Comparative evidence of how two Gulf Cooperation Council (GCC) states translate artificial intelligence (AI) ambitions into post-New Public Management (post-NPM) outcomes are scarce because most studies focus on Western democracies. To fill this gap, we examine constitutional, collective choice, and operational rules that shape 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 integrates a most similar/ most different systems design with multiple sources: 62 public documents issued between 2018 and 2025, embedded UAE cases (Smart Dubai and MBZUAI), and 39 interviews with officials conducted from Aug 2024 to May 2025. Dual coding and process tracing connect rule configurations to AI performance. Our cross-case analysis identifies four mutually reinforcing mechanisms behind divergent trajectories. In the UAE, concentrated authority, credible sanctions, pro-innovation narratives, and flexible reinvestment rules transform pilots into hundreds of operating services and significant recycled savings. Kuwait's dispersed veto points, exhortative sanctions, cautious discourse, and lapsed AI budgets, by contrast, confine initiatives to pilot mode de - spite equivalent fiscal resources. These 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 advance societal goals only when backed by enforceable safeguards. To curb ethics washing and test the transferability of these mechanisms beyond the GCC, future research should track rule diffusion over time, experiment with blended legitimacy-efficiency scorecards, and investigate how narrative framing shapes citizen consent for data sharing.

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.

Former President Donald Trump is embarking this week on a high-stakes tour of the Persian Gulf region, targeting business deals and strategic partnerships with three oil-rich nations: Saudi Arabia, the United Arab Emirates and Qatar. The trip marks Trump's first major foreign visit of his new term and comes as nuclear negotiations with Iran drag on and as war continues between Israel and the Palestinian terror organization, Hamas, in the Gaza Strip. While business is the official focus, the backdrop is anything but calm. White House press secretary Karoline Leavitt described the mission as part of Trump's broader vision that "extremism is defeated [through] commerce and cultural exchanges." Under President Joe Biden, U.S. relations with Gulf states cooled, particularly after Biden vowed to make Saudi Crown Prince Mohammed bin Salman a "pariah" over the 2018 killing of journalist Jamal Khashoggi.

Intelligent Task Offloading in V ANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency Tariq Qayyum, Asadullah Tariq, Muhammad Ali, Mohamed Adel Serhani, Zouheir Trabelsi, Maite L opez-S anchez College of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi, UAE Department of Computer Science, SCIT, Beaconhouse National University, Lahore Pakistan College of Computing and Informatics, University of Sharjah, Sharjah, UAE Department of Mathematics, University of Barcelona, Gran Via de les Corts Catalanes, Barcelona Abstract --V ehicular Ad-hoc Networks (V ANETs) are integral to intelligent transportation systems, enabling vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing. However, the highly dynamic nature of V ANETs introduces challenges, such as unpredictable network conditions, high latency, energy inefficiency, and task failure. Extensive simulations demonstrate that the proposed framework achieves significant reductions in latency and energy usage while improving task success rates and network throughput. By offering an efficient, and scalable solution, this framework sets the foundation for enhancing real-time applications in dynamic vehicular environments. Index T erms--V ANET, T ask offloading, vehicular communication, resource allocation, scalable I.

At an AI research lab on the edges of Abu Dhabi last year, an international team of 25 computer scientists were putting the finishing touches on a deep learning algorithm before sending it to be trained on 4,000 powerful computer chips. The AI system, which cost several million dollars to train, was funded by an arm of the Abu Dhabi government called the Advanced Technology Research Council (ATRC). Despite the government's substantial investment, ATRC director Faisal Al Bannai decided to release the finished model online for free. If it was as good as the team believed, the boost to the United Arab Emirates' reputation would be all the return the government needed on its investment, he reasoned. When the AI, named Falcon after the UAE's national bird, was publicly released last September, it became a sensation. By some measures it was the best open-source large language model (LLM) available in the world at that point, outperforming top offerings from Meta and Google.

OpenAI CEO Sam Altman is seeking to raise trillions of dollars from investors, including the United Arab Emirates government, to boost the world's capacity to produce advanced chips and power artificial intelligence, The Wall Street Journal has reported. Altman's "wildly ambitious tech initiative" could require raising as much as 7 trillion, the WSJ reported on Thursday, quoting people familiar with the matter. As part of his pitch to investors, Altman has proposed building dozens of chip foundries that would then be run by existing chip makers, such as Taiwan Semiconductor Manufacturing Company (TSMC), the Journal said. The plans aim to solve obstacles to OpenAI's growth, including a scarcity of chips that power AI models such as ChatGPT, according to the WSJ, which described the sums being sought as "outlandishly large by the standards of corporate fundraising". Altamn's plans have so far seen him hold meetings with senior UAE officials, TSMC executives, US Secretary of Commerce Gina Raimondo and SoftBank's chief executive Masayoshi Son, according to the report.

