Automation in Hotel Front Office: Evaluating the Effects of AI and Self Check-in Systems on Operational Efficiency

Automation in hotel front offices, driven by artificial intelligence (AI) and self-check-in systems, is emerging as a transformative solution to enhance operational efficiency and elevate guest experiences. This study employs a mixed-methods approach to evaluate the effects of these technologies by integrating quantitative performance metrics and qualitative insights. Data were collected from multiple hotel properties using standardized surveys, management records, semi-structured interviews, and direct observations to capture key indicators such as check-in duration, error rates, labor cost savings, and guest atisfaction.Quantitative analysis reveals that the implementation of AI and self-check-in kiosks significantly reduces processing times and minimizes human errors in routine tasks such as guest registration, billing, and reservations. AI-driven analytics further empower hotel managers by providing real-time insights that support dynamic resource allocation and strategic decision-making, ultimately leading to cost reductions and enhanced operational reliability. Qualitative findings highlight that, while automation improves efficiency, its success hinges on a balanced integration with human oversight, ensuring that personalized service is maintained alongside technological advancements. The study also identifies critical challenges, including high initial capital investments, the necessity for comprehensive staff training, and increased cybersecurity and data privacy concerns. These suggest that a phased implementation strategy, combined with ongoing employee development and robust security measures, is essential for maximizing the benefits of automation while mitigating associated risks.