Structured AI Integration Maturity: A Conceptual Framework for Diagnosing Governance-Adoption Gaps in Postsecondary Institutions

This working paper introduces the Structured AI Integration Maturity (SAIM) framework, a conceptual diagnostic model for identifying governance-adoption gaps in postsecondary AI implementation. The framework distinguishes visible AI adoption activity from functionally governed integration and argues that AI use can expand across faculty and administrative roles before institutions have developed the governance structures needed to support clear, accountable, and sustainable practice. Grounded in industrial-organizational psychology and institutional theory, SAIM proposes a testable pathway in which governance maturity is associated with lower role ambiguity, which is in turn associated with lower workforce destabilization signals. The paper also introduces an eight-entry governance failure mode taxonomy and identifies adoption visibility bias as a masking dynamic through which institutions may mistake visible AI activity for actual integration maturity. This manuscript is a conceptual framework and working paper. It does not report empirical validation data. Future phases of the research agenda will focus on instrument development, cross-sectional testing, longitudinal validation, and practical diagnostic tool development for postsecondary institutions.