Abstract            Volume:13  Issue-11  Year-2025         Original Research Articles

Online ISSN : 2347 - 3215 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcret@gmail.com

Event management is increasingly complex, technology-driven, and data-rich. Integrating Artificial Intelligence (AI) into Failure Mode and Effects Analysis (FMEA) — hereafter AI-FMEA — can transform traditional, manual risk assessments into predictive, data-driven processes that detect hidden failure modes, prioritize interventions, and automate detection and mitigation workflows. This study proposes an AI-FMEA framework for event management and evaluates its impact through a mixed-methods pilot involving event professionals (n = 140) and three live events where an AI-FMEA prototype was deployed. Quantitative analysis showed that AI-FMEA competency (advisory + technical) was strongly associated with improved event reliability (R² =.58, p <.001). Qualitative feedback highlighted improvements in early failure detection (computer vision and anomaly detection), communication through NLP-driven alerts, and more effective contingency planning via digital twin simulations. The findings suggest that AI-FMEA enhances predictive risk control, reduces Risk Priority Numbers (RPNs) through targeted automation, and improves stakeholder satisfaction — but success depends on human–AI collaboration, data quality, and organizational readiness. The study concludes with practical guidelines for integrating AI into FMEA workflows and recommendations for training event teams in AI-enhanced risk management.

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