The objective of this study is to assess challenges inhibiting artificial intelligence (AI) implementation in transportation and to develop a framework for decision making in the UAE. A mixed-research method approach was employed, combining qualitative interviews with industry experts and quantitative survey data from 393 industry managers in the UAE. The partial least square structural equation modelling (PLS-SEM) approach was employed in the analysis of the quantitative data gathered to further examine the mediating influence of the organisation’s ability to implement AI. Specifically, SmartPLS version 4 software was used for the analysis of data gathered. The findings reveal that high implementation costs, lack of infrastructure, lack of quality data and funding/resources are the most significant barriers to AI implementation in transportation in the UAE. Based on the findings, a conceptual framework is proposed, offering recommendations for industry practitioners and policymakers to facilitate the implementation of AI-enabled systems in transportation operations. The originality and value lie in the contribution of the study to the gap in transportation literature by providing empirical data relating to challenges and issues limiting efficient and successful AI implementation in the transport operations in the UAE.
Suwaidi et al. (Tue,) studied this question.