TLDR: This research introduces a new AI Adoption Index specifically for the Gulf Cooperation Council (GCC) public sector. Combining literature review, national AI strategies, and a survey of 203 government employees, the study found that robust infrastructure and clear policy mandates are the strongest drivers of successful AI implementation, while organizational readiness has a lesser impact in early adoption stages. The index provides a context-sensitive tool for benchmarking AI maturity and guiding strategic resource allocation in the GCC region.
Artificial intelligence (AI) is rapidly reshaping how governments worldwide deliver services, manage resources, and interact with citizens. This global trend is particularly evident in the Gulf Cooperation Council (GCC) region, where all six member states—Saudi Arabia, the United Arab Emirates (UAE), Qatar, Bahrain, Kuwait, and Oman—have launched or updated National AI Strategies (NASs) between 2018 and 2024. These strategies align AI development with ambitious national visions for economic diversification and global competitiveness, such as the UAE’s National Innovation Strategy and Saudi Arabia’s Vision 2030.
Despite this strong commitment to digital transformation, accurately measuring AI’s actual impact on governance and service delivery in a localized manner has been challenging. Traditional global indices, like those from the UN E-Government Survey or the World Bank’s GovTech Maturity Index, offer broad benchmarks but often overlook the unique drivers, barriers, and governance models specific to GCC countries. These tools might capture basic digital readiness but often miss the AI-specific complexities of algorithmic accountability or the top-down policy mandates prevalent in the GCC.
Developing a Tailored AI Adoption Index
To address this gap, a recent study by Mohammad Rashed Albous, Anwaar AlKandari, and Abdel Latef Anouze developed and validated a novel AI Adoption Index specifically tailored for the GCC public sector. The research employed a comprehensive approach, combining a theory-driven foundation with a data-driven methodology. The theory aspect involved an in-depth analysis of existing literature and the six National AI Strategies of the GCC countries. This was complemented by a data-driven approach, utilizing a survey of 203 mid- and senior-level government employees and advanced statistical techniques, including K-Means clustering, Principal Component Analysis (PCA), and Partial Least Squares Structural Equation Modeling (PLS-SEM).
The study identified three core dimensions influencing AI adoption: Infrastructure & Resources, Organizational Readiness, and Policy & Regulatory Environment. These dimensions were then empirically refined and validated through the survey data.
Key Findings: Infrastructure and Policy Lead the Way
The findings of the study revealed that robust infrastructure and clear policy mandates exert the strongest influence on successful AI implementations in the GCC public sector. These factors significantly overshadow organizational readiness in the early stages of AI adoption. The combined model developed by the researchers explains 70% of the variance in AI outcomes, suggesting that resource-rich environments and top-down policy directives are powerful drivers of rapid, though potentially uneven, technology uptake.
Specifically, the PLS-SEM analysis showed a strong, positive, and highly significant influence of Infrastructure & Resources on AI Outcomes. The Policy & Regulatory Environment also demonstrated a moderate but significant effect. Surprisingly, Organizational Readiness showed a small and statistically insignificant impact on AI Outcomes. This suggests that once strong infrastructure and clear policies are established, the broader aspects of organizational readiness, such as workforce training or leadership engagement, might be less critical in the initial phases of AI deployment or could be implicitly covered by the top-down approach.
The GCC AI Adoption Index
Based on these insights, the researchers proposed a combined AI Adoption Index. This index integrates each construct’s normalized path coefficient as a weight, providing a practical tool for policymakers. The formula, rescaled to a 0–100 scale, is: AI Adoption Index = (0.75 Infrastructure + 0.02 Organizational Readiness + 0.23 Policy & Regulatory Environment) × 100. This weighting clearly indicates the dominant roles of Infrastructure & Resources (approximately 75%) and Policy & Regulatory Environment (approximately 23%) in driving AI outcomes, with Organizational Readiness contributing a minimal 2%.
A higher index score suggests an environment highly conducive to AI adoption, while lower scores indicate more limited readiness. The study also identified two main adopter groups within the GCC: a “High AI Adoption” cluster, primarily comprising respondents from the UAE and Saudi Arabia, and a “Moderate Adoption” cluster, encompassing other GCC states.
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Implications for Policymakers
This research offers actionable guidance for policymakers in the GCC. It underscores that establishing robust digital infrastructure and ensuring clear regulatory mandates are crucial first steps for accelerating AI implementation. While organizational readiness may seem secondary in early stages, it is expected to become more critical as AI programs mature and require deeper cultural acceptance, continuous skill development, and sustained leadership commitment.
The index serves as both a diagnostic and benchmarking tool, helping governments identify specific adoption gaps and strategically allocate resources for capacity-building efforts. By providing a holistic yet context-sensitive measure, this study supports sustained AI-driven transformation in the GCC region and beyond. For more details, you can read the full research paper here.
