Khalid Alshehhi

This study investigates the critical success factors (CSFs) influencing the procurement of artificial intelligence (AI) systems within the United Arab Emirates (UAE) public sector. While AI holds immense potential to enhance public service delivery, its successful integration hinges on critical factors. This research utilizes Interpretive Structural Modeling (ISM) to analyze the CSFs impacting AI procurement within the UAE public sector. Through ISM, a structural model is developed to highlight the interrelationships between these CSFs and their influence on the procurement process, outlining the key elements for successful AI procurement within the UAE public sector. Based on the literature review and expert validation from the UAE public sector, ten CSFs were identified. This study found that clear needs assessment is the most influential CSF, while the long-term value of AI systems or services is the least influential. This study provides policymakers and public sector leaders with valuable insights, enabling them to formulate effective strategies to optimize the procurement process and establish a strong foundation for AI adoption. Finally, this will lead to an improved and more efficient public service delivery in the UAE.

This systematic review aims to examine how the public sector may leverage artificial intelligence (AI) use through the exploration of AI adoption frameworks and how such frameworks can steer best practices of AI implementation and governance in the sector. Through inclusion and exclusion criteria, 30 articles were retrieved from academic databases, specifically Science Direct, Springer Link, and Wiley Online Library. The AI adoption frameworks are categorized into four groups: Regulatory frameworks, normative frameworks, applicative frameworks, and evaluative frameworks. Regulatory frameworks can provide standardizing and prescriptive guidelines to public sector organizations adopting AI technologies. Normative frameworks can strengthen the ethical and human rights aspects of AI adoption instead of devaluing human skills and eroding human agency. Applicative frameworks can help public sector organizations achieve positive and responsible outcomes for AI adoption. Alternatively, evaluative frameworks can spell improvements in the quality of public service delivery after identifying areas for improvement in an evaluation of AI systems.