TRANSFORMING TALENT ACQUISITION: INNOVATIONS, METRICS, AND PROCESS OPTIMIZATION THROUGH ADVANCED TECHNOLOGIES
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https://doi.org/10.8224/journaloi.v73i4.549##semicolon##
Talent acquisition metrics##common.commaListSeparator## advanced technologies##common.commaListSeparator## AI-driven recruitment##common.commaListSeparator## Machine learning##common.commaListSeparator## Data analyticsसार
Talent acquisition (TA) has experienced significant transformation through the use of advanced technologies, such as Artificial Intelligence (AI), Machine Learning (ML), data analytics, and blockchain. These innovations have transformed recruitment processes, addressing inefficiencies in traditional methods. AI-driven tools streamline tasks such as resume screening, predictive hiring, and candidate matching, while ML enhances personalization by tailoring strategies to individuals. Data analytics provides actionable insights for workforce planning, and blockchain provides security and transparency in background verification. Together, these technologies have improved efficiency, improved candidate experiences, and facilitated diversity and inclusion in hiring.
Despite these advancements, the challenges such as ethical concerns, algorithmic bias, data privacy risks, and resistance to change remain obstacles to adoption. High implementation costs and integration complexities are further complicating the process. Case studies of organizations such as Google and IBM highlight the potential of these technologies when implemented properly.
This paper highlights the need for further research focusing on measuring the effectiveness of these technologies using key hiring metrics such as time-to-hire, cost-per-hire, and quality-of-hire. These metrics are essential for making data-driven decisions and optimizing recruitment strategies. By addressing these areas, future studies can provide effective insights to build more efficient, ethical, and future-ready talent acquisition processes.
