AI-Augmented Project Management
Enhancing Decision-Making Through Predictive Analytics and Automation
Keywords:
AI, predictive analytics, automation, project managementAbstract
In recent years, artificial intelligence (AI) has emerged as a transformative force in various sectors, including project management. This paper explores the integration of AI-powered predictive analytics and automation in enhancing decision-making processes within project management. The study emphasizes optimizing resource allocation, task scheduling, and risk mitigation through AI technologies. By leveraging machine learning algorithms and data analytics, project managers can make more informed decisions that lead to improved project outcomes. The paper also examines the challenges associated with implementing AI in project management and provides insights into best practices for successful integration. The findings highlight the importance of adopting AI tools to foster a proactive approach to project management, ensuring that organizations remain competitive in an increasingly complex business environment.
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References
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.
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.
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.
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.
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.
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.
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.
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.
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.
Disterer, G. (2019). Project management in the digital age: AI, machine learning, and automation. International Journal of Managing Projects in Business, 12(1), 23-38.
O'Sullivan, D., & Ainsworth, S. (2021). The role of data analytics in project management decision-making. International Journal of Information Systems and Project Management, 9(2), 65-79.
Alhawari, S., & Al-Sharif, M. (2021). Leveraging AI in project risk management: An empirical analysis. Computers in Industry, 131, 103447.
Muda, A., & Krichak, R. (2021). Automating project reporting: A case study. Journal of Business Research, 124, 207-215.
Kwan, K., & Wong, L. (2021). Decision-making in project management: The role of AI and automation. Management Decision, 59(6), 1245-1260.
Tharp, B. (2021). A guide to predictive analytics for project managers. Project Management Review, 53(3), 12-17.
Smith, D., & Lewis, M. (2021). Automation and decision-making: The impact of AI in project environments. Journal of Project Management, 36(5), 481-490.
Lee, J., & Chang, H. (2020). Predictive analytics in project management: A literature review. Project Management Journal, 51(3), 155-165.
Ahmed, S., & Kwan, M. (2020). The effects of AI on project management efficiency: An empirical study. International Journal of Production Economics, 227, 107703.
Boehm, B. W. (2020). Software engineering economics: Project management tools and techniques. Software Engineering Journal, 35(4), 320-330.
Al-Qatawneh, L., & Naser, M. (2021). The challenges of AI adoption in project management. Business Process Management Journal, 27(6), 1793-1806.
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