AI transforms product management with continuous learning and collaboration
AI-Native Product Management: A Vision
Software EngineeringArtificial Intelligence
Summary
Managing product development can be hard because teams build software faster than they decide what to build. This paper suggests making AI an active, ongoing team member in product work to help sense problems, explore ideas, make decisions, build solutions, and learn what works. The authors propose using AI personas and agents that work alongside human professionals in a shared context to improve product management. They also identify new research questions that need to be tested in practice.
What this means in practice
- •For product managers: Integrate AI as a continuous teammate to enhance decision-making and learning across product lifecycle phases in complex software projects.
- •For software development teams: Adopt AI-driven operating models involving AI agents and personas to streamline collaboration and speed software realization responsibly.
A position paper. It proposes an approach and reports no results.
Authors
Ricardo Britto
Abstract
Artificial intelligence is increasingly used to support product management tasks. At the same time, AI-native research and development (R&D) may increase software realization capacity. Organizations may realize software faster than they can identify, qualify, and reconsider what should be realized. This paper asks how product management should be re-envisioned under these conditions. We synthesize research on software product management, product learning, human-AI collaboration, and the Product Management and R&D boundary. We propose a vision in which AI becomes a persistent participant in product work. The vision covers sensing, exploration, decision-making, realization, and learning. We propose six principles and make the vision tangible through an operating model grounded in human professionals, AI personas, agents, skills, and a shared product context. We further argue that AI-native Product Management and AI-native R&D can form a continuous product-learning process. Finally, we identify four AI-specific research directions that require empirical investigation.