Artificial intelligence (AI), machine learning and predictive modeling are becoming enabling technologies in many day-to-day applications. Translation of these advances to the patient’s bedside for AI assisted interventions is not yet the norm. With specific emphasis on CLL, here, we review the progress of prognostic models in hematology and highlight sources of stagnation that may be limiting significant improvements in prognostication in the near future. We discuss issues related to performance, trust, modeling simplicity, and prognostic marker robustness and find that the major limiting factor in progressing toward state-of-the-art prognostication within the hematological community, is not the lack of able AI algorithms but rather, the lack of their adoption. Current models in CLL still deal with the ‘average’ patient while the use of patient-centric approaches remains absent. Using lessons from research areas where machine learning has become an enabling technology, we derive recommendations and propose methods for achieving state-of-the-art predictions in modeling health data, that can be readily adopted by the CLL modeling community.