TLDR: This study examines how Gulf Cooperation Council (GCC) nations are preparing their workforces for an AI-driven future, using Socio-Technical Systems (STS) theory. It finds that while GCC countries are investing heavily in AI infrastructure, the development of social subsystems like skills, governance, and incentives is uneven. The research highlights a shift from technology-focused strategies to more balanced socio-technical approaches, but notes persistent gaps in regulatory coherence and the emergence of a dual-track talent system (research elites vs. trained practitioners). The study concludes that regulatory harmonization is more crucial for long-term success than fiscal capacity and proposes policy actions to create a more integrated and inclusive AI workforce.
The rapid rise of artificial intelligence (AI) across the Gulf Cooperation Council (GCC) nations—comprising Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar, Kuwait, Bahrain, and Oman—presents a critical question: are the significant investments in AI infrastructure being matched by equally robust developments in skills, incentives, and governance for the workforce?
A recent study, “Artificial intelligence and the Gulf Cooperation Council workforce: adapting to the future of work,” delves into this very challenge, using Socio-Technical Systems (STS) theory as its foundation. This theory suggests that lasting performance gains only emerge when both the technical aspects (like algorithms and data infrastructure) and the social aspects (such as skills, incentives, and governance) are optimized together. The GCC, with its oil-funded infrastructure and unique labor markets, serves as a fascinating case study to examine this balance.
Understanding the Approach
The research employed a mixed-methods approach to audit workforce preparedness across the GCC. It involved four key phases:
- Analyzing six national AI strategies (NASs) to understand stated goals and mechanisms.
- Mapping 47 publicly disclosed AI initiatives from January 2017 to April 2025 to gauge implementation.
- Conducting comparative case studies of two prominent AI institutions: the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in the UAE and the Saudi Data & Artificial Intelligence Authority (SDAIA) Academy in KSA.
- Developing a scenario matrix to explore future possibilities based on oil revenues and regulatory coherence.
Key Findings: A Region in Transition
The study reveals that the GCC is gradually moving from a “technology-push” approach to one that emphasizes joint optimization of both technical and social elements. An overall “STS-alignment index” of 72% across initiatives indicates tangible progress, though with variations among countries, ranging from KSA’s 90% to Oman’s 57%.
Policy Evolution
Early national AI strategies, such as those from the UAE (2018), Qatar (2019), and KSA (2020), primarily focused on building hardware like data centers and supercomputing capabilities. However, later strategies from Oman (2023), Bahrain (2024), and Kuwait (2024) show a shift towards social complements, including ethics guidelines, SME upskilling, and public-sector job redesign. This evolution suggests a growing understanding that technology alone is not enough; human capital and governance must keep pace.
The “Skill Genome”
An analysis of policy documents highlighted three dominant social priorities: Skills Development, Job Creation, and Data Governance. The UAE and KSA lead in explicit commitments to skills development, while Kuwait shows a stronger focus on job creation, aiming to transition citizens from public to private sectors. In terms of data governance, while the UAE and KSA have robust laws in place, other nations are still in the process of codifying their privacy frameworks, indicating a “soft-law” approach that is still evolving.
Technical Capabilities
The technical priorities across GCC strategies emphasize High-Performance Computing (HPC), cybersecurity, and Arabic-oriented Natural Language Processing (NLP) as foundational. Machine learning, large language models (LLMs), and data analysis are also important, suggesting a move towards mainstream AI adoption. Less emphasis is currently placed on areas like explainable AI, robotics, and deep learning, which are seen as longer-term priorities.
Governance and Regulation
While there’s a surge in AI-related governance documents, many are non-binding guidelines rather than formal legislation. Only the UAE and KSA have comprehensive data protection laws similar to GDPR, with enforceable fines and breach notification requirements. Other GCC states rely on a patchwork of sector-specific rules, leading to potential gaps in accountability and coordination, especially for cross-border AI supply chains. The UAE’s recent move in April 2025 to establish a comprehensive AI legislative framework is a significant step in this direction.
Workforce Development
The study identified a strong focus on workforce formation, with over half of the initiatives dedicated to skills pipelines, specialist degrees, and vocational boot camps. This includes the launch of indigenous R&D capacities like Noor (KSA) and Jais (UAE) LLMs, which are creating new organizational units for ethics review and Arabic-language curation.
Two-Track Talent System
The comparative case studies of MBZUAI and SDAIA Academy highlight a developing “two-track” talent system. MBZUAI focuses on research-driven depth, producing elite graduates and frontier outputs like the Jais LLM. SDAIA Academy, on the other hand, emphasizes vocational breadth, offering short-cycle courses to rapidly train specialists for immediate labor-market demands, particularly for mega-projects like NEOM. Without bridging mechanisms like credit transfers or shared resources, this could lead to a bifurcated labor market where advanced R&D and routine implementation evolve in isolation.
Future Scenarios
The scenario analysis suggests that regulatory coherence might be more critical for systemic resilience than abundant oil revenues. Fragmented regulations can hinder progress even with high capital, while harmonized standards can sustain socio-technical loops even under fiscal austerity. This underscores the importance of a unified regulatory environment for AI adoption and talent mobility.
Also Read:
- Institutional Rules, Not Wealth, Drive AI Adoption in GCC Public Sectors
- Assessing AI Readiness: A New Approach for the Modern Workforce
Moving Forward: A Societal Marathon
The research concludes that the GCC’s AI journey is shifting from a series of isolated technology sprints to a more demanding “socio-technical marathon.” To ensure resilient and inclusive labor-market outcomes, the region needs to focus less on just acquiring more GPUs and more on institutional craftsmanship that fuses technical ambition with robust social architecture.
The study proposes five key policy actions:
- Integrating AI capabilities into cultural heritage and public service curricula, leveraging Arabic NLP for heritage documentation and data analytics for service optimization.
- Curtailing the environmental footprint of AI infrastructure through green procurement standards and linking public subsidies to efficiency metrics.
- Instituting a GCC-wide “AI Skills Passport” for mutual recognition of certificates, reducing labor-mobility frictions and fostering an integrated regional labor market.
- Bridging the two-track talent system through rotational fellowships, shared HPC access, and micro-credential credit transfers to create a seamless talent pipeline.
- Catalyzing bottom-up experimentation with targeted micro-grants for SMEs, startups, and civil society organizations in domains like logistics, manufacturing, and cultural preservation.
These measures aim to transform the GCC’s AI agenda into one that safeguards cultural heritage, advances sustainability, and secures inclusive prosperity alongside productivity gains. For more detailed insights, you can refer to the full research paper available here.
