Digital Transformation and Algorithmic Hiring: Evaluating AI Recruitment Failures in Biotechnology Talent Acquisition

Abstract

The rapid adoption of artificial intelligence (AI) in recruitment marks a significant transformation in how organizations identify, evaluate, and select talent. Although AI-driven hiring systems offer efficiency, cost reduction, and standardized decision-making, their implementation may introduce organizational risks if technological capabilities do not align with complex workforce requirements. This case study analyzes a global biotechnology company that adopted an AI-based applicant screening system as part of a broader digital transformation initiative. The investigation reveals that algorithmic screening tools inadvertently excluded highly qualified candidates for specialized research and development positions, thereby narrowing the candidate pool despite ongoing staffing shortages. Drawing on peer-reviewed literature from databases such as Google Scholar and ProQuest, the analysis employs a narrative literature review to assess the organizational drivers, benefits, and risks of AI-enabled recruitment. The findings demonstrate that keyword-based screening models, limitations in training data, and insufficient human oversight contributed to algorithmic bias and false-negative screening errors. These results show that automated hiring systems can undermine strategic talent acquisition in knowledge-intensive industries when organizational processes and technological tools are not effectively integrated. The study applies strategic and change management frameworks, including the McKinsey 7S model and Kotter’s change model, to interpret the organizational dynamics underlying the technology failure. The results underscore the importance of sociotechnical integration, governance structures, and leadership oversight to ensure that AI recruitment systems support rather than replace human judgment. The study concludes with practical recommendations for organizations seeking to implement AI-enabled hiring technologies responsibly while maintaining access to diverse, highly specialized talent in scientific and innovation-driven sectors.