Disentangling Innovation Practices in Automation-Adopting Organizations: a Co-Performance Perspective
2026-08-17 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors studied how people in charge of innovation at a European airport handle introducing automation in their work. They found that these innovation practitioners often focus on making systems fully automated first and think about how humans fit in later. Their research identified five design principles for better cooperation between humans and machines but noted challenges like limited changes to solutions and little ongoing learning after testing phases. The authors suggest it would help to think more about human roles earlier, try ideas step-by-step, and involve workers more throughout the process.
automationinnovation practitionershuman-computer interactionco-performanceautonomous operationspilot phasehuman rolesiterative designworker-automation arrangementsco-learning
Authors
Garoa Gomez-Beldarrain, Kars Alfrink, Euiyoung Kim, Elisa Giaccardi, Alessandro Bozzon, Himanshu Verma
Abstract
As organizations increasingly adopt automation, innovation practitioners are responsible for selecting, adapting, testing, and implementing externally sourced innovations. However, little is known about how these upstream practices shape worker-automation arrangements, limiting our ability to intervene in innovation practice to address automation adoption challenges. To disentangle this relationship, we interviewed nine innovation practitioners at a major European airport pursuing long-term autonomous operations and analyzed their practices through a co-performance lens. We synthesize five co-performance design principles and examine where current practices align or conflict. Our findings reveal tensions: innovation practitioners prioritize full-automation arrangements while postponing human considerations; contextual constraints shape solutions, but openness to reconfiguration remains limited; and co-learning rarely extends beyond pilot phases. These insights provide HCI research and practice with guidance for reframing the conceptualization of automation, particularly by encouraging earlier consideration of human roles, promoting iterative visions, and recognizing workers as co-designers throughout innovation pipelines.