Operationalizing Generative AI in Software Product Management: A Review of Managerial Use-Cases, Governance, and Ethical Guardrails

This paper synthesizes recent evidence on how generative AI reshapes software product management across discovery-to-delivery workflows, emphasizing managerial deci- sions, outcomes, and governance. Drawing on studies spanning market analysis, positioning, customer insight, requirements en- gineering, Agile execution, UI/UX, and engineering productivity, the paper maps concrete applications to established product management domains and highlights observable effects on ef- ficiency, quality, and customer experience. Using change and strategy lenses, the analysis outlines adoption prerequisites— strategy alignment, role design, process integration, data readi- ness, and risk controls—alongside an ethics agenda covering bias, privacy, accountability, and IP exposure. The contribution distills a practical blueprint for product leaders: where to deploy generative AI for business impact, how to embed it within portfolio and lifecycle decisions, and which guardrails enable responsible scaling. The primary contribution of this paper is a novel conceptual framework that integrates GenAI capabilities into the ISPMA lifecycle, providing a structured model for adoption, governance, and impact assessment for both researchers and practitioners.