Autonomous experimentation with self-driving laboratories has shown the ability to accelerate lengthy experimental campaigns, reducing the time that researchers spend on tedious manual experiments. Self-driving laboratories combine automation and machine learning (ML) to perform experiments, evaluate acquired data, and decide subsequent experimental conditions. This paradigm has been applied to a myriad of problems in fields such as organic synthesis , materials development , drug discovery , and more. Recently, autonomous experimentation in electrochemistry has begun to emerge, with self-driving labs being used for mechanistic investigations and materials discovery. This recent emergence of autonomous experiments in electrochemistry has been driven by mainstream acceptance of ML and automation across the community as tools that complement chemical intuition.