Adoption of Large Language Models in Scrum Management: Insights from Brazilian Practitioners

TL;DR

The paper investigates how Brazilian Scrum practitioners adopt Large Language Models for management activities, addressing the empirical gap on real-world use. It employs a Goal-Question-Metric driven survey of 70 professionals, with 49 active Scrum participants and 33 using LLM assistants, to map familiarity, usage across artifacts, events, and roles, and to identify perceived benefits and risks. Findings reveal widespread, routine use with LLMs aiding exploration and artifact refinement and yielding productivity and quality benefits, but also frequent near-correct outputs and concerns about confidentiality and hallucinations. The work provides an initial empirical characterization of LLM adoption in Scrum management, offering practical implications for governance, literacy, and responsible integration, and outlining avenues for longitudinal and mechanism-focused future research.

Abstract

Scrum is widely adopted in software project management due to its adaptability and collaborative nature. The recent emergence of Large Language Models (LLMs) has created new opportunities to support knowledge-intensive Scrum practices. However, existing research has largely focused on technical activities such as coding and testing, with limited evidence on the use of LLMs in management-related Scrum activities. In this study, we investigate the use of LLMs in Scrum management activities through a survey of 70 Brazilian professionals. Among them, 49 actively use Scrum, and 33 reported using LLM-based assistants in their Scrum practices. The results indicate a high level of proficiency and frequent use of LLMs, with 85% of respondents reporting intermediate or advanced proficiency and 52% using them daily. LLM use concentrates on exploring Scrum practices, with artifacts and events receiving targeted yet uneven support, whereas broader management tasks appear to be adopted more cautiously. The main benefits include increased productivity (78%) and reduced manual effort (75%). However, several critical risks remain, as respondents report 'almost correct' outputs (81%), confidentiality concerns (63%), and hallucinations during use (59%). This work provides one of the first empirical characterizations of LLM use in Scrum management, identifying current practices, quantifying benefits and risks, and outlining directions for responsible adoption and integration in Agile environments.

Figures (4)

• Figure 1: Methodological workflow of the questionnaire survey.
• Figure 2: LLM uses across Scrum activities: learning, artifacts, events, and management tasks.
• Figure 3: Non-planned, planned, and current use of LLMs across categories.
• Figure 4: Perceived benefits and challenges associated with the use of AI-based assistants.