Artificial Intelligence Tools Adoption and Operational Efficiency In 5-Star Hotels in Westlands Constituency, Nairobi County, Kenya

Abstract

Artificial intelligence (AI) is increasingly transforming hotel operations in Kenya, particularly in inventory management, demand forecasting, and personalized guest services. Despite its potential, widespread adoption faces challenges including infrastructure limitations, workforce adaptability, cultural perceptions, and financial constraints. The purpose of the study was therefore to investigate the effect of AI adoption on operational efficiency in 5-star hotels within Westlands Constituency, Nairobi County Kenya. The specific objectives were to; examine the effect of AI-driven process automation on operational efficiency in 5-star hotels in Westlands Constituency, Nairobi County, Kenya, assess how AI-enabled decision support systems adoption affect operational efficiency in 5-star hotels in Westlands Constituency, Nairobi County, Kenya, evaluate the effect of AI-based customer interaction technologies on operational efficiency in 5-star hotels in Westlands Constituency, Nairobi County, Kenya. Specifically, it examined how automation effect operational efficiency; how decision support systems enhance service quality, speed, and accuracy; and how customer interaction tools affect guest satisfaction and service standards. This study was guided by 4 theories namely, Technology Acceptance Model (TAM), Resource-Based View (RBV) Theory, Service Automation and Customer Experience Theory and Contingency Theory. A census approach was employed, targeting 96 participants comprising hotel managers and departmental heads from eight 5-star hotels. The study relied purely on primary data which was collected using structured questionnaires and key informant interviews, with pre-testing conducted to ensure instrument validity. Descriptive statistics (mean, standard deviation, frequency, percentage) and inferential statistics (correlation and regression analysis) were used to analyze the data. Results were presented using tables to provide a comprehensive understanding of AI adoption in Kenya’s hospitality sector. The findings revealed that AI-driven process automation explained 41.4% of the variation in operational efficiency in hotels (R² = 0.414). The results indicated that the model was significant in explaining the relationship between AI-driven process automation and operational efficiency (p = 0.000). Additionally, the regression analysis showed that AI-driven process automation had a significant positive effect on operational efficiency (β = 0.412, p-value = 0.000). For AI-enabled decision support systems, the adoption explained 47.3% of the variation in operational efficiency (R² = 0.473), with a statistically significant relationship (p-value = 0.000). Regression results showed that AI-enabled decision support systems positively impacted operational efficiency (β = 0.342, p = 0.000). Thematic analysis revealed that AI adoption in hotels has enhanced operational efficiency and reduced costs through process automation, predictive maintenance, and decision-support systems. However, while AI-based customer interaction technologies showed a positive relationship with customer satisfaction and service quality improvement (β = 0.112), this effect was statistically insignificant (p = 0.160; t = 1.419 < 1.96). This indicates that although AI has potential to improve guest experience, its influence on customer satisfaction and service quality is not yet significant, suggesting that other factors may have a stronger impact in shaping guest perceptions and service outcomes in hotels. challenges like high costs, staff resistance, and employee technological capabilities were identified as possible challenges to AI Adoption.