Artificial Intelligence in Organic Synthesis

Artificial intelligence (AI) is rapidly reshaping organic synthesis; nevertheless, currently most laboratory practice still remains driven by human intuition, trial‐and‐error optimization, and manual interpretation of analytical data. Here, we synthesize recent advances that move AI from isolated demonstrations to a practical toolkit spanning the full experimental cycle: molecular design and prioritization, computer‐assisted synthesis planning and route selection, catalyst and condition optimization, and AI‐enabled product identification and verification using chromatographic and spectroscopic data. We analyze these developments using a three‐level hierarchy—AI assistant, AI analyst, and emerging AI researcher—and map them onto what bench chemists can deploy today, from commercial and open retrosynthesis platforms to multimodal structure elucidation workflows. We also frame adoption as a strategic choice among three trajectories, arguing that near‐term impact will be dominated not by fully autonomous robotic laboratories but by scalable “digital co‐expert” approaches that compress candidate spaces and accelerate decision‐making while preserving rigorous human validation. Finally, we highlight why data quality, laboratory variability, underreported negative results, and black‐box failure modes demand calibrated reliance, mechanistic plausibility checks, and standardized synthesis applications. Together, these trends point to an end‐to‐end digital thread for organic synthesis that optimizes decisions across workflows rather than individual steps.