Abstract

Purpose: This study examines the potential of integrating Generative Artificial Intelligence (GenAI) into IT project management, with a view to identifying how it could transform project management processes. Design/methodology/approach: A case study approach was employed in order to analyse IT companies across a variety of industries. In order to gain a comprehensive understanding of GenAI's applications and impact, the research combined qualitative interviews with project managers and technical leads with quantitative analysis of project performance metrics. Findings: The results demonstrate GenAI's capacity for markedly enhancing project management, encompassing enhanced project efficiency, more effective risk management, and more efficacious stakeholder communication. Key applications include predictive analytics for risk identification, resource optimisation algorithms to mitigate bottlenecks, and automated quality assurance tools for defect detection. However, challenges such as data quality, algorithmic bias, organisational resistance, and the necessity for transparent AI frameworks were also identified. Research limitations/implications: The findings, based on IT companies, may have limited generalizability to other industries. The study primarily addresses short-term impacts, with long-term implications yet to be explored. Future research should examine GenAI's applicability in different sectors, its ethical considerations, scalability, and integration with traditional project management frameworks. Practical implications: Organisations can use GenAI to overcome long-standing project management challenges. The case study examples presented in the article demonstrate GenAI's ability to manage the complex dynamics of IT projects, making it an invaluable tool for IT professionals seeking to optimise project outcomes. Originality/value: This study contributes to the limited research on GenAI in IT project management by presenting empirical evidence from case studies. It offers actionable insights for practitioners and proposes directions for future research, including exploring long-term impacts, ethical implications, and hybrid methodologies integrating GenAI with traditional frameworks.

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