Lifecycle‐Based Governance to Build Reliable Ethical AI Systems

ABSTRACT Artificial intelligence (AI) systems represent a paradigm shift in technological capabilities, offering transformative potential across industries while introducing novel governance and implementation challenges. This paper presents a comprehensive framework for understanding AI systems through three critical dimensions: trustworthiness characteristics, lifecycle management, and stakeholder ecosystem. We systematically analyze the technical and operational requirements for robust, reliable, and ethical AI deployment, drawing upon established industry practices while addressing contemporary challenges. The framework emphasizes the dynamic nature of AI systems compared with traditional software, particularly in their data dependencies, continuous learning requirements, and probabilistic outputs. For organizational leaders, we provide actionable insights into risk mitigation, compliance strategies, and governance structures necessary for responsible AI adoption. The paper concludes with strategic recommendations for aligning AI initiatives with business objectives while maintaining ethical standards and regulatory compliance. To enhance practical relevance, the analysis is supplemented with brief case vignettes from manufacturing, finance, healthcare, and public administration, which illustrate how the framework reveals hidden risks and guides effective interventions. The conceptual model and real‐world examples offer an integrated roadmap for researchers and practitioners.