Artificial intelligence and the Gulf Cooperation Council workforce: adapting to the future of work

Mohammad Rashed Albous (), Melodena Stephens and Odeh Rashed Al-Jayyousi
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Mohammad Rashed Albous: Abdullah Al Salem University
Melodena Stephens: Mohammed Bin Rashid School of Government
Odeh Rashed Al-Jayyousi: Arabian Gulf University

Humanities and Social Sciences Communications, 2025, vol. 12, issue 1, 1-17

Abstract: Abstract The rapid expansion of artificial intelligence (AI) in the Gulf Cooperation Council (GCC) raises a central question: are investments in compute infrastructure matched by an equally robust build-out of skills, incentives, and governance? Grounded in socio-technical systems (STS) theory, this mixed-methods study audits workforce preparedness across Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar, Kuwait, Bahrain, and Oman. We combine term frequency–inverse document frequency (TF–IDF) analysis of six national AI strategies (NASs), an inventory of 47 publicly disclosed AI initiatives (January 2017–April 2025), paired case studies, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and the Saudi Data & Artificial Intelligence Authority (SDAIA) Academy, and a scenario matrix linking oil-revenue slack (technical capacity) to regulatory coherence (social alignment). Across the corpus, 34/47 initiatives (0.72; 95% Wilson CI 0.58–0.83) exhibit joint social–technical design; country-level indices span 0.57–0.90 (small n; intervals overlap). Scenario results suggest that, under our modeled conditions, regulatory convergence plausibly binds outcomes more than fiscal capacity: fragmented rules can offset high oil revenues, while harmonized standards help preserve progress under austerity. We also identify an emerging two-track talent system, research elites versus rapidly trained practitioners, that risks labor-market bifurcation without bridging mechanisms. By extending STS inquiry to oil-rich, state-led economies, the study refines theory and sets a research agenda focused on longitudinal coupling metrics, ethnographies of coordination, and outcome-based performance indicators.

Date: 2025
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DOI: 10.1057/s41599-025-05984-5

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