Research into antibody-drug conjugates (ADCs) is currently at an inflection point due to recent clinical impact. ADC biotransformation analysis is key for understanding the structural integrity of ADCs in vivo and is a critical aspect of drug development, especially at the lead selection stage. Data analysis of biotansformed products is hindered by the manual and time-consuming analyte identification process oftentimes taking days to weeks. We developed a streamlined data analysis workflow enabling more automated peak identification using several commercial software tools that significantly improve data processing efficiency. A linker-payload biotransformation library was created for each new molecule and combined with antibody sequence information for peak matching. As a proof of concept, we tested this workflow across different payload and linker types, acquired using different mass spectrometers: an example using a topoisomerase I inhibitor-conjugated ADC (SCIEX ZenoTOF 7600) and a comparison to a published in vivo ADC biotransformation data set for a pyrrolobenzodiazepine-conjugated ADC (ThermoFisher QE HF-X). Using this more automated workflow, we rapidly identified major biotransformation species that were previously found manually including loss of linker-payload, thiosuccinimide ring hydrolysis, cysteinylation at the deconjugation site(s), and partial linker-payload cleavage. This improved data-analysis workflow has demonstrated superb effectiveness in streamlining overall ADC biotransformation identification and enabled quantification that was highly comparable to previously obtained results. Broadening application of advanced analytical techniques to study biotherapeutic biotransformation can now more effectively impact drug development by enabling faster design-test-analyze cycle times, critical in early drug discovery settings, opening new avenues for more effective collaboration between analytical chemists and bioconjugate engineers.