Driving digital transformation in the oil and gas sector: unlocking AI’s potential in the UAE

Purpose This study aims to examine the implementation of artificial intelligence (AI) in the UAE oil and gas sector, focusing on operational, project management and executive-level challenges. It addresses gaps in existing research, which predominantly originates from North America and Europe, and provides a regional perspective on the practical challenges of AI adoption. The study aims to guide future AI implementation projects in the UAE by offering a structured framework. Design/methodology/approach A three-phase qualitative study was conducted using an interpretivist methodology. Semi-structured interviews were carried out with operational engineers, project managers and senior executives in the UAE oil and gas sector. Data saturation was achieved, and thematic analysis was used to interpret the findings. Findings The study identifies the necessity of adopting a lessons-learnt approach, reflecting the emerging stage of AI in the sector. It emphasises the importance of structured change management methodologies to address gaps in digital transformation research. The findings also highlight the critical need for collaborative efforts to bridge skill gaps and expertise, despite challenges posed by the sector’s siloed organisational culture. Senior management support is identified as essential for fostering collaboration. A developed model was applied to an existing AI project and presented as a preliminary evaluation. Research limitations/implications This study contributes to the literature on AI implementation by providing a region-specific perspective for the UAE oil and gas sector. It identifies key challenges and offers actionable recommendations for practitioners and researchers, highlighting the importance of structured change management and collaboration in overcoming barriers to AI adoption. Originality/value This study presents a novel and impactful framework for implementing AI in the UAE oil and gas sector, effectively bridging both regional and practical research gaps. It highlights the critical importance of equipping engineers with the requisite skills to facilitate successful AI adoption and delivers a robust, practice-oriented model that can guide future initiatives and strategic decision-making within the industry.