Human-AI Collaboration in High-Volume Project Risk Management: A Cross-Domain Framework Transfer Study

As artificial intelligence tools generate increasingly complex risk scenarios, project managers face a critical challenge: hundreds of potential risks per project that far exceed human capacity to evaluate manually. While other high-risk domains have developed frameworks for human-AI collaboration in high-volume contexts, project management lacks systematic approaches to this emerging problem. Without proper frameworks, AI adoption risks creating an "intelligence paradox"—where AI's analytical power generates so many scenarios that managers become more overwhelmed than before. This study presents a cross-domain framework transfer analysis through a systematic review of 344 papers. We identify four proven human-AI collaboration frameworks from cybersecurity, robotics, security screening, and financial systems that successfully manage high-volume risk data. Our analysis reveals that while 17 studies document information overload in project management, none provide systematic solutions adapted from other domains. To address this gap, we develop a comprehensive framework consisting of: (a) a transfer matrix that maps each source framework to specific project management challenges, (b) an integrated system architecture preventing AI-induced overload, and (c) three critical principles—context preservation, threshold calibration, and integration requirements—that guide successful implementation. The framework provides practitioners with concrete pathways for adaptation: from cybersecurity's HAT framework for risk prioritization to weak-signal detection for early warning systems. This study offers the first systematic approach to transferring proven human-AI collaboration models to project management, providing both immediate solutions and a methodology that other fields facing AI-integration challenges could adapt.