PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 28, 20260 citationsOpen Access

When AI Helps, When It Hurts: A Contextual Research Framework for Integrating Artificial Intelligence into Agile Scrum Workflows

View Full Paper
FRFilip RadulovićTKTomaž Klobučar

Key Points

  • The aim is to explore how contextual factors influence the effectiveness of AI in Agile Scrum workflows.
  • Conducted a literature review on AI and Agile Scrum
  • Proposed a research framework for contextual analysis
  • Developed a mixed-methods approach combining interviews and surveys
  • Identified organizational and team-level conditions that affect AI's impact
  • Highlighted the inconsistent effects of AI on efficiency and collaboration
  • Set the foundation for future quantitative research on AI integration in Agile environments

Abstract

As artificial intelligence (AI) increasingly enters agile project management environments, its impact remains inconsistent, boosting efficiency in some cases while disrupting collaboration in others. Rather than assuming AI’s universal benefit, existing literature challenges this assumption and opens a knowledge gap for investigating the organizational and team-level conditions that moderate AI effectiveness. This paper conducts a literature review and proposes a research framework based on the constructs for the contextual analysis of AI in Agile Scrum, exploring when, how, and for whom AI integration enhances or hinders Agile Scrum workflows. It proposes a methodology for future research that is a quantitative-dominant mixed-methods approach, combining semi-structured interviews with a structured survey. The aim of this paper is to identify the contextual factors that shape AI’s impact in Agile Scrum, which will serve as the basis for the research framework.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Radulović et al. (2025) studied this question.

synapsesocial.com/papers/69a287130a974eb0d3c028c0https://doi.org/10.54820/entrenova-2025-0058
Ask AI
Helpful
Bookmark
Share
View Full Paper