barriers.