Evaluating the Integration of Deep Learning-Based Artificial Intelligence in the UAE Service Industry, with a Focus on Emirates Airlines' Technological Advancements in Aviation

Evaluating the Integration of Deep Learning-Based Artificial Intelligence in the UAE Service Industry, with a Focus on Emirates Airlines' Technological Advancements in Aviation Dr Pouria Liravi Senior Lecturer Operations and Supply chain University of Derby Mohammad Ali Alhosani Doctorate student Innovation and Technology Adoption University of Derby m.alhosani1@unimail.derby.ac.uk Abstract This study examines UAE service industry AI use. AI has rapidly transformed several industries, necessitating a thorough understanding of its usage. The research tries to identify UAE service industry success elements, obstacles, and ethical issues. This goal can be achieved using four research objectives. 1) Evaluate UAE service industry deep learning usage. 2) To examine deep learning-based AI adoption success factors. 3) To examine service industry organisations' deep learning-based AI adoption problems 4) To advise UAE service industry decision-makers on deep learning-based AI. A comprehensive literature review gleaned insights from current investigations. The research will use qualitative and quantitative methodologies to reach its purpose. Key stakeholders with deep learning-based AI adoption experience in the service industry will be interviewed (Miklosik et al., 2019). A larger sample of UAE service industry organisations will be surveyed for the research. The RBV framework will show how organisations' unique resources and competencies affect AI adoption and implementation (Collins, 2021). Case studies from organisations that successfully applied deep learning-based AI in service operations will be included. The model should help explain UAE service business AI adoption (Pandya & Al Janahi, 2021). It demonstrates that AI adoption requires organisational culture, leadership support, technical skill, data quality, and collaborative human-AI engagement. Expect technological implementation, change management, workforce adaptability, data governance, and legal compliance challenges (Stanfill & Marc, 2019). This research is crucial to the goal. The important success elements and difficulties will assist decision-makers, practitioners, and policymakers implement AI. This study can successfully incorporate AI by closing literature gaps. Keywords AI Adoption, Service Industry, United Arab Emirates. Biographies Collins, C. J. (2021). Expanding the resource based view model of strategic human resource management. The International Journal of Human Resource Management, 32(2), 331-358. Miklosik, A., Kuchta, M., Evans, N., & Zak, S. (2019). Towards the adoption of machine learning-based analytical tools in digital marketing. Ieee Access, 7, 85705-85718. Pandya, B., & Al Janahi, M. M. (2021). The intervention of artificial intelligence in the recruitment function in UAE's hospitality industry. Transnational Marketing Journal, 9(1), 89-105. Stanfill, M. H., & Marc, D. T. (2019). Health information management: implications of artificial intelligence on healthcare data and information management. Yearbook of medical informatics, 28(01), 056-064.