Business Intelligence (BI) and Artificial Intelligence (AI) are central to organizational decision-making and digital transformation. Despite widespread adoption, organizations differ significantly in how effectively they develop and integrate these capabilities. These differences are often described as analytics maturity, yet the concept remains theoretically ambiguous and empirically underexplored, particularly in the context of BI–AI convergence. This paper examines how analytics maturity is defined and developed in practice and which conditions shape its evolution. Based on a qualitative multiple-case study with interviews of internal analytics leaders, external consultants, and solution providers across energy, professional services, and software sectors, the study conceptualizes analytics maturity as a systemic organizational capability encompassing data foundations, governance, work practices, and decision-making processes. The findings show that analytics maturity is not a linear progression toward advanced techniques, but a context-dependent and uneven capability shaped by data quality, leadership support, organizational learning, and trust in analytics outputs. Environmental factors such as regulation act primarily as boundary conditions. Drawing on the Technology–Organization–Environment framework and Dynamic Capabilities Theory, the study develops an integrated TOE \(\times \) DCT framework that conceptualizes analytics maturity as the alignment between contextual conditions and dynamic capability processes. This framework advances theory beyond technology adoption and provides practical insights for building sustainable BI and AI capabilities.