Artificial intelligence-based organizational decision-making in recruitment practice

BACKGROUND: In the realm of contemporary management, decision-making emerged as a pivotal aspect, particularly within the human resource (HR) domain, profoundly influencing an organization’s culture and efficacy. The integration of high-quality data and artificial intelligence (AI) based automation in the recruitment process became very important in driving data-driven decision-making processes while hiring for any position. OBJECTIVE: This study sought to deeply explore the landscape of AI-based decision-making in the recruitment process while investigating the adoption of AI-enabled data-driven approaches within recruitment practice. Additionally, it aimed to propose a strong and healthy model aimed at significantly enhancing organizational effectiveness. METHODS: Smart PLS tool stood as the cornerstone of the analytical approach, offering a healthy framework for testing and validating our proposed model. To achieve the objectives, a two-fold approach was employed. Firstly, a comprehensive review of existing literature was conducted, encompassing seminal works and contemporary research on AI in HR and recruitment decision-making. Secondly, a small-scale survey was administered across diverse organizations representing IT and manufacturing industries. The survey focused on evaluating the current trends in AI adoption within the recruitment process and its impacts on decision-making, specifically emphasizing the utilization of assisted intelligence tools for routine activities and reporting upkeep. This dual-method approach provided insights into the practical challenges faced by organizations in adopting end-to-end decision-making automation. RESULTS: The combination of information gathered from a small survey and extensive research showed a common pattern: widespread use of AI tools mainly for routine activities categorized as assisted intelligence, offering partial solutions for the recruitment day-to-day tasks. However, it underscored the growing necessity for automation intelligence that encompasses holistic end-to-end decision-making processes in recruitment practice. The utilization of the tool Smart-PLS for analyzing the data from the small-scale survey of 50 respondents further enhanced the depth and reliability of the findings. For declaring the model’s fitness, the Smart PLS tool was used by providing various indicators and techniques, such as goodness-of-fit measures and bootstrapping procedures, to ensure the accuracy and reliability of the model. CONCLUSIONS: In the evolving landscape of increased automation, striking a balance between AI-driven decision-making in recruitment and human intervention holds paramount importance, especially in decisions affecting employees. The utilization of the Smart PLS tool not only enhanced the credibility of our findings but also underscored our commitment to employing state-of-the-art methodologies in advancing knowledge within our field. This study advocates for the adoption of an AI-enabled data-driven approach, emphasizing its potential to optimize recruitment practice and strengthen organizational effectiveness.