Artificial intelligence for data-driven human resource decision-making: A systematic review

The study unites the thoughts and discussions regarding how AI, as a mean of utilizing access to big data in prompt decision-making, is transforming the decision pattern and significance of the core human resource (HR) functions, along with the complexities and concerns associated. With a view to doing so, the study adopts a systematic literature review methodology based on 25 papers selected following the PRISMA framework from the Scopus data, ranging from 2023 to 2025. The study portrays that different contemporary AI technologies, including machine learning, deep learning, natural language processing, generative AI and big data analytics, have reshaped the decision pattern of core HR functions such as recruitment, attrition and performance management. Data reveals that HRM has now improved predictive and personalization capabilities through advanced neural networks, explainable AI tools and applicant tracking systems. However, this AI-HR integration presents multidimensional challenges, including organizational resistance, algorithmic bias, transparency and data integration. The study primarily presents its findings in three dimensions: AI-driven HR practices, challenges and ethical concerns of AI technologies. The study develops a set of theoretical and practical implications for HR professionals, policymakers and researchers to better understand human-AI interaction, and adopt personalized strategies that reduce employee dissatisfaction and resistance. The study also lays directions for further exploration considering multidisciplinary, multi-cultural and cross-country research to generalize the results, as well as a temporal scope for a comparative analysis of AI adoption in both the pre- and post-GPT eras, with an emphasis on human-AI interaction and ethical concerns. • Three unique dimensions are evaluated: AI applications, practical challenges and ethical risks. • Contemporary AI transforms HR decisions by enabling advanced, data-driven and more sophisticated decision-making processes. • Studies recommend personalized strategies reflecting employee-specific dynamics instead of one-size-fits-all approaches. • Require balanced human–AI interaction to reduce black-box opacity, and ensure transparency and interpretability.