The advent of AI has changed perceptions about practices in many fields, including human resource management (HRM). The HRM is assisted globally through AI mechanisms, starting from talent acquisition for increasing efficiency in recruitment without compromising the merit of candidates to finding the most appropriate one for an organization. Key recruitment processes include candidate sourcing, screening, and selection, which are automated by AI tools. This can lead to greater scalability and perhaps lower human bias. On the other hand, some of the key challenges which HRM also faces with the integration of AI are the algorithmic bias, overlooking qualitatively strong candidates, and limitations regarding innate human attribute assessment. AI systems invariably fall short in accounting for the psychological traits, personality growth, emotional intelligence, and nuances of behavioral manifestations and socio-cultural background since their design lack the capacity to read the subtle human traits related to body language and responses. While state-of-the-art neuro-fuzzy logic and sophisticated algorithms have been developed, most of these systems are based on predefined parameters of each organization and require structured inputs regarding specific job roles. Although AI technology remains very user-friendly, there are certain doubts raised regarding candidate and organizational friendliness. This has resulted in AI technology needing to adapt and evolve according to human and organizational requirements. As long as these constraints are not resolved, human intelligence and AI technology will continue to work in tandem with each other. In the current literature on talent acquisition, there seems to be a lack of a comprehensive review of the role of AI in talent acquisition. The current research seeks to fulfill this knowledge gap by examining both the benefits and drawbacks of AI in talent acquisition.