Abstract
<title>Abstract</title> <p> <bold>Background</bold> Most healthcare AI initiatives fail to deliver sustained organizational value, and the causes are structural rather than technological. Data fragmentation, workforce unpreparedness, governance deficits, process complexity, and vendor ecosystem burden are the primary conditions that determine whether AI performs reliably at scale. These conditions are rarely assessed before AI investment is committed. <bold>Objective</bold> This paper introduces the Digital Transformation Debt (DT-Debt) framework, a structured organizational assessment tool built around seven dimensions of accumulated digital burden. The framework positions any organization on a cumulative debt scale, classifies it into one of four archetypes, and uses that archetype to drive specific AI readiness guidance and remediation priorities. <bold>Methods</bold> An integrative evidence synthesis was conducted drawing on peer-reviewed literature, policy frameworks, and market survey and thought leadership by leading consulting firms. Six recurring AI value gap domains were identified and characterized. The DT-Debt framework was constructed across seven sociotechnical dimensions, each assessed through observable organizational signals. The cumulative debt level across the seven dimensions positions the organization in one of four archetypes: Transformation-Constrained, Partially Modernized, Functionally Ready, and AI-Ready. Three synthetic health system profiles demonstrate the framework in practice. <bold>Results</bold> Six structural AI value gaps are identified and mapped to specific DT-Debt dimensions. The cumulative archetype scale differentiates AI deployment risk with sufficient precision to drive distinct remediation and deployment pathways. Each archetype is characterized by a specific organizational profile, a set of AI failure modes it predicts, and a clear set of immediate priorities and leading indicators for progress. <bold>Conclusions</bold> The DT-Debt framework reframes AI readiness as a diagnosable organizational condition. Knowing where an organization sits on the cumulative debt scale, and why it sits there, gives health system leaders the specific information they need to sequence AI investment intelligently, target remediation where it predicts the highest failure risk, and build the organizational capacity that AI requires to perform reliably. </p>