Abstract

This case study examines the co-development of a micro credentials program by the Statistics Centre - Abu Dhabi (SCAD) and the United Arab Emirates University (UAEU). It outlines the provision of agile, practice-oriented training in official statistics while advancing Abu Dhabi’s objective of building a high-skill, AI-ready government workforce. Reflecting a global trend in collaboration between National Statistical Offices (NSOs) and academia, the initiative responds to evolving competency needs in data science, artificial intelligence, and modern statistical production. Building on Pfeffermann (2025), the study shows how modular learning and targeted upskilling can strengthen statistical capacity and support national data and AI strategies.

The program equips Abu Dhabi government professionals with competencies in data analysis, visualization, machine learning, and methodological innovation. These skills are essential for designing, testing, and validating AI applications in official statistics and administrative data. Co-designed by SCAD’s Statistical Training Institute (STI) and UAEU faculty, the curriculum blends academic rigor with operational relevance and aligns with the UAE’s National Qualifications Framework at Level 7. The paper outlines the program’s structure and first-year implementation, using participant feedback and institutional reflections to assess its effectiveness in developing skills for AI-enabled statistical operations.

The case situates the initiative within SCAD’s mission to build a decentralized statistical ecosystem and an AI-native public sector. By integrating machine learning, algorithmic thinking, and advanced analytics into official statistics training, the program enables staff to identify high-value AI opportunities, collaborate with technical teams, and interpret AI-generated outputs for policy use.

Findings highlight gains in bridging theory and practice, workforce readiness, and university engagement, alongside challenges in partner alignment, scalability, and quality assurance. The case offers transferable lessons for NSOs and universities seeking flexible, future ready training models for an AI-enabled statistical ecosystem.