AI-Driven Recruitment in Hospitality: Balancing Efficiency and Human Touch
Burcin Turkkan Zencirli & Cynthia Mejia
What the paper says
This case study examines the implementation of AI-driven recruitment systems in the hospitality industry through Global Hotel Group’s experience, highlighting the tension between operational efficiency and maintaining human touch in hiring processes. Operating over 200 hotels across 35 countries, Global Hotel Group invested $1.2 million in AI recruitment technology to address challenges in talent acquisition and retention. While the system reduced hiring time from 30 to 3 days and generated $500,000 in annual savings, it also revealed significant challenges in maintaining the company’s cultural values and commitment to diversity. The implementation resulted in mixed outcomes: increased female hiring rates (35%–42%) but decreased representation from non-traditional educational backgrounds (40%–34%) and lower retention rates (68%–62%). Through the lens of Perceived Organizational Justice and Technology-Organization-Environment frameworks, the study explores how AI recruitment systems impact candidate experience, organizational culture, and operational effectiveness. The case highlights specific incidents, including Ahmed Khalil’s experience and a whistleblower event that exposed system biases, demonstrating the complexities of balancing technological efficiency with human-centered hiring practices. As Global Hotel Group prepares for its flagship Dubai hotel opening amid growing competition from Regal Hotels’ successful hybrid recruitment model, the company faces critical decisions about its recruitment strategy. The study concludes by presenting three potential paths forward: maintaining the full AI system with improvements, adopting a hybrid model, or recalibrating the entire approach with enhanced bias mitigation strategies. This analysis provides valuable insights for hospitality organizations navigating the integration of AI in recruitment while maintaining their commitment to personalized service and cultural values.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.