The Dubai Government has made significant investments in digital learning through platforms such as Al Mawrid and Bayanati, enabling widespread access to employee training and upskilling. However, there remains a major gap in translating accumulated learning into intelligent workforce restructuring. This paper proposes “SkillChain DX,” a policy-driven framework that applies artificial intelligence (AI) to dynamically map employee-acquired skills to evolving job roles across departments, developed using a conceptual design science and policy analysis approach. The framework integrates blockchain to ensure secure, tamper-proof verification of skill credentials across diverse training platforms. To validate feasibility, a pilot prototype was implemented using sentence-transformer models for semantic skill inference and cryptographic hashing mechanisms for decentralized credential verification. Experimental evaluation across six controlled scenarios demonstrated an average role-matching accuracy of approximately 82%, blockchain transaction throughput exceeding 1000 operations per second, and near-instant credential verification with over 99% performance improvement compared to manual processes. The findings demonstrate that integrating AI-driven skill inference with decentralized credential verification can significantly enhance internal mobility, role alignment, and workforce planning at a policy level. The study benchmarks international practices and outlines a practical implementation path for the Dubai Government using only publicly available technologies and case studies, positioning SkillChain DX as one of the first integrated AI–blockchain policy frameworks tailored to public sector human resources (HR) transformation in Dubai. The proposed system framework bridges the current disconnect between training access and organizational transformation, supporting a proactive, transparent, and skills-first public sector, while offering actionable policy insights for future government HR modernization.
Al-Jaziri et al. (2026) studied this question.