The Role of AI in Adaptive Project Management: Automating Dynamic Task Prioritization Based on Real-Time Data

Authors

  • Emily Turner PhD, Senior Research Scientist, Institute of Project Management, Toronto, Canada Author

Keywords:

Artificial Intelligence, adaptive project management

Abstract

This paper explores the transformative role of Artificial Intelligence (AI) in adaptive project management, specifically focusing on the automation of dynamic task prioritization based on real-time data. In fast-paced industries where project requirements can change rapidly, traditional project management methods often struggle to keep pace. This research discusses how AI systems can analyze real-time data and automatically adjust task prioritization to enhance project efficiency and responsiveness. By integrating AI into adaptive project management methodologies, organizations can improve decision-making processes, optimize resource allocation, and ultimately achieve better project outcomes. The findings underscore the potential of AI to create a more agile project management framework that is capable of responding to dynamic market demands and operational challenges.

Downloads

Download data is not yet available.

References

Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.

Alluri, Venkat Rama Raju, et al. "DevOps Project Management: Aligning Development and Operations Teams." Journal of Science & Technology 1.1 (2020): 464-487.

Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.

Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.

Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.

Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.

Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.

Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.

Katari, Pranadeep, et al. "Remote Project Management: Best Practices for Distributed Teams in the Post-Pandemic Era." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 145-167.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

K. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: MIT Press, 2012.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. 25th Int. Conf. Neural Inf. Process. Syst., 2012, pp. 1097–1105.

J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85-117, Jan. 2015.

S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Upper Saddle River, NJ, USA: Prentice Hall, 2020.

M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.

G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: With Applications in R, 2nd ed. New York, NY, USA: Springer, 2021.

C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.

R. D. Luque, M. Carrión, and C. L. Castillo, "Ethics in artificial intelligence: An overview of ethical theories and models," Int. J. Interact. Multimedia Artif. Intell., vol. 5, no. 5, pp. 4-14, Jan. 2019.

S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.

Y. Bengio, "Learning deep architectures for AI," Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1-127, 2009.

A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd ed. Sebastopol, CA, USA: O'Reilly Media, 2019.

A. Holzinger, P. Kieseberg, A. Weippl, and E. Tjoa, "Current advances, trends and challenges of machine learning and knowledge extraction: From machine learning to explainable AI," in Lecture Notes in Computer Science, vol. 11015. Cham, Switzerland: Springer, 2018, pp. 1-8.

Downloads

Published

13-12-2023

How to Cite

[1]
E. Turner, “The Role of AI in Adaptive Project Management: Automating Dynamic Task Prioritization Based on Real-Time Data”, J. of Artificial Int. Research and App., vol. 3, no. 2, pp. 719–725, Dec. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/254

Similar Articles

51-60 of 139

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