Exploring Organizational Readiness and Ecosystem Coordination for Industrial XR
Hasan Tarik Akbaba, Efe Bozkir, Anna Puhl, Süleyman Özdel, Enkelejda Kasneci
TL;DR
This study investigates why industrial XR adoption stalls at the Pilot Trap, revealing a Great Inversion in which organizational readiness rather than technical maturity constrains scaling. Through 17 expert interviews across adopters, integrators, and providers, it shows that misaligned KPIs, insufficient change management, and weak ecosystem coordination drive the persistence of pilots. The authors propose a problem-first, ecosystem-coordinated approach with staged scaling, IT co-design, executive sponsorship, and user-centric UX to convert pilots into sustained platforms. These findings shift the focus from hardware maturity to organizational transformation, offering practical guidance and advancing ecosystem-centric theory of XR deployment.
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.