Business Model Transformation in Oil and Gas Industry in UAE: the Influence of AI

Alsulaity, Iman Hamad (2024) Business Model Transformation in Oil and Gas Industry in UAE: the Influence of AI. PhD thesis, University of Gloucestershire. doi:10.46289/SUAN4610

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Text (Final thesis) 15100 Alsulaity, Iman Hamad (2024) Business model transformation in oil and gas industry in UAE - the influence of AI.pdf - Accepted Version Available under License All Rights Reserved. Download (4MB) | Preview

Abstract

This study has investigated the experiences of implementing AI in the UAE oil and gas sector. Academic studies have ranged from those conducted in North America and Europe, which have provided a rich source of information, but have neglected the experiences of the challenges associated with implementing AI from an operational, project management and executive level. There has also been an acknowledgement that there have been only a few studies conducted in the region, and to date, there has been limited research focused on informing future AI implementation projects. Initially informed by the researcher’s professional experience, then drawing on existing academic knowledge, the study involved conducting a qualitative study over three-phases. The main study used an interpretivist methodology in the form of semi-structured interviews, which investigated initially the experiences and perceptions of eleven operational engineers, then four project managers and finally three executive or senior managers working in the UAE oil and gas sector. The findings of the study were gathered until saturation was reached and then interpreted using a thematic approach. The study’s first key finding is the importance associated with using a lesson learned methodology. As this technology is new and still in its infancy, this methodology of learning from the past is essential. Linked to this, this study found that there was a specific need for a recognised and systematic change management methodology to be adopted and followed, which addresses the recognised gap that this theme had been neglected or omitted from previous studies as to digital transformational AI solutions in the oil and gas sector. The second finding is associated with the need to seek collaborative involvement to address potential skill gaps and expertise. Although this can be challenging as the industry is known for its silo culture and not sharing business data, there is a need for closer collaboration but also needs senior management support. Finally, the model has been applied through using an existing AI project, then presented in the form of a preliminary evaluation. In conclusion, this thesis provides a deep and rich conceptual insight, knowledge and understanding on how to implement an AI solution in the UAE oil and gas sector. The first contribution of the study includes the development of a new framework to represent the various stages of implementing AI in the oil and gas sector. The second contribution relates to the need for greater collaboration and for the existing engineers to possess the necessary skills and talents. Like all studies there are limitations. With only eighteen participants involved, eleven operational staff, four project managers and three executive or senior managers, the study cannot provide a generalised outcome, but instead has provided a rich insight from those working for the main provider in the UAE oil and gas sector. Secondly, this study has accessed participants who are often unavailable or inaccessible to provide a critical insight which could not be effectively achieved if a quantitative project were adopted.

Item Type:	Thesis (PhD)
Uncontrolled Keywords:	AI; AI solutions; Oil and gas sector
Subjects:	H Social Sciences > HF Commerce > HF5001 Business
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Anna Kerr
Date Deposited:	05 Jun 2025 09:32
Last Modified:	05 Jun 2025 09:32
URI:	https://eprints.glos.ac.uk/id/eprint/15100

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