Artificial Intelligence and Project Governance

Enhancing Compliance and Accountability Through Automated Monitoring

Authors

  • Sarah Thompson Assistant Professor, Department of Management, University of Melbourne, Melbourne, Australia Author

Keywords:

Artificial Intelligence, project governance, compliance, accountability

Abstract

In the contemporary landscape of project management, the integration of Artificial Intelligence (AI) has emerged as a pivotal force in enhancing governance frameworks. This paper investigates how AI technologies can automate the monitoring of compliance and accountability in projects, thereby ensuring adherence to regulatory and organizational standards. By utilizing machine learning algorithms, natural language processing, and data analytics, organizations can streamline oversight processes, mitigate risks, and promote transparency. The paper discusses the implications of automated monitoring systems for project governance, highlighting their potential to improve decision-making, foster accountability, and enhance stakeholder trust. Additionally, it examines challenges associated with AI implementation in governance practices and offers recommendations for successful integration. Ultimately, the study advocates for the strategic use of AI as a means to bolster project governance, ensuring projects meet established compliance standards while achieving their intended objectives.

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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.

M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, no. 6245, pp. 255-260, 2015.

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Published

02-11-2023

How to Cite

[1]
Sarah Thompson, “Artificial Intelligence and Project Governance: Enhancing Compliance and Accountability Through Automated Monitoring”, Australian Journal of Machine Learning Research & Applications, vol. 3, no. 2, pp. 518–523, Nov. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/159

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