Application of Large Language Models in Product Management: A Systematic Literature Review
Paper in proceeding, 2026

This systematic literature review analyzes how Generative AI (GenAI), specifically Large Language Models (LLMs), impacts Software Product Management (SPM). Based on recent studies, our analysis reveals that current research is in a nascent, experimental phase, focusing on task-level applications like requirements engineering and user persona generation, which show potential for efficiency gains. However, the field suffers from critical limitations, including a lack of methodological standardization, an over-reliance on a narrow range of LLMs like ChatGPT, and a focus on superficial efficiency metrics over measures of product success. We conclude that while LLMs show promise for discrete PM tasks, their true transformative potential lies in integrated, workflow-centric systems. This paper provides a baseline of the current research and calls for a more rigorous, impact-focused agenda for future studies.

Software Engineering

Systematic Literature Review

Product Management

Generative Artificial Intelligence

Author

Vitor Mori Serra

Eindhoven University of Technology

Jan Bosch

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

University of Gothenburg

Helena Holmström Olsson

Malmö university

Lecture Notes in Computer Science

0302-9743 (ISSN) 1611-3349 (eISSN)

Vol. 16361 LNCS 319-333
9783032120885 (ISBN)

26th International Conference on Product-Focused Software Process Improvement, PROFES 2025
Salerno, Italy,

Subject Categories (SSIF 2025)

Artificial Intelligence

DOI

10.1007/978-3-032-12089-2_20

More information

Latest update

12/23/2025