Orchestration logics for artificial intelligence platforms: From raw data to industry‐specific applications
研究了医疗影像领域五个AI平台，识别出四种编排逻辑（平台资源化、数据中心协作、分布式优化、应用中介），帮助平台所有者协调各方参与者，实现从原始数据到行业应用的转化。
Abstract Artificial intelligence (AI) platforms face distinct orchestration challenges in industry‐specific settings, such as the need for specialised resources, data‐sharing concerns, heterogeneous users and context‐sensitive applications. This study investigates how these platforms can effectively orchestrate autonomous actors in developing and consuming AI applications despite these challenges. Through an analysis of five AI platforms for medical imaging, we identify four orchestration logics: platform resourcing, data‐centric collaboration, distributed refinement and application brokering. These logics illustrate how platform owners can verticalize the AI development process by orchestrating actors who co‐create, share and refine data and AI models, ultimately producing industry‐specific applications capable of generalisation. Our findings extend research on platform orchestration logics and change our perspective from boundary resources to a process of boundary processing. These insights provide a theoretical foundation and practical strategies to build effective industry‐specific AI platforms.