Digital transformation through innovation: The human-AI decision spectrum
基于航空航天、重型工程、信息技术和制药行业的案例研究，提出了一个数字创新概念模型，涵盖学习过程和产品开发过程，并识别出原创性、可靠性、可转移性和适应性四个特征，以指导人机交互决策。
The rapidly changing and increasingly complex processes enabled by artificial intelligence (AI) applications challenge the conventional concepts of innovation. In contrast to a general perception that AI adoption can augment innovation output, managers still lack empirical guidance on how to structure innovation processes with human-AI interaction across time and space. Drawing on observations from case studies in the aerospace, heavy engineering, information technology, and pharmaceutical sectors, this paper presents the development of a conceptual model for digital innovation to represent (i) Learning Processes (LPs) focusing on knowledge creation and knowledge reuse and (ii) Product Development Processes (PDPs) leading to radical and incremental changes. The conceptual model is inductively developed based on a theory building approach using multiple case studies. A set of transformative characteristics centralized on Originality, Reliability, Transferability, and Adaptability (ORTA) are identified to guide decision-making along multi-stage and cross-layer innovation processes involving cyclical handoffs between humans and machine agents. These ORTA characteristics form a base for strategic decision-making along the Human and AI decision spectrum suited to prepare companies for survival and prosperity in their journeys of digital transformation.