AI-Native Product Management: A Vision
Organizations: Ericsson, Sweden
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.
Figures & tables
| Synthesized theme | Vision element | Literature foundation |
|---|---|---|
| End-to-end product responsibility | Human product judgment; Product Management professional perspectives | SPM scope and organizational capability [ 32 , 12 , 22 ] . |
| Continuous evidence-driven exploration and learning | Continuous sensing; exploration; prototyping; challenge; lifecycle learning | Market sensing, exploration, experimentation, and organizational learning [ 9 , 26 , 3 , 23 ] . |
| Persistent rather than task-triggered AI participation | Persistent AI participation; expanded strategic exploration | AI in product management and strategic decision-making [ 34 , 25 , 8 ] . |
| Different levels of AI initiative and human judgment | AI-operated, human-directed, and collaborative activities | Human–AI collaboration and delegation [ 27 , 1 , 14 ] . |
| Continuity between product and technical reasoning | Product Management–R&D interleaving; shared semantic context; living product intent | Knowledge boundaries and AI-native R&D [ 7 , 2 , 4 ] . |
| Expected benefit | How the principles contribute |
|---|---|
| Better sensing and strategic exploration | Connect customer, competitor, market, technology, and product signals and stress-test direction before commitment. |
| Faster learning before commitment | Make important assumptions testable through rapid prototypes. |
| More adaptive product direction | Keep evidence, assumptions, decisions, and changing conditions connected. |
| Better Product Management–R&D collaboration | Make product rationale, technical constraints, and uncertainty more visible. |
| End-to-end product learning | Connect opportunities and decisions with realization and observed outcomes. |