DOI: 10.36347/sjams.2023.v11i12.021

Pages: 2119-2130

As global healthcare systems face increasing pressure to do more with less, Artificial Intelligence (AI) has emerged as a promising solution. Yet, despite growing investment, most evaluations of AI in hospitals remain narrow, focused almost exclusively on financial metrics, while overlooking critical human and systemic dimensions. This paper challenges that status quo by introducing a multidimensional framework for evaluating AI’s return on investment (ROI), one that integrates performance data with staff experience, leadership alignment, and patient-centered outcomes. Using Appreciative Inquiry as the conceptual framework, the study employed a qualitative, multi-source methodology. It included document analysis, thematic synthesis of relevant case studies, and process visualization using P-charts to assess institutional performance across three domains: compliance with patient safety protocols, staff engagement in AI-led initiatives, and timeliness of patient discharges. Over a 12-month observation period, safety non-compliance rates trended downward and remained within control limits, while staff engagement demonstrated consistently high participation with minimal variation. Discharge timeliness exceeded 90% in most months, driven by AI-supported coordination and predictive analytics. Drawing insights from healthcare, education, and digital ethics, this paper proposes that the ROI of AI extends beyond conventional financial returns to include organizational resilience and human-centered transformation. The study recommends that healthcare institutions adopt participatory leadership models, prioritize digital readiness, and use appreciative, systems-based frameworks to evaluate AI implementation. It concludes by calling for a redefinition of ROI that encompasses cultural alignment, ethical governance, and long-term institutional learning.