Reimagining recruitment: traditional methods meet AI interventions- A 20-year assessment (2003–2023)

The study aims to compile existing research on traditional hiring and recruitment practices adopting AI. This study explicitly tries to comprehend the significant theories, analytical methods, procedures, and essential aspects of recruiting evaluation. The study analyses 60 research articles using a systematic literature review procedure, and it summarises its conclusions using the theory-context-characteristics-methodology (TCCM) framework. This research shows a substantial change in talent acquisition strategies over the previous two decades. While tried and tested, traditional hiring practices have encountered several obstacles, such as lengthy manual screening processes, biases, and restricted access to a diverse candidate pool. AI-adopted recruitment is a possible alternative, delivering improved speed, impartiality, and customized applicant experiences. The paper offers a conceptual framework for future empirical research derived from the SLR and unique synthesis of TAM and RBV. By incorporating AI-assisted recruiting tools, emphasizing user-centered design, and allocating resources to artificial intelligence, organizations can improve their decision-making process. AI skills can be improved by HR personnel and training programs, and resource distribution ought to be based on perceived utility. This study adds to the body of knowledge on talent acquisition techniques and lays the groundwork for subsequent investigations into the changing function of AI in HR procedures.