Institutional AI Adaptation Under Uncertainty: From Implementation Plans to Adaptation Pathways

Institutional discussions of artificial intelligence (AI) frequently begin with adoption and readiness: whether an organisation possesses the conditions required to deploy a technology effectively. This paper asks what changes when implementation is treated as part of a longer process of institutional adaptation. Drawing on an exploratory inquiry involving educational institutions and integrating research on AI readiness, organisational learning, digital transformation, path dependence, real options and decision-making under deep uncertainty, it develops an adaptation-pathway perspective. The argument is that AI interventions can alter the capabilities, routines, governance arrangements, knowledge and dependencies from which later decisions are made. Institutions therefore face two linked problems: recognising and comparing plausible pathways from their present conditions, and revisiting those pathways as both institutional states and external circumstances evolve. Four conceptual principles concern recursive readiness, intervention sequence, pathway discovery and the contingent value of flexibility. The paper offers an analytical synthesis, not a validated model or evidence that any pathway yields superior outcomes.