Therapeutic mAbs or biologics as well as engineered, bispecific and conjugate antibody-drug combinations are used to treat cancer, immune-mediated indications, infections and rare diseases. The mAbs, which are present in many complex, protein-rich biological matrices, require a holistic bioanalytical approach to address pharmacokinetics/pharmacodynamics (PK/PD), immunogenicity, biomarkers, molecular integrity, and drugs and drug-related species. This review discusses emerging methods expected to shape the future of mAb bioanalysis from time-consuming workflows to digital, miniaturized and automated processes, providing enhanced information and greater productivity. In particular, it describes AI/ML, automation and high-throughput platforms, microsampling, advanced MS and integrated digital workflows.AI/ML applications have demonstrated improved analysis of complex datasets, including Raman-based prediction with 2.4% error versus 14.6% using linear analysis, XGBoost-based N-glycan quantification consistent with LC–MS (R² >0.95), and convolutional neural networks achieving R² values of 0.94–0.99 for charge-variant and protein monitoring. However, most applications remain centered on process monitoring and physicochemical characterization rather than fully validated clinical bioanalysis. Automated and high-throughput LC–MS and immunoassay workflows improve throughput, reproducibility, and operational consistency, with applications including therapeutic drug monitoring of free and total infliximab. Microsampling approaches such as dried blood spots, volumetric absorptive what is the future of central laboratories? Microsampling, and dried capillary blood allow low-volume and decentralized sampling, with the reported strong serum concordance (R >0.95) and high ADA agreement. Nevertheless, many challenges are faced with matrix effects, hematocrit, stability, extraction efficiency, validation and regulatory acceptance. Success in the future will rely on integrated, standardized, interpretable, and fit-for-purpose workflows synergistically leveraging microsampling, automation, advanced MS, and AI-based data interpretation.