Assessing The Impact of AI-powered Recruitment on Operational Efficiency and Workforce Diversity in Zimbabwe’s Coal Mining Sector

Mailos Mumpande; Shepard Wara

doi:10.36948/ijfmr.2026.v08i02.68875

Assessing The Impact of AI-powered Recruitment on Operational Efficiency and Workforce Diversity in Zimbabwe’s Coal Mining Sector

Author(s)	Mr. Mailos Mumpande, Mr. Shepard Wara
Country	Zimbabwe
Abstract	This study sought to assess the impact of AI-powered recruitment on operational efficiency and workforce diversity in Zimbabwe’s coal mining sector. The study was guided by a pragmatism research philosophy; hence a mixed methodology paradigm was adopted. A sequential explanatory research design was employed. A target population of thirty participants participated in the study. Since the population was small, all thirty participants were considered into the sample through census sampling. To gather quantitative data, a questionnaire was employed, followed by interviews with 5 HR Directors who were purposively sampled. Quantitative data was gathered and analysed using content analysis method while qualitative data was analysed thematically. The study found that AI recruitment tools reduce time involved in hiring, improves new hire retention, reduces recruitment costs and improves the speed of candidate feedback, hence a positive impact on operational efficiency. Besides, the study found that AI tools help mitigate unconscious human bias in candidate shortlisting, thereby enhancing workforce diversity. In light of these findings, the study recommends that coal mining companies in Zimbabwe should embrace AI recruitment away from traditional recruitment methods for success towards job-relevant skills and predictive cognitive assessments that have been validated for non-discriminatory outcomes. The study also recommends that coal mining companies in Zimbabwe should move away from unstructured interviews to highly structured, competency-based interviews using consistent scoring rubrics that are not influenced by the AI’s initial ranking in order to neutralize both human and algorithmic bias.
Keywords	Artificial intelligence, Recruitment, Operational efficiency, Workforce diversity
Field	Business Administration
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-13
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.68875

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Assessing The Impact of AI-powered Recruitment on Operational Efficiency and Workforce Diversity in Zimbabwe’s Coal Mining Sector

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