Digital transformation through innovation: The human-AI decision spectrum

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.