Abstract

This article addresses the growing concern that generative artificial intelligence (AI) may replace human expertise in organizations. Instead of asking whether AI should be used, it examines why human judgment rooted in experience cannot be fully replaced by current AI systems and how organizations can work with AI more effectively. Drawing on research from cognitive science, neuroscience, and organizational studies, the paper explains how people use prior experience to interpret context, notice subtle cues, and make sense of ambiguous situations—capabilities that differ fundamentally from how large language models process data. Evidence from recent studies of AI use in hiring, performance management, healthcare, and knowledge work shows recurring problems, including mistakes in unusual cases, missed context, over-reliance on AI recommendations, and reduced visibility of real skill differences among employees. In response, we propose a five-part Human–AI Collaboration Framework designed to help organizations use AI for efficiency while keeping human judgment active and accountable in key Human Resource Management decisions. The analysis shows that AI performs best in routine, data-rich situations but falls short when decisions require lived experience and contextual understanding. By framing organizations as systems built on accumulated experience, this article offers practical guidance for responsible AI integration and outlines directions for future research on human–AI collaboration.

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