Generative Artificial Intelligence in Managerial Processes: Capacities and Ethical Imperatives
Keywords:
Generative artificial intelligence, intelligent management, data-driven decision-making, AI ethics, algorithmic transparency, data fairnessAbstract
Objective: This study aims to systematically explore the capacities, challenges, and ethical imperatives of generative artificial intelligence (AI) in managerial processes, providing a comprehensive framework for its responsible and effective adoption in organizations.
Methods and Materials: This qualitative review study was conducted through a systematic analysis of 12 peer-reviewed academic articles related to generative AI and management. Articles were selected based on relevance, credibility, and recency. Data were analyzed inductively using Nvivo 14, following open, axial, and selective coding procedures to identify core themes and subthemes.
Findings: The qualitative analysis revealed three major capacities of generative AI in management: enhancing data-driven decision-making, improving organizational efficiency, and facilitating strategic innovation. However, challenges such as inadequate infrastructure, algorithmic bias, organizational resistance, and legal ambiguities hinder full-scale implementation. The findings also emphasize the need for ethical frameworks encompassing algorithmic transparency, human accountability, data fairness, and privacy protection.
Conclusion: Generative AI can act as a transformative force in managerial contexts if embedded within ethical principles, human oversight, and institutional governance. Achieving equilibrium between technological efficiency and moral responsibility is essential for ensuring sustainable and human-centered utilization of generative AI in decision-making.
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