Building the low-carbon industries of the future means leveraging advanced and emerging technologies like AI, IoT, and robotics to improve efficiency, incentivizing energy efficiency among manufacturers, and promoting scalable decarbonization best practices. As one of the world's largest integrated energy companies, ADNOC, is faced with a generational challenge of minimizing emissions while maximizing energy outputs, says ADNOC Executive Director of Low Carbon Solutions and International Growth, Musabbeh Al Kaabi. Beyond implementing nature-based solutions such as mangrove planting, ADNOC is implementing and piloting new technology to permanently remove carbon through mineralization, says Al Kaabi. Startups and players outside the traditional energy sector are also emerging with new innovations employing AI, supercomputing, and big data analytics that can help accelerate the energy transition. By establishing a resilient science and technology ecosystem within the UAE and investing in clean energy projects and renewables worldwide, the nation looks to address climate change challenges regionally and globally, says Al Amiri. Looking forward, these investments and policies will create new green business models and services that can enable the UAE to achieve both carbon neutrality and strong economic growth through its pragmatic, resilient, and inclusive approach.

The novel coronavirus (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) after it was first discovered in Wuhan, China [1]. Over one year, the virus has infected more than 68 million people worldwide [2]. The virus can be fatal for elderly people or ones with chronic diseases [3]. Different countries across the globe have imposed several social practices and strategies to reduce the spread of the infection and to ensure the well-being of the residents. These practices and strategies include but are not limited to social distancing, restricted and authorized travels, remote work and education, reduced working staff in organizations, and frequent COVID-19 tests. These measures have been proved potential in reducing the disease spread and death in the previous pandemics [3], [4]. Several studies have focused on machine learning time series models to forecast the number of COVID-19 infections in different countries [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. This is to aid the government in designing and regulating efficient virus spread-mitigating strategies and to enable healthcare organizations for effective planning of health personnel and facilities resources. Based on the forecasted infections, the government can either make the confinement laws stricter or can ease them.

Stockholm, 17 October 2022 – Anoto Group AB (publ) ("Anoto") announced on 7 September 2022 that it is working with a government in the Middle East to conduct a Proof of Concept (PoC) for KAIT's AI Solution. Anoto announces today that such government is the government of United Arab Emirates (UAE). We have received a Letter of Intent (LOI) related to a possible purchase of KAIT's AI Solution from the Emirates School Establishment (ESE), which oversees public schools in the UAE. Before purchase, it is customary for schools to undergo a PoC pilot. The PoC for ESE will start in the beginning of November and finish in December of 2022. We have also secured PoCs with three private schools in UAE and one school in Jordan, making it a total of four schools in the Middle East region.

Sharjah: Artificial Intelligence experts and professionals from government and private sector entities who are integrating AI systems in their operations have shared insights and real-life examples of the transformative power of AI in driving the economic growth of businesses in various sectors during a forum organised by the Sharjah Entrepreneurship Center (Sheraa) recently at its headquarters. The Sheraa AI Forum was held in the presence of HE Major General Saif Al Zari Al Shamsi, Commander-in-Chief of Sharjah Police; H.E. Mohammed Bin Taliah, Chief of Government Services of the UAE Government; Najla Al Midfa, CEO of Sheraa, and a representative of the Minister of State for Artificial Intelligence office, Sharjah Police General Managers, Minister's Office of Artificial Intelligence representative and heads of companies and executives from the government and private sectors. The forum also brought together 150 Emirati youth, entrepreneurs and tech founders to shine light on best practices in the field and the opening of new investment opportunities in the public and private sectors with the ongoing adoption of advanced technologies. During the event, Sharjah Police and Sheraa startups shared their experiences in utilising AI in their products and solutions, noting the transformative power of AI in driving the economic growth of businesses in various sectors. Sheraa AI Forum, which is aligned with the UAE National Strategy for Artificial Intelligence 2031, which includes building work teams to enhance artificial intelligence and formulating joint strategic plans to increase the application of AI mechanisms in various sectors.