When AI Assistance Becomes Invisible: Organizational Challenges of Competence Illusion in the Age of Generative AI

Abstract: The proliferation of large language models (LLMs) in knowledge work has fundamentally altered how individuals perform cognitive tasks and perceive their own capabilities. This article examines the LLM fallacy, a cognitive attribution error in which individuals systematically misinterpret AI-assisted outputs as evidence of independent competence, creating divergence between perceived and actual capability. Drawing on theories of automation bias, cognitive offloading, and distributed cognition, we analyze how LLM interaction properties—including opacity, fluency, and immediacy—obscure the boundary between human and machine contributions. Organizations face mounting challenges as traditional evaluation frameworks struggle to distinguish system-assisted performance from independently grounded expertise. We examine implications across hiring, credentialing, education, and professional development, and propose organizational responses centered on transparency architectures, process-aware evaluation, and calibrated AI literacy. This synthesis bridges individual-level attribution dynamics with institutional assessment practices, offering evidence-based guidance for organizations navigating the transformation of cognitive work in the age of generative AI.

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