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

Abstract

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.