Large language models in Scrum management: A practitioner survey of use, perceived benefits, risks, and human–AI collaboration

Context: Scrum management includes knowledge-intensive activities such as backlog refinement, event preparation, and stakeholder alignment. Large language model (LLM) chat assistants may support these activities, but empirical evidence remains limited. Objective: This study investigates practitioners’ self-reported LLM use in Scrum management, perceived benefits and risks, and expectations for human–AI collaboration. Methods: We conducted a practitioner survey, and the results are based on a final analytic sample of 79 respondents who reported using LLM chat assistants in Scrum management. Closed-ended items were analyzed descriptively; open-ended responses underwent thematic coding. Results: In this sample, 48.1% reported daily or almost daily use, and 79.7% rated their LLM knowledge as at least qualified. Reported use centered on backlog refinement, event summarization, and Scrum-practice exploration. Perceived benefits included productivity (77.2%), reduced repetitive-task time (75.9%), and artifact quality (74.7%). Reported problems included almost-correct outputs (65.8%), hallucinations (54.4%), privacy concerns (50.6%), and content-validation difficulty (46.8%). Most respondents envisioned human-led or balanced human–AI collaboration (81.0%); 65.8% selected the no-role-replacement option. Conclusion: Respondents describe LLM chat assistants as complementary tools in Scrum management, particularly for text-intensive and analytical tasks, while emphasizing the continued need for human judgment, validation, and oversight.