新兴的'混合专业人士'：生成式AI对阿联酋技能需求变化的影响

The Emerging ‘Hybrid Professional’:GenAI’s Impact on Skill Demand Abstract G is invariant to geography, confirming its nature as a structural force. The results reveal sharpoccupational polarisation: Clerical Support Workers face high substitution risks (53.8 percentautomatability), while manual roles remain insulated. Critically, for the dominant professionalclass, AI’s primary mechanism is not substitution but the fundamentalredefinitionof coretasks. By identifying the emergence of the “hybrid professional,” this study provides a preciseframework for policymakers to align Emiratisation, migration, and upskilling strategies with the Introduction T critical uncertainty: firms and nations are investing billions in a technology whose impact theydo not yet fully understand. Global models offer broad predictions, but they are insufficientfor navigating the high-stakes reality of a nation like the United Arab Emirates (UAE), whereimmense, state-driven AI ambition collides with unique economic and demographic pressures.The UAE’s national strategies for economic diversification and workforce nationalisation (whatthe UAE government refers to as “Emiratisation”) are being forged at the very moment AI is set This challenge is particularly acute in a labour market where expatriate workers havehistorically dominated most sectors, particularly in low- and semi-skilled roles. The rationaleof this analysis, therefore, stems from the urgent need to understand how AI can be leveraged This report confronts this uncertainty by moving beyond speculation to provide a high-resolution map of AI’s real-world impact on the UAE’s labour market. The challenge forpolicymakers and business leaders is not the absence of ambition, but a lack of granular,empirical evidence. It is one thing to know that AI will transform skills; it is another to know Thisreport introduces a novel,task-level methodology to analyse AI’s impact.Bydeconstructing over 23,000 online job postings into their constituent tasks, the authors’proprietary analytical pipeline creates the Job Automatability Index—a bottom-up, data-drivenmeasure of automation exposure. This approach allows one to pinpoint the true loci of change,revealing a sharp occupational polarisation hidden within broad sectoral trends. This report Literature Review G North, the UAE is defined by asevere demographic imbalance—where expatriates dominatethe workforce—and a state-mandated urgency to integrate nationals into the private sector(‘Emiratisation’). This creates a specific, high-stakes paradox: national strategies are pushing citizens towardadministrative and white-collar roles that are now the primary targets of AI automation. Globalmodels cannot account for this collision between workforce nationalisation and technologicalsubstitution. This review synthesises established research to pinpoint this critical gap, arguing A New Technological Paradigm: Disruption of Speed and Scope The current technological wave driven by GenAI is fundamentally distinct from previous shiftsin its speed and scope.2Its adoption rate is unmatched: ChatGPT, for example, reached 100million users in only two months—a milestone that took the internet seven years—dramatically Simultaneously, its scope extends beyond the routine administrative tasks disrupted byearlier technologies4to target core cognitive functions, like coding, design, and analysis, longconsidered the domain of high-skilled professionals.5Nearly 70 percent of occupations may Skills-Based Transformation: The Four Occupational Archetypes The most effective analysis of AI’s impact views occupations not as monolithic jobs to beeliminated but as dynamic bundles of tasks. This skill-first approach reveals that AI is apolarising force, sorting roles into four distinct archetypes: created, augmented, disrupted,and insulated.7Created occupations, such as AI engineering, are a small but rapidly growing Critical transformations occur in the other two categories. Disrupted occupations, such asadministrative assistants, are defined by a high concentration of automatable skills, creating anurgent need for skills-based career transitions. Augmented occupations represent the future ofprofessional work. In roles like software engineering, AI automates routine components (e.g.,code generation), freeing professionals to focus on higher-value human skills. Firms using AI Emerging Dynamics: Inequality and the Productivity Paradox This skills transformation is not unfolding in a uniform manner. A consistent finding is thatAI’s impact deepens gender inequality, as women, globally, are overrepresented in “disrupted”occupations while men are concentrated in “augmented” or “insulated” roles.10For instance,recent US-based data indicates that 79 percent of working women hold jobs susceptible to Simultaneously, a “productivity paradox” has emerged, where surging individual efficiency isfailing to translate into organisational profit. On one hand, AI adoption