Abstract: Recent field-experimental evidence reveals that workers systematically reduce their reliance on artificial intelligence recommendations when that usage is visible to evaluators, even at measurable performance costs. This phenomenon—termed "AI shaming"—reflects emerging workplace norms in which heavy AI adoption signals lack of confidence, competence, or independent judgment. Drawing on labor economics, organizational behavior, and technology adoption research, this article examines how image concerns shape AI integration in contemporary organizations. Analysis shows that workers fear visible AI reliance conveys weakness in judgment—a trait increasingly valued in AI-assisted work—leading to systematic under-utilization of algorithmic recommendations. The performance penalty is substantial: accuracy declines approximately 3.4% when AI use becomes observable, with one in four potential successful human-AI collaborations lost to visibility concerns. These effects persist despite explicit performance incentives, reassurances about worker quality, and clear communication that evaluators assess only accuracy on identical AI-assisted tasks. The article synthesizes evidence on organizational responses, including transparency recalibration, distributed evaluation structures, and purpose-driven culture shifts, while highlighting why overcoming AI stigma proves particularly resistant to conventional interventions. Findings underscore that realizing AI's productivity promise requires not only better algorithms but fundamental rethinking of how organizations frame, monitor, and reward technology adoption.