A technology-oriented approach to AI predominates in research and practice, yet despite a high level of technological readiness, projects often fail due to poor domain-specific problem framing and data quality in early-stage AI system development. This contribution conducts an analysis of existing AI-related readiness models, to identify gaps in addressing these factors. The use case-centered AI readiness level framework is proposed on the basis of these findings – a unified, evidence-based model that links problem, data, and technology readiness across planning and implementation stages.
