Abstract

This research-in-progress delves into the potential integration of Large Language Models (LLMs), exemplified by ChatGPT, into agile project management methodologies. The study outlines the promising applications, anticipated benefits, and inherent challenges of such an integration while emphasizing the importance of a balanced approach. Drawing insights from an extensive literature review, a conceptual framework is being developed to provide a comprehensive understanding of this integration, and empirical research methods will be employed for validation. The intersection of Artificial Intelligence (AI) and Project Management through LLM integration holds significant implications for both fields. In the context of agile projects, LLMs like ChatGPT offer diverse applications, from backlog management in Scrum and Kanban to assisting in sprint planning, daily standup meetings, retrospectives, pair programming, and more. However, the introduction of LLMs also necessitates careful consideration of their limitations and the essential role of human oversight to mitigate potential risks. This study places special emphasis on achieving a balanced integration that capitalizes on the numerous benefits of LLMs in agile project management. These benefits include improved efficiency, proactive risk management, enhanced collaboration, data-driven decision-making, and scalability. Nevertheless, challenges such as job displacement, privacy concerns, ethical implications, and technical complexities need to be addressed to ensure responsible and sustainable integration. The conceptual framework which is being developed through a synthesis of existing theories and identified research gaps aims to provide a structured lens for understanding the multifaceted implications of LLM integration. Empirical research methods, such as interviews, focus groups, and surveys, will subsequently be employed to validate and refine the framework's practicality by engaging project managers, practitioners, and stakeholders. While acknowledging potential limitations, such as participant bias and contextual influences, this research strives to offer valuable insights for both academia and industry. By bridging theory and practice, it contributes to the evolving landscape of AI-driven project management. The study's core objective revolves around fostering sustainable agile project management outcomes through the responsible integration of LLMs. By harnessing the benefits of LLMs and addressing challenges, organizations can envision a future where AI augments agile practices, leading to more efficient, effective, and ultimately sustainable project management approaches.

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