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.