🤖 AI Summary
This study addresses the persistent “pilot purgatory” that hinders the large-scale deployment of industrial extended reality (XR) applications. Through in-depth interviews with 17 industry experts and an ecosystem analysis framework, it reveals that the primary barriers have shifted from technological maturity to organizational readiness and stakeholder coordination. The work proposes a “great reversal” perspective, arguing that systemic factors—such as organizational change resistance, misaligned performance metrics, and internal political dynamics—now constitute the core challenges, rather than technical limitations. Emphasizing a problem-driven, ecosystem-coordinated transition pathway, the study identifies incentive misalignment as a critical friction point, offering both theoretical grounding and practical guidance for scaling industrial XR beyond isolated pilots.
📝 Abstract
Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.''This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion''in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.