Trustworthy Generative Artificial Intelligence for Enterprise Applications: A Conceptual Framework for Governance, Reliability, and Human-Centered Deployment

Abstract

The rapid diffusion of generative artificial intelligence across enterprise environments has outpaced the governance structures required to ensure that its outputs remain reliable, explainable, and aligned with organizational and societal values. This paper addresses the resulting trust deficit by asking how enterprises can operationalize trustworthy generative artificial intelligence in a manner that reconciles technical performance with ethical accountability. Adopting a qualitative, theory-building design grounded in systematic narrative synthesis, the study integrates literature from computer science, information systems, management, and technology ethics to construct an integrated conceptual framework. The review draws upon established risk taxonomies, alignment methodologies such as reinforcement learning from human feedback and retrieval-augmented generation, and regulatory instruments including the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework and the European Union Artificial Intelligence Act. Findings indicate that trustworthy deployment depends on five interdependent pillars: technical reliability, transparency and explainability, governance and regulatory alignment, human-centered oversight, and organizational readiness. These pillars are represented through a lifecycle model comprising design, validation, controlled deployment, continuous monitoring, and audit, connected by a recursive feedback loop. The paper contributes to scholarship by moving beyond fragmented, single-dimension treatments of artificial intelligence trust toward an integrative, enterprise-oriented model that is empirically groundable and practically actionable. It concludes by outlining limitations associated with the conceptual method and by proposing an agenda for future empirical validation across industry sectors.