# A systematic review of generative AI usage for IT project management

arXiv: https://arxiv.org/abs/2604.21958 · HTML: https://mlsanity.com/paper/2604.21958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [Using LLMs in Software Requirements Specifications: An Empirical Evaluation](https://mlsanity.com/paper/2404.17842/) (2024)
- [Large Language Models Versus Static Code Analysis Tools: A Systematic Benchmark for Vulnerability Detection](https://mlsanity.com/paper/2508.04448/) (2025)
- [An Empirical Study on the Effectiveness of Large Language Models for SATD Identification and Classification](https://mlsanity.com/paper/2405.06806/) (2024)
- [Cognitive Agents Powered by Large Language Models for Agile Software Project Management](https://mlsanity.com/paper/2508.16678/) (2025)
- [Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey](https://mlsanity.com/paper/2505.07058/) (2025)
- [Generative AI for Software Project Management: Insights from a Review of Software Practitioner Literature](https://mlsanity.com/paper/2510.10887/) (2025)
- [Enhancing Team Diversity with Generative AI: A Novel Project Management Framework](https://mlsanity.com/paper/2502.05181/) (2025)

## Abstract (arXiv)

This paper aims to synthesize current knowledge on generative AI in IT project management using the PRISMA methodology to provide researchers with a comprehensive perspective on techniques, applications, adoption trends, limitations, and integration across project management tools and process groups. The analysis reveals a clear dominance of OpenAI's GPT in the included studies but relying primarily on prompt engineering, suggesting that research in this area remains at an exploratory stage. Finally, it identifies and discusses three promising research directions for AI-enabled project management, including process group-specific AI agents, project role-based AI agents, and hybrid collaborative networks that enable human-guided orchestration.