Coronary artery disease (CAD) requires complex, high-stakes clinical decision-making, and healthcare organizations are increasingly exploring artificial intelligence (AI)–based clinical decision support systems (CDSS). However, pre-implementation adoption is shaped not only by technical capability but also by social, professional, and organizational processes. To explore multi-stakeholder perceptions and readiness toward an AI-based CDSS for CAD prior to implementation. We conducted semi-structured interviews with clinicians (n=7), hospital administrators (n=5), and healthcare IT/data personnel (n=3). Data were analyzed using an interpretivist, reflexive thematic analysis. We focused on how stakeholders made meaning of AI-based decision support, including perceived roles, conditions for trust, implications for professional autonomy and accountability, and organizational readiness. Five themes were identified: (1) AI as a supportive—not decisive—actor in clinical practice; (2) trust as a conditional and socially constructed process, linked to transparency and institutional endorsement; (3) professional autonomy and accountability tensions, with persistent responsibility ambiguity; (4) organizational readiness and institutional constraints, particularly workflow integration, training, governance, and data infrastructure; and (5) divergent expectations across stakeholder groups that risk misalignment during implementation. Stakeholders were cautiously open to an AI-based CDSS for CAD, but framed adoption as contingent on governance, accountability clarity, and fit within existing clinical workflows. Pre-implementation qualitative inquiry can surface misalignments and guide socio-technical implementation strategies that strengthen trust, preserve professional judgment, and reduce organizational risk.