The Future of AI-Powered Healthcare Analytics: Architecture, Governance, and Business Value

The swift adoption of artificial intelligence (AI) in healthcare analytics is limited by disjointed data architecture, inconsistent governance practices, and poorly defined schemes of value realisation. Current studies cover these dimensions separately, which restricts the possibility of scalable and responsible implementation. The proposed work suggests a unified multi-layer system that brings together interoperable data fabrics, decentralised learning, advanced AI modelling, governance automation, and value measurement in a coherent system. The architecture will be an FHIR-based interoperability layer, federated learning, foundation models, and human digital twins, and it will be managed by an embedded MLOps-driven lifecycle using risk-based regulatory principles. Construct synthesis and artefact development were developed through the use of a structured integrative literature review (2015–2026) and design science. The resultant framework makes governance checkpoints operational in the AI lifecycle and creates quantifiable avenues between technical architecture and operational effectiveness, clinical utility, and strategic change. The 2025–2035 roadmap provides phases for optimising the infrastructure of AI-native healthcare systems. The suggested model offers a blueprint for systems-level scalable, privacy-preserving, and economically sustainable deployment of AI in healthcare.