Examining the Effectiveness of Artificial Intelligence (AI) in Organizational Recruitment at ZANACO Bank

This thesis examines the effectiveness of Artificial Intelligence (AI) in Organizational recruitment at ZANACO Bank in Zambia. AI use in recruitment globally has gained significant traction owing to the use of AI-Powered tools which automate routine tasks. Apart from automation of tasks, the adoption of AI worldwide can be attributed to a number of factors such as improved efficiency, and enhanced candidate interaction. The main objective of this study is to examine the effectiveness of AI in organisational recruitment at ZANACO Bank in Zambia. The specific objectives are to establish the effects of AI in recruitment at ZANACO, examine the effect of AI on recruitment quality by considering candidate fit and performance of candidates, identify limitations of AI in recruitment, and evaluate the effectiveness of AI-driven candidate screening and shortlisting processes in improving efficiency and reducing bias. A mixed methods approach was employed which combined qualitative and quantitative data collection and analysis methods. This method was chosen as it allows for a more comprehensive understanding of the research problem by combining the strengths of both qualitative and quantitative methods. The target population included the recruitment staff in ZANACO, and the job applicants who have been end users of the AI-powered recruitment processes. Data collection was by way of questionnaire, secondary data from the internet, and interviews from Focused Group Discussions (FSGs). This data was analyzed using SPSS and Excel programs. The findings of the study reveals that ZANACO management has invested 80% of HR resources to align to AI. This has led to improvements such as reduced time-to-hire, improved the quality of candidates and enhanced recruiter efficiency by over 60%. In addition, challenges in the AI process were discovered and consisted of over-automation which represented 40%, technical challenges, and data quality issues. Some of the limitations faced by ZANACO in AI implementation include resistance to change, biasness of AI systems, and difficulties in integrating AI system with other existing HR systems. The study also established that AI has improved efficiency of recruitment at ZANACO by 50% and enhanced candidate experience by 20%. It has also positively impacted in improved matching of candidates and reduced biasness. The FGDs also revealed that AI has contributed to cost reduction of the recruitment process and contributed to the profitability and positive image of the organisation. The study also revealed that AI has reduced bias in recruitment to about 40% through mitigation of unconscious bias by evaluating candidates based on objective data. The recommendations arising from the study are that organizations ought to invest in continuous AI skills development, improvement of data quality and addressing regulatory compliance concerns to benefit much from AI-driven tools. ZANACO should conduct a thorough analysis of current recruitment processes to identify areas where AI can lead to organization value addition. The findings of the study have contributed to the growing body of knowledge in recruitment as it gives insights to HR practitioners, policy makers and researchers seeking to find out more on AI and its potential in the developing global trends of organisations.