Background: Rapid advances in artificial intelligence (AI) have accelerated transformation across sectors—especially healthcare, retail supply chains, and financial services—producing both demonstrable operational gains and novel systemic risks (Khemasuwan & Colt, 2021; Reynolds, 2024). The convergence of generative models, federated learning, and large-scale analytics requires new frameworks that integrate technical architectures, governance, and domain-specific constraints.

Objective: This article synthesizes multidisciplinary evidence from recent applied studies, industry reports, and methodological reviews to present a coherent conceptual and operational framework for deploying AI in high-stakes domains—healthcare and finance—while accounting for data governance, model robustness, and organizational adoption processes. The aim is to move beyond descriptive summaries to produce actionable methodological guidance suitable for researchers, practitioners, and policy makers.

Methods: We conduct a structured, theory-driven synthesis of the supplied literature, mapping empirical findings and conceptual contributions onto three layers: (1) data and infrastructure (including database management and federated learning), (2) algorithmic systems (including large language models and domain-adapted generative AI), and (3) governance and organizational pathways (including compliance, risk management, and human–AI collaboration). Each layer is examined for technical requirements, failure modes, mitigation strategies, and measurable outcomes, with claims tied to the provided sources.

Results: The synthesis highlights common enablers—scalable data architectures, domain-specific model fine-tuning, and federated approaches for privacy-preserving learning—and recurring challenges—data quality, distribution shift, regulatory complexity, and the skills gap among professionals (Batra, 2018; Kaur et al., 2024; Shounik, 2025). Domain-specific vignettes illustrate how AI improves diagnostics and operational throughput in healthcare while simultaneously demanding robust validation and clinical governance (Cleveland Clinic, 2024; Mayo Clinic Press Editors, 2024; Daley, 2024). In finance, generative AI and advanced analytics enable faster due diligence and anomaly detection but require layered controls to prevent model-induced fraud and systemic concentration risks (Sergiienko, 2024; Paleti et al., 2021).

Conclusions: Realizing AI’s promise necessitates integrated architectures that combine scalable databases, privacy-preserving training (federated learning), domain-aligned model evaluation, and explicit human oversight. Implementation roadmaps must prioritize data hygiene, incremental validation, workforce reskilling, and governance protocols that reconcile business agility with safety and compliance. We offer a comprehensive operational checklist and research agenda to guide next-phase deployments and empirical evaluation.

Artificial intelligence, healthcare, finance, federated learning

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