The Barcelona Clinic Liver Cancer (BCLC) classification has been the mainstay for prognostic assessment and initial treatment selection in hepatocellular carcinoma (HCC) for more than two decades. It is widely clinically accepted and has been reaffirmed in the recently renewed European Association for the Study of the Liver (EASL) Clinical Practice Guidelines on the management of HCC. Its design is based on simple clinical and imaging parameters, which makes it highly applicable in clinical routine. However, it does not fully utilize all information, which is potentially encoded in routine radiology imaging. With artificial intelligence (AI) methods now maturing, we have a robust way to extract and quantify digital imaging features fully automatically without much user input and with high precision. Therefore, AI could bridge quantitative imaging into clinical decision-making, together with the existing BCLC classification. However, despite substantial AI advancements in many fields such as automated tumor volumetry, radiomics, detection of metastatic lesions, and even capturing opportunistic imaging biomarkers, a translational gap persists. While challenges related to technical, administrative, and cost-related, but also training-related factors have to be taken into account, a certain aversion to change, as well as absence of standardized AI validation and missing workflow integration hamper the clinical implementation in routine care. This article aims to evaluate current AI-quantified imaging parameters and their potential for synergy with the established BCLC classification.