Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI in Agile Project Management: Automating Sprint Planning and Progress Tracking

PDF
  • Dr. Emily Carter
Dr. Emily Carter
Assistant Professor, Department of Information Systems, University of Toronto, Toronto, Canada

Published 28-09-2024

Keywords

  • Artificial Intelligence,
  • Agile Project Management
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
D. E. Carter, “AI in Agile Project Management: Automating Sprint Planning and Progress Tracking”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 77–83, Sep. 2024, Accessed: Nov. 07, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/175

Abstract

The advent of Artificial Intelligence (AI) has revolutionized various sectors, including project management. This paper investigates the integration of AI tools in Agile project management, focusing on automating sprint planning and progress tracking. Agile frameworks, characterized by iterative development and flexibility, demand efficient planning and continuous monitoring to ensure team productivity and project success. AI technologies, such as machine learning algorithms and natural language processing, provide solutions to automate routine tasks involved in sprint planning and progress tracking. By analyzing historical project data, AI can predict workload, optimize task assignments, and enhance communication within teams. This research highlights the benefits of incorporating AI into Agile practices, emphasizing increased efficiency, improved decision-making, and enhanced team collaboration. Case studies of organizations successfully implementing AI tools in Agile environments illustrate the tangible benefits achieved, including faster delivery times and higher stakeholder satisfaction. Ultimately, this paper argues that leveraging AI in Agile project management can lead to significant improvements in workflow automation and team performance.

PDF

Downloads

Download data is not yet available.

References

  1. Gayam, Swaroop Reddy. "Deep Learning for Image Recognition: Advanced Algorithms and Applications in Medical Imaging, Autonomous Vehicles, and Security Systems." Hong Kong Journal of AI and Medicine 4.1 (2024): 223-258.
  2. Thuraka, Bharadwaj, et al. "Leveraging artificial intelligence and strategic management for success in inter/national projects in US and beyond." Journal of Engineering Research and Reports 26.8 (2024): 49-59.
  3. Ahmad, Tanzeem, et al. "Sustainable Project Management: Integrating Environmental Considerations into IT Projects." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 191-217.
  4. Nimmagadda, Venkata Siva Prakash. "AI in Pharmaceutical Manufacturing: Optimizing Production Processes and Ensuring Quality Control." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 338-379.
  5. Putha, Sudharshan. "AI-Driven Predictive Analytics for Vehicle Health Monitoring and Diagnostics in Connected Cars." Hong Kong Journal of AI and Medicine 4.1 (2024): 297-339.
  6. Sahu, Mohit Kumar. "AI-Based Supply Chain Optimization in Manufacturing: Enhancing Demand Forecasting and Inventory Management." Journal of Science & Technology 1.1 (2020): 424-464.
  7. Kasaraneni, Ramana Kumar. "AI-Enhanced Virtual Screening for Drug Repurposing: Accelerating the Identification of New Uses for Existing Drugs." Hong Kong Journal of AI and Medicine 1.2 (2021): 129-161.
  8. Pattyam, Sandeep Pushyamitra. "Data Engineering for Business Intelligence: Techniques for ETL, Data Integration, and Real-Time Reporting." Hong Kong Journal of AI and Medicine 1.2 (2021): 1-54.
  9. Pal, Dheeraj Kumar Dukhiram, et al. "AI-Assisted Project Management: Enhancing Decision-Making and Forecasting." Journal of Artificial Intelligence Research 3.2 (2023): 146-171.
  10. Babb, J. (2019). Data quality challenges in AI project management. Data & Knowledge Engineering, 118, 54-63.
  11. O'Brien, J., & McCarthy, S. (2018). Managing change in Agile teams: The role of leadership. Project Management Journal, 49(1), 73-86.
  12. Wood, J., & Toma, S. (2021). Ethics and AI in project management: Navigating challenges. Journal of Business Ethics, 170(2), 345-360.
  13. Revilla, E., & Holgado, C. (2020). The integration of AI in project management: A review of the literature. International Journal of Project Management, 38(2), 165-175.
  14. Kira, D., & Wind, J. (2021). Future of work: How AI transforms Agile project management. International Journal of Project Management, 39(3), 315-328.
  15. Ghafoor, K. (2020). Understanding the benefits of AI in Agile project management. International Journal of Information Systems and Project Management, 8(3), 1-15.
  16. Hossain, M., & Hossain, N. (2019). The impact of machine learning on project management. Journal of Engineering Management, 36(3), 401-415.
  17. Azhar, S., & Arif, M. (2020). Transforming Agile project delivery through AI-driven tools. Journal of Software Engineering Research and Development, 8(2), 51-69.
  18. Wong, C., & Kwan, T. (2021). AI-enhanced methodologies for Agile project management: A systematic review. Project Management Journal, 52(1), 35-50.
  19. Li, Y., & Yang, C. (2021). Machine learning in Agile project management: Opportunities and challenges. Journal of Systems and Software, 171, 110832.
  20. Nguyen, T., & DeVries, M. (2020). Project management automation through AI: Perspectives from Agile teams. International Journal of Information Systems and Project Management, 8(4), 16-29.

Similar Articles

You may also start an advanced similarity search for this article.