Integrating AI into Kanban for Agile Mobile Product Development: Enhancing Workflow Efficiency, Real-Time Monitoring, and Task Prioritization

Authors

  • Seema Kumari Independent Researcher, USA Author

Keywords:

Artificial Intelligence, Kanban, Agile methodologies, mobile product development

Abstract

The integration of Artificial Intelligence (AI) into Kanban systems has emerged as a transformative approach to enhancing workflow efficiency, real-time monitoring, and task prioritization within Agile mobile product development. This paper aims to systematically investigate the intersection of AI and Kanban methodologies, elucidating how these technologies can synergistically improve the performance and adaptability of Agile teams in dynamic mobile development environments. With the increasing complexity of mobile applications and the rapid pace of technological advancements, traditional Kanban practices may fall short in addressing the nuanced challenges that contemporary development teams face. Hence, this research proposes a novel framework that leverages AI capabilities to augment Kanban practices, thus facilitating more intelligent decision-making processes.

Readership Data

🌐

Refreshing Cached Analytics Data

The cached analytics data has become stale and www.thesciencebrigade.com is making a fresh request to fetch the latest data from Google Analytics. This may take 20-30 seconds depending on the server response time from Google Analytics. Please do not close the browser during this time. We appreciate your patience.

Downloads

Download data is not yet available.

References

K. Schwaber and J. Sutherland, "The Scrum Guide," Scrum.org, 2020. [Online]. Available: https://www.scrumguides.org/scrum-guide.html.

M. K. Verma, "Kanban for Software Development: A Comprehensive Guide," International Journal of Software Engineering and Applications, vol. 8, no. 3, pp. 1-12, 2017.

M. R. Poppendieck and T. Poppendieck, Lean Software Development: An Agile Toolkit. Addison-Wesley, 2003.

Mahesh, Madhu. "Broker Incentives and Their Influence on Medicare Plan Selection: A Comparative Analysis of Medicare Advantage and Part D." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 493-512.

J. Singh, “Understanding Retrieval-Augmented Generation (RAG) Models in AI: A Deep Dive into the Fusion of Neural Networks and External Databases for Enhanced AI Performance”, J. of Art. Int. Research, vol. 2, no. 2, pp. 258–275, Jul. 2022

Tamanampudi, Venkata Mohit. "Natural Language Processing for Anomaly Detection in DevOps Logs: Enhancing System Reliability and Incident Response." African Journal of Artificial Intelligence and Sustainable Development 2.1 (2022): 97-142.

Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.

Thota, Shashi, et al. "Few-Shot Learning in Computer Vision: Practical Applications and Techniques." Human-Computer Interaction Perspectives 3.1 (2023): 29-59.

Vaithiyalingam, Gnanavelan. "Bridging the Gap: AI, Automation, and the Future of Seamless Healthcare Claims Processing." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 248-267.

Khan, Samira, and Hassan Khan. "Harnessing Automation and AI to Overcome Challenges in Healthcare Claims Processing: A New Era of Efficiency and Security." Distributed Learning and Broad Applications in Scientific Research 8 (2022): 154-174.

Singh, Jaswinder. "The Ethics of Data Ownership in Autonomous Driving: Navigating Legal, Privacy, and Decision-Making Challenges in a Fully Automated Transport System." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 324-366.

Tamanampudi, Venkata Mohit. "AI-Powered Continuous Deployment: Leveraging Machine Learning for Predictive Monitoring and Anomaly Detection in DevOps Environments." Hong Kong Journal of AI and Medicine 2.1 (2022): 37-77.

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.

J. M. Leach, "The Effect of Kanban on Software Development Performance: A Case Study," Journal of Software: Evolution and Process, vol. 28, no. 10, pp. 1-13, 2016.

Downloads

Published

06-12-2023

How to Cite

“Integrating AI into Kanban for Agile Mobile Product Development: Enhancing Workflow Efficiency, Real-Time Monitoring, and Task Prioritization ”. Journal of Science & Technology, vol. 4, no. 6, Dec. 2023, pp. 123-39, https://www.thesciencebrigade.com/jst/article/view/427.

Plaudit