<p>As artificial intelligence (AI) systems become increasingly integrated into products and services, the challenge of operationalizing ethical principles throughout the development lifecycle has become urgent. While numerous high-level guidelines articulate normative ideals, their translation into actionable development practices remains limited. This systematic literature review analyzes 62 peer-reviewed frameworks published between 2015 and 2024 that aim to integrate ethical considerations into AI-driven product development. The study combines quantitative coding with thematic analysis to assess each framework across four dimensions: lifecycle-phase coverage, ethical-principle integration, stakeholder-responsibility distribution, and empirical validation. The review applies two analytic techniques—the Gap Map and the Principle–Practice Spectrum—which help reveal structural asymmetries and blind spots in how ethics is considered in the analysed frameworks. Key findings include a disproportionate emphasis on mid-lifecycle phases, developer-centric allocation of ethical responsibility, and limited engagement with high-stakes principles such as justice or sustainability. Fewer than half of the frameworks demonstrate empirical validation. By developing a typology and mapping gaps at the intersections of ethical principles, stakeholder roles, and lifecycle phases, this review contributes a methodological foundation for evaluating the feasibility of ethical AI frameworks and offers actionable insights for policymakers and practitioners engaged in AI product development.</p>