Automated Risk Assessment and Collaborative Decision-Making AI Applications in Agile Project Management and Stakeholder Engagement
DOI:
https://doi.org/10.29284/v2jv8q59Keywords:
Artificial intelligence, Agile project management, Risk assessment, Collaborative decision-making.Abstract
Background: The growing sophistication of contemporary project settings in the United States has exceeded the boundaries of the conventional humanistic management paradigm. Machine learning, natural language processing (NLP), and predictive analytics devices define how artificial intelligence (AI) is turning Agile Project Management (APM) into an activity that is reactive and relies on intuition to a proactive, evidence-based model of intelligence.
Research Objective: The proposed systematic review of the literature will integrate both empirical data and practical models on the integration of AI into the agile lifecycle, with a specific focus on its effects on automated risk assessment, collaborative decision-making, and sustained stakeholder engagement in the United States.
Research Methods: A systematic search in the major academic and industry databases such as PubMed, Scopus, and Web of Science was carried out according to PRISMA (Preferred Reporting Items to Systematic Reviews and Meta-Analyses) guidelines. The review has examined recent AI-powered frameworks, including the integration of Jira, Azure DevOps, and TensorFlow, etc.
Conclusion: The introduction of AI is a strategic requirement that turns the role of the project manager into a tactical rather than a strategic organizer. It is estimated that 80 percent of the traditional project management activities will have been automated by 2030, and a fundamental shift to what is also known as Human-AI Teaming (HAIT) will be necessary. The key to successful implementation is the culture of experimentation, compliance with strong governance principles, including the NIST AI Risk Management Framework, and ethical transparency as the most effective way to align stakeholders in the long term and achieve a successful project.
References
1. Taboada IA, et al. Systematic bibliometric analysis of artificial intelligence in project management. Electronics. 2025;14(4):800.
2. Tominc et al. Scientometric and qualitative analysis of AI in project management: challenges and opportunities. F1000Research. 2024;14:1357.
3. Bogner L. Goldman Sachs just put AI on every employee’s desk while your company is still evaluating AI. Chief AI Officer [Internet]. 2024. Available from: https://chiefaiofficer.com/blog/goldman-sachs-just-put-ai-on-every-employees-desk-while-your-company-is-still-evaluating-ai/
4. Al-Arafat et al. AI-based decision support systems for risk reduction and resource distribution in agile software project management. Systems. 2025;13(3):208.
5. Zenhub. AI project management agile guide: scaling with intelligence. Zenhub [Internet]. 2025 Oct 24. Available from: https://www.zenhub.com/blog-posts/ai-project-management-agile-guide
6. Matplotlib and Power BI visualization frameworks for Agile. Systems. 2025;13(3):208.
7. Vijaya et al. AI in project management: exploring theoretical models for decision-making and risk management. ResearchGate [Internet]. 2024. Available from:
8. Information extraction and identification from clinical texts using NLP. JMIR Med Inform. 2023;11(1):e42477.
9. Natural language processing toolkits in the agile software development documentation process. Computers. 2022;11(12):179.
10. Haase J, Walker PB, Berardi O, Karwowski W. Get Real Get Better: a framework for developing agile program management in the U.S. Navy supported by the application of advanced data analytics and AI. Technologies. 2023;11(6):165.
11. Human-AI collaboration models and computationally informed mission planning. Technologies. 2023;11(6):165.
12. Preprints.org. Impact of AI on multi-criteria decision-making (MCDM), stakeholder relations, and human-AI collaboration models. Preprints [Internet]. 2025 Apr 21. Available from: https://www.preprints.org/manuscript/202504.1617
13. AI in stakeholder management: the Framework of Trust to identify, inform, engage, and validate. Agilemania [Internet]. 2024. Available from: https://agilemania.com/ai-in-stakeholder-management
14. Real-time dashboards and interactive analytics for distributed agile alignment. Zenhub Guide. 2025 Oct 24.
15. DigitalDefynd. Impact of AI & ML in the finance and fintech industry. DigitalDefynd IQ [Internet]. 2025. Available from: https://digitaldefynd.com/IQ/impact-of-ai-machine-learning-ml-in-fintech/
16. Microsoft. AI use cases for nonprofit leaders: realize value with AI. Microsoft Nonprofits [Internet]. 2024. Available from: https://www.microsoft.com/content/dam/microsoft/msc/documents/presentations/nonprofits/pdfs/AI-use-cases-for-nonprofit-leaders.pdf
17. Avancer. Ethical AI/ML model development: high-performance solutions for healthcare and life sciences. Avancer [Internet]. 2024. Available from: https://avancer.co/our-services/ethical-aiml-model-development-high-performance-solutions
18. Pellera. Top 5 AI adoption challenges for 2025: overcoming barriers to success. Pellera Blog [Internet]. 2024. Available from: https://pellera.com/blog/top-5-ai-adoption-challenges-for-2025-overcoming-barriers-to-success/
19. PEX Network. PEX Report 2025/26: global state of business transformation. AI, Data & Analytics Network [Internet]. 2025 Oct 02. Available from: https://www.aidataanalytics.network/data-science-ai/news-trends/data-quality-availability-top-list-of-ai-adoption-barriers
20. IBM. The 5 biggest AI adoption challenges for 2025: concerns about data accuracy or bias. IBM Think Insights [Internet]. 2024. Available from: https://www.ibm.com/think/insights/ai-adoption-challenges
21. Deloitte. Enterprise challenges for agentic AI, physical AI and sovereign AI. Deloitte Applied AI [Internet]. 2024. Available from: https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/blogs/pulse-check-series-latest-ai-developments/ai-adoption-challenges-ai-trends.html
22. The Decision Lab. What are organizational barriers to AI adoption? Skill gaps and change resistance. The Decision Lab Reference Guide [Internet]. 2024. Available from: https://thedecisionlab.com/reference-guide/management/organizational-barriers-to-ai-adoption
23. Microsoft. Enterprise AI maturity in five steps: our guide for IT leaders. Microsoft Inside Track [Internet]. 2024. Available from: https://www.microsoft.com/insidetrack/blog/enterprise-ai-maturity-in-five-steps-our-guide-for-it-leaders/
24. National Institute of Standards and Technology. Artificial intelligence risk management framework (AI RMF 1.0). NIST AI 100-1 [Internet]. 2023 Jan 26. Available from: https://doi.org/10.6028/NIST.AI.100-1
25. NIST. NIST AI RMF playbook: foundational structure for managing AI risks. National Institute of Standards and Technology [Internet]. 2023 Mar 30. Available from: https://www.nist.gov/itl/ai-risk-management-framework/ai-rmf-development
26. A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments.[Internet].2025.Available from:https://openurl.ebsco.com/EPDB%3Agcd%3A8%3A1235864/detailv2?sid=ebsco%3Aplink%3Acrawler&id=ebsco%3Adoi%3A10.3390%2Fsu17209234&crl=f&link_origin=none
27. Artificial Intelligence-Driven Business Analytics for IT Strategy: Advancing Decision-Making, Real-Time Insights, and Organizational Agility Through Intelligent Automation and Data Integration.[Internet].2025.
Available from:https://www.ceeol.com/search/article-detail?id=1361321
28. Nenni, M.E., De Felice, F., De Luca, C. et al. How artificial intelligence will transform project management in the age of digitization: a systematic literature review. Manag Rev Q 75, 1669–1716 (2025). https://doi.org/10.1007/s11301-024-00418-z
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

