From AI Maturity to Reopenable Decisions: The AI Native Transition Method v0.2 as an Open, Workflow-Level Decision Artifact

Enterprise AI strategies are commonly organized as maturity assessments, project portfolios, risk frameworks, or technology roadmaps. These approaches are valuable, yet they leave a recurrent managerial problem under-specified: when AI capabilities, costs, platform features, legal conditions, and organizational constraints change, how should a firm decide whether to invest in a workflow, how to source the required capability, and how to redesign the work without treating those questions as one irreversible technology choice? This working paper presents the AI Native Transition Method v0.2, an open, vendor-neutral design artifact whose primary unit of analysis is a bounded workflow. The method separates three decision axes—investment, sourcing, and work design—subjects them to explicit evidence gates, records their rationale in a versioned Decision Record, and reopens only the affected decision when a predefined trigger occurs. The artifact was developed through an Action Design Research orientation and refined through three retrospective public-case stress tests and a non-blind virtual portability pretest. The current evidence supports conceptual usefulness and traceability, but not causal effectiveness, academic validation, certification, or standard status. The paper contributes a decision-centered definition of AI Native, a workflow-level decision architecture for a moving capability frontier, the mechanism of selective decision reopening, and an open evaluation programme for independent testing. Seven falsifiable propositions and a staged validation protocol are provided.