The modular design of the ARCANA pipeline enables real-time monitoring of battery degradation profiles, promoting timely and cost-effective interventions. This proactive approach prevents prolonged suboptimal testing conditions, improving the R&D process, and contributes to more informed material selection and protocol optimization. By automating data collection, processing, and analysis, researchers can streamline their experimental workflows and reduce human error. Furthermore, ML models can continuously learn from new data, adapt to evolving experimental conditions, and provide real-time insights. This integration of ML and laboratory workflows has the potential to transform battery research, enabling researchers to make data-driven decisions, uncover novel insights more rapidly and accelerating the pace of discovery.