Ancon Technologies Case

Abstract

This case study examines the organizational, operational, and ethical challenges Ancon Technologies faced during its digital transformation when implementing an artificial intelligence (AI)-driven applicant screening system. In particular, the study focuses on how this company's reliance on keyword matching and historical hiring data unintentionally excluded highly qualified candidates, delayed the filling of critical research positions, and weakened its ability to compete for them within the biotechnology industry. The objective of the research is to identify the design, governance, and oversight improvements needed to better align AI-enabled recruitment systems with organizational talent acquisition goals. A qualitative, literature-based case study methodology was used to analyze the problem within its strategic and technological context. A secondary research approach was used to inform the analysis, drawing from one peer-reviewed journal article, scholarly books, and academic literature on digital transformation, artificial intelligence in recruitment, ethical governance, and change management. Using the Strategic Alignment Model, Digital Transformation Theory, and Kotter's Eight-Step Change Model, the study identifies and addresses the root causes of recruitment failure. There is a growing body of evidence showing that misalignment among algorithm design, training data, governance practices, and workforce needs can compromise the effectiveness of recruitment and the effectiveness of an organization. The case highlights that overreliance on automated decision-making without sufficient human oversight increases the risk of bias, exclusion, and poor hiring outcomes. The study concludes that organizations can improve AI-driven recruitment by broadening evaluation criteria, updating training data, strengthening governance frameworks, and embedding human judgment into decision-making processes. This study demonstrates the importance of aligning technology, ethics, and strategy to support fair, effective, and sustainable talent acquisition through the responsible implementation of AI.