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Li-ion batteries are the essential energy-storage building blocks of modern society. However, producing ultra-high electrochemical performance in safe and sustainable batteries for example, e-mobility, and portable and stationary applications, demands overcoming major technological challenges. Materials engineering and new chemistries are key aspects to achieving this objective, intimately linked to the use of advanced characterization techniques. In particular, operando investigations are currently attracting enormous interest. Synchrotron- and neutron-based bulk techniques are increasingly employed as they provide unique insights into the chemical, morphological, and structural changes inside electrodes and electrolytes across multiple length scales with high time/spatial resolutions. However, data acquisition, data analysis, and scientific outcomes must be accelerated to increase the overall benefits to the academic and industrial communities, requiring a paradigm shift beyond traditional single-shot, sophisticated experiments. Here a multi-scale and multi-technique integrated workflow is presented to enhance bulk characterization, based on standardized and automated data acquisition and analysis for high-throughput and high-fidelity experiments, the optimization of versatile and tunable cells, as well as multi-modal correlative characterization. Furthermore, new mechanisms, methods and organizations such as artificial intelligence-aided modeling-driven strategies, coordinated beamtime allocations, and community-unified infrastructures are discussed in order to highlight perspectives in battery research at large scale facilities.