Evaluating Multimodal Agentic AI in Reducing Cognitive Load for High-Frequency Travelers

High-frequency travelers face significant cognitive overload due to the fragmentation of digital travel tools, requiring the use of 5-10 separate applications for planning, booking, expense management, and documentation. This research evaluates the effectiveness of multimodal agentic AI systems in reducing cognitive load through unified travel operations. We propose an architecture integrating text, voice, image, and video interfaces with an offline-first Progressive Web Application framework, achieving 95% feature availability without internet connectivity. The system employs Large Language Models fine-tuned for travel contexts, processing multimodal inputs through preprocessing, intent detection, and context-aware task routing. Evaluation with 150 high-frequency travelers (business travelers and digital nomads) demonstrates a 40% reduction in task completion time, 85% improvement in accessibility task completion rates, and 90% user satisfaction with AI interactions. The findings indicate that multimodal agentic AI significantly reduces cognitive load while improving accessibility and operational efficiency for travelers managing complex itineraries across diverse connectivity environments.