The rapid adoption of AI in business is transforming various sectors, including human resources. Many firms struggle to implement data analytics, with 41% unprepared to utilize new tools. AI encompasses technologies automating tasks requiring human cognition, particularly in natural language processing and pattern recognition. Deep learning using neural networks is becoming prevalent, mimicking adaptive human decision-making. However, most organizations haven't fully utilized data analytics in employee management. While data analytics is more easily applied in marketing (predicting sales, customer behavior), its application in HR faces complexities, especially in defining 'better employee' and accurately measuring performance. Performance appraisal scores, a common metric, suffer from validity, reliability, and bias issues. Individual performance is also difficult to isolate from group performance. HR datasets tend to be smaller and contain less data compared to marketing, and critical outcomes (like employee terminations) are rare events, making prediction challenging. Employment decisions are also impacted by socio-psychological factors like perceived fairness and employee reactions to algorithm-based choices. This study aims to provide a framework for successful AI implementation in HR recruitment, focusing on the UAE context, guided by three research questions: 1) the extent of AI implementation in HR practices by employers; 2) UAE natives' perception of AI in recruitment; and 3) how AI can be used to recruit digital natives in the UAE.

The literature review explores AI's role in strategic HR planning, recruitment and selection, planned training and development, tactical performance appraisal, and the impact of ease of use on efficient HR practices. AI's capabilities include optimizing data, forecasting future demand, and improving the precision of HR planning. However, AI is not a bias-free solution, and algorithms can perpetuate existing inequalities. In recruitment, AI offers objective candidate assessment and efficient resume screening, using algorithms and machine learning for improved selection. AI-powered tools can enhance training and development by designing personalized programs and career development plans. In performance appraisal, AI offers objective data recording and fair performance standards. Finally, AI facilitates automation of routine HR tasks, providing cognitive insights for decision-making and enhancing efficiency. However, the literature highlights potential challenges like algorithm bias and the need for responsible AI implementation.

This study employed a mixed-method design, combining qualitative and quantitative approaches. Qualitative data was collected through eight semi-structured interviews with HR professionals and AI experts in the UAE. Interviews explored existing HR practices, AI integration in recruitment, challenges, and AI's potential in hiring. Thematic analysis was used to analyze interview transcripts. Quantitative data was gathered using a survey of 248 HR employees and AI staff from various UAE companies. The survey measured factors like strategic HR planning, recruitment process efficiency, training and development effectiveness, performance appraisal, AI integration, and ease of use, all using a 5-point Likert scale. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the quantitative data, testing the relationships between the variables. Convenience sampling was used for the survey.

Qualitative findings revealed that while HR professionals valued the human touch in traditional recruitment, AI was used mainly for pre-screening and pre-selection. AI was seen as beneficial for reducing administrative tasks, speeding up recruitment, and promoting equal opportunity. Challenges included technological adaptation and the need for appropriate tools. Quantitative findings (PLS-SEM analysis) indicated significant positive effects of planned training and development, tactical performance appraisal and AI integration, and AI integration with efficient HR practices on overall HR efficiency. The moderating role of ease of use was found to be insignificant. Cronbach's alpha values for all survey constructs exceeded the 0.7 threshold, indicating acceptable reliability. The results are presented in several tables, which detail the outer loadings, convergent validity, Fornell and Larcker criterion, and path analysis, showing significant relationships between variables.

The findings demonstrate that AI can significantly enhance HR efficiency in several areas. By automating routine tasks, AI frees up HR professionals to focus on strategic planning and candidate engagement. The significant positive relationships between AI integration and various HR practices highlight the potential benefits of AI adoption. However, the lack of a significant moderating effect of ease of use suggests that the successful implementation of AI depends more on strategic planning and integration rather than solely on user-friendliness. The importance of human touch in recruitment is also emphasized, suggesting a complementary rather than a fully replacement role for AI.

This study contributes to the understanding of AI's impact on HR practices in the UAE. AI can improve efficiency in several HR functions. However, successful implementation requires careful planning, training, and addressing potential challenges. Future research could explore AI implementation in other HR areas, such as compensation and benefits, or delve deeper into the specific types of AI technologies used in different HR contexts. It could also examine the effects of AI on employee morale and job satisfaction.

The study's reliance on convenience sampling may limit the generalizability of the findings. Future research could use a more representative sample to increase generalizability. Also, the study's focus on the UAE context may limit transferability of the findings to other countries with different cultural or regulatory environments. The relatively small number of qualitative interviews might not fully represent the range of views within the UAE HR landscape.