The competitive nature of the job market has caused organizations today to be challenged with the efficient screening of large volumes of resumes toward identifying the right candidate. Automated resume screening systems (ARSS) have revolutionized hiring processes by streamlining them in such a manner. This paper explores the novel technologies on which ARSS are based, including machine learning algorithms, NLP, and data analytics. We therefore direct our attention to the review of the current ARSS models on the effectiveness of increasing recruitment outcomes, reducing bias, and improving candidate experience. Besides this, we discuss the implications of the implementation of ARSS in Talent Acquisition Strategy and identify avenues for further research. The results therefore show it is not about hiring efficiency but rather inclusiveness within the recruitment process by right use of ARSS.