Emerging Technologies and Management Practices: Navigating the Convergence of Computer Science and Organizational Management

Abstract

The complex intersection of organizational management and machine learning (ML) is examined in this study, along with adoption opportunities and obstacles. Using interpretivist philosophy as well as a deductive approach, we performed a thorough analysis with secondary data. The quantitative analysis showed rising rates of machine learning adoption in a variety of sectors, most notably finance and healthcare. The main challenges determined by the challenges and opportunities matrix were workforce adaptation, integration complexities, in addition to ethical concerns. Organizational structures are reshaped by ML, which dramatically improves operational efficiency and strategic decision-making. Metrics for organizational adaptability place a strong emphasis on the development of workforce skills, the efficacy of change management, implementation agility, and feedback loop application. Critical analysis emphasizes how important it is to support adaptive organizational cultures and match the adoption of ML with moral values. Proactive ethical concerns, strategic workforce development, alongside cooperative policy frameworks are encouraged in the recommendations. Subsequent research ought to investigate the long-term effects of machine learning on organizational dynamics through looking at real-time case studies.