The Future of AI in the GCC Post-NPM Landscape: A Comparative Analysis of Kuwait and the UAE

Comparative evidence on how Gulf Cooperation Council (GCC) states turn artificial intelligence (AI) ambitions into post--New Public Management (post-NPM) outcomes is scarce because most studies examine Western democracies. We analyze constitutional, collective-choice, and operational rules shaping AI uptake in two contrasting GCC members, the United Arab Emirates (UAE) and Kuwait, and whether they foster citizen centricity, collaborative governance, and public value creation. Anchored in Ostrom's Institutional Analysis and Development framework, the study combines a most similar/most different systems design with multiple sources: 62 public documents from 2018--2025, embedded UAE cases (Smart Dubai and MBZUAI), and 39 interviews with officials conducted Aug 2024--May 2025. Dual coding and process tracing connect rule configurations to AI performance. Cross-case analysis identifies four reinforcing mechanisms behind divergent trajectories. In the UAE, concentrated authority, credible sanctions, pro-innovation narratives, and flexible reinvestment rules scale pilots into hundreds of services and sizable recycled savings. In Kuwait, dispersed veto points, exhortative sanctions, cautious discourse, and lapsed AI budgets confine initiatives to pilot mode despite equivalent fiscal resources. The findings refine institutional theory by showing that vertical rule coherence, not wealth, determines AI's public-value yield, and temper post-NPM optimism by revealing that efficiency metrics serve societal goals only when backed by enforceable safeguards. To curb ethics washing and test transferability beyond the GCC, future work should track rule diffusion over time, develop blended legitimacy--efficiency scorecards, and examine how narrative framing shapes citizen consent for data sharing.

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

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.