Paper Type
Short
Paper Number
PACIS2026-1996
Description
Organizations increasingly embed AI, analytics, and digital infrastructures into routine work, yet organizational learning theory offers limited explanation of how learning becomes routinized when signals, workflows, and governance are digitally mediated. This study develops AI/Analytics-Accelerated Learning Architecture (AILA), a micro–meso–macro framework explaining how structured traces are curated into organizational signals, embedded into workflow routines, and governed for adoption and scale. Using a qualitative single-case analysis of a five-year epilepsy learning health system, the study triangulates documents, operational artifacts, meeting transcripts, and leadership interviews to reconstruct how digitally mediated learning unfolded over time. The findings show that learning depends not on analytics alone but on the interdependence among reliable trace capture, workflow-embedded routines, and governance mechanisms. The study contributes to healthcare information systems and digital transformation research by theorizing the meso-level transmission layer through which digital infrastructures convert local experience into routinized organizational action.
Recommended Citation
Ko, Dong-Gil, "AI- and Analytics-Mediated Organizational Learning: Curated Insights, Workflow Routines, and Governance in an Epilepsy Learning Health System" (2026). PACIS 2026 Proceedings. 14.
https://aisel.aisnet.org/pacis2026/it_strategy/it_strategy/14
AI- and Analytics-Mediated Organizational Learning: Curated Insights, Workflow Routines, and Governance in an Epilepsy Learning Health System
Organizations increasingly embed AI, analytics, and digital infrastructures into routine work, yet organizational learning theory offers limited explanation of how learning becomes routinized when signals, workflows, and governance are digitally mediated. This study develops AI/Analytics-Accelerated Learning Architecture (AILA), a micro–meso–macro framework explaining how structured traces are curated into organizational signals, embedded into workflow routines, and governed for adoption and scale. Using a qualitative single-case analysis of a five-year epilepsy learning health system, the study triangulates documents, operational artifacts, meeting transcripts, and leadership interviews to reconstruct how digitally mediated learning unfolded over time. The findings show that learning depends not on analytics alone but on the interdependence among reliable trace capture, workflow-embedded routines, and governance mechanisms. The study contributes to healthcare information systems and digital transformation research by theorizing the meso-level transmission layer through which digital infrastructures convert local experience into routinized organizational action.
Comments
11-Strategy