Artificial intelligence in analytical chemistry is rapidly advancing from automation and pattern recognition to systems that reason, generate, and adapt. This talk examines the latest implementations of machine learning and generative AI in chemical analysis, with emphasis on spectroscopy, chemometrics, and data-rich measurement workflows. Topics include foundation models for spectral interpretation, hybrid physics–AI models, autonomous calibration and method optimization, uncertainty-aware predictions, and explainable AI for regulatory and scientific application. Practical examples from the literature will distinguish genuine analytical advances from overstated claims, and outline where AI meaningfully improves accuracy, robustness, and productivity—and where human expertise (domain knowledge) remains indispensable. The goal is to provide a realistic framework for deploying AI that enhances chemical insight rather than replacing it.
