Impact of Artificial Intelligence on HR practices in the UAE

The study examines how rapidly evolving AI technologies are influencing HR practices, particularly within the UAE context where digitization aligns with national visions such as Abu Dhabi Economic Vision 2030. It addresses challenges in HR decision-making (e.g., performance appraisal validity, small and sparse HR datasets, potential algorithmic bias, and legal and socio-psychological concerns) and focuses on AI’s potential to enhance recruitment and broader HR functions. The stated objective is to offer a framework for successful implementation of AI into recruitment from both applicant and employer perspectives in the UAE. The research questions are: (1) To what extent have employers implemented AI into HR practices? (2) How do UAE digital natives perceive the implementation of AI into recruitment? (3) To what extent can AI be utilized in HR practices in the UAE to recruit digital natives? The purpose is to inform strategy for integrating AI into HR, and the importance lies in guiding organizations through HR digitization while considering fairness, explainability, and employee perceptions.

The literature review discusses several HR domains affected by AI: (1) Strategic HR planning through AI: Big data and predictive analytics can improve precision in forecasting workforce needs and inform HR strategy, though responsible algorithm use is required to avoid discrimination and unequal access. (2) Smooth recruitment and selection: AI supports resume parsing, automated screening, applicant ranking, chatbots for candidate engagement, and personality inference from online footprints. Tools like HireVue can analyze video interviews (tone, facial expressions) to benchmark against top performers, accelerating time-to-hire and reducing manual workload, while raising concerns about bias and validity. (3) Planned training and development: AI-enabled personalized learning (e.g., mobile coaching, individualized employee maps) can tailor development paths, monitor training progress, improve satisfaction, and reduce costs; however, sensitive issues still require human interpretation. (4) Tactical performance appraisal: AI can record performance data objectively, integrate external benchmarks, and support fairer standards, potentially increasing employee motivation and reducing waste. (5) Ease of use and efficient HR practices: AI enables process automation, cognitive insights, and autonomous decisioning; examples include NASA’s AI-enabled HR tasks and applications in customer behavior prediction, benefits selection, and fraud detection. Overall, AI promises efficiency and improved decision-making but must be implemented with attention to fairness, transparency, and usability.

Design: Mixed-method study combining qualitative (exploratory) and quantitative (explanatory) components to identify and test relationships among AI and HR practice factors. Qualitative component focused on perceptions of UAE natives and employers regarding AI in recruitment; quantitative component assessed relationships among constructs in a proposed framework using PLS-SEM. Sampling and participants: Organizations were selected based on active use or development of AI in HR processes. Given the nascent state of AI-in-HRM and limited adopters in the UAE, firms beyond the UAE were not strictly excluded, though the focus remained on UAE practices. Qualitative data comprised 8 semi-structured interviews with HR professionals conducted via Skype due to COVID-19; interviews were in Arabic and translated into English. Quantitative data were collected via a close-ended questionnaire (5-point Likert scale) from 248 respondents (HR employees and AI staff) using convenience sampling; 77% female, largest age group 40–49 (44.4%), varied tenure. Measures: Items for constructs (Strategic HR planning through AI; Smooth recruitment and selection process; Planned training and development process; Tactical performance appraisal; Integration of AI; Ease of use; Efficient HR practices) were adapted from prior literature and expert recommendations. Analysis: Qualitative data were analyzed using thematic analysis (interpretivist paradigm) with coding to derive themes. Quantitative analysis used PLS-SEM: measurement model for reliability/validity, then structural model for hypothesis testing. Reported reliability included Cronbach’s alpha values (most constructs ≥0.70) and convergent validity (composite reliability and AVE meeting benchmarks). Path coefficients and significance were estimated with standard errors, t-stats, and p-values.

Qualitative findings: - Human touch remains valued in conventional recruitment; participants noted better communication and relationship-building with human-to-human interactions. - 6 of 8 interviewees reported using AI primarily for pre-screening/pre-selection (e.g., parsing resumes, social media screening via LinkedIn/Facebook, and personality assessments). - Strategy development (who/where/when/how to recruit) was seen as requiring human capabilities; interviewees did not emphasize AI use in strategic recruitment planning. - Benefits of AI: reduced administrative workload, faster processes, improved candidate experience, ability to identify passive candidates, and potential reduction in human bias. - Challenges: adaptation to new technology, lack of trust and technological readiness, and the need for appropriate tools and skills. Quantitative findings (PLS-SEM): - Strategic HR planning through AI → Integration of AI: negative, significant (β = -0.575, p < 0.001). - Smooth recruitment and selection process → Integration of AI: negative, significant (β = -0.693, p < 0.001). - Planned training and development process → Integration of AI: positive, significant (β = 0.231, p = 0.021). - Tactical performance appraisal → Integration of AI: positive, significant (β = 0.719, p < 0.001). - Integration of AI → Efficient HR practices: positive but not significant (β = 0.104, p = 0.131). - Moderated path (Integration of AI → Ease of Use → Efficient HR practices): negative and not significant (β = -0.093, p = 0.066). Measurement model highlights included acceptable composite reliability and AVE across constructs; illustrative outer loadings generally exceeded typical thresholds.

The findings indicate that AI adoption in HR within the UAE context is concentrated in operational areas that lend themselves to data capture and automation. Qualitative insights show recruiters value human interaction and communication, explaining why strategic recruitment decisions remain human-led, while AI is applied to pre-screening and administrative tasks. This addresses RQ1 by showing partial implementation of AI across HR functions, with stronger use in pre-selection rather than strategic planning. For RQ2, perceptions among UAE stakeholders highlight openness to AI’s efficiency benefits but emphasize trust, usability, and the preservation of human elements in recruitment, suggesting that successful adoption depends on user acceptance and change management. For RQ3, the quantitative model suggests that enhancing training and development and improving performance appraisal processes are positively associated with integrating AI, whereas existing approaches to strategic HR planning and smooth recruitment processes may be inversely related to AI integration, potentially reflecting organizational maturity, process fit, or change resistance. Despite a positive coefficient, integration of AI did not translate into statistically significant improvements in efficient HR practices in the model, and ease of use did not significantly moderate this relationship, underscoring the need to address usability, alignment, and process redesign. Practically, organizations should leverage AI to automate routine HR tasks, adopt conversational AI to streamline transactions, and enable HR professionals to focus on strategic planning. Ensuring fairness, explainability, and responsible algorithm use is essential to realize benefits and maintain trust.

The study contributes mixed-method evidence on AI’s impact on HR in the UAE, demonstrating that AI is most effective in automating routine, administrative, and pre-screening tasks, accelerating recruitment, and potentially reducing bias. Positive, significant quantitative links emerged between AI integration and both training and development as well as performance appraisal processes, while links from existing strategic planning and smooth recruitment processes to AI integration were negative. Integration of AI did not significantly improve efficient HR practices in the tested model, and ease of use did not significantly moderate this effect. Overall, AI should extend—not replace—conventional recruitment, complementing human judgment and interaction. Organizations should invest in training and technological readiness to capture AI’s benefits and to support HR’s shift toward strategic activities.

- Limited number of organizations in the UAE actively integrating AI in HR constrained sampling; while the focus was the UAE, firms beyond the UAE were not strictly excluded due to the nascent adoption landscape. - Convenience sampling for the survey (n=248) may limit generalizability. - Qualitative component included only 8 interviews, all conducted via Skype during COVID-19 restrictions; interviews conducted in Arabic and translated into English may introduce translation nuances. - The quantitative design relied on self-reported measures from HR/AI professionals and used secondary data to complement findings. - Cross-sectional data and PLS-SEM preclude causal inference beyond modeled associations.