Automated Risk Assessment and Collaborative Decision-Making AI Applications in Agile Project Management and Stakeholder Engagement

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.

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