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AI-Native Product Management: A Vision

arXiv 2610.14823 ↗First posted 10 Oct 2026cs.SE cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 38 references that are in the map. First posted on arXiv 10 Oct 2026.

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References · 5

  1. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  2. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167
  3. 2024Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investorscited by 9
  4. 2025Diverse AI Personas Can Mitigate the Homogenization Effect in Human-AI Collaborative Ideationcited by 3
  5. 2026The AI-Native Large-Scale Agile Software Development Manifestocited by 2

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