Organizational AI Readiness Maturity Model (AIRMM): A Conceptual Framework Bridging AI Readiness and Maturity Perspectives

Abstract

Artificial intelligence (AI) adoption requires more than technological availability; it depends on organizational conditions related to strategy, resources, knowledge, culture, data, and processes. Existing AI readiness frameworks primarily identify these conditions, while AI maturity models generally assess the sophistication of AI capabilities and use. This study addresses the conceptual space between these two perspectives by proposing an Organizational AI Readiness Maturity Model (AIRMM). Drawing on organizational readiness theory, AI readiness research, and maturity-model literature, the AIRMM conceptualizes organizational preparedness as a progressive and multidimensional construct. The proposed framework comprises six dimensions—strategic alignment, resources, knowledge, culture, data, and process readiness—and four maturity levels ranging from Fragmented to Institutionalized. Rather than treating organizations as simply ready or not ready, the model assesses the degree to which readiness conditions are developed, formalized, integrated, and institutionalized. The AIRMM contributes to the literature by introducing a developmental perspective on organizational AI readiness and distinguishing readiness maturity from broader AI maturity. As a conceptual framework, it also provides a basis for organizational diagnosis and future empirical validation.