•Artificial intelligence (AI) reshapes how innovation is organized.•AI overcomes human constraints in information processing.•AI helps to generate innovations.•AI exerts a transformative impact on ...the digital organization of innovation.
Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company's entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.
Artificial intelligence (AI) will have a substantial impact on firms in virtually all industries. Without guidance on how to implement and scale AI, companies will be outcompeted by the next ...generation of highly innovative and competitive companies that manage to incorporate AI into their operations. Research shows that competition is fierce and that there is a lack of frameworks to implement and scale AI successfully. This study begins to address this gap by providing a systematic review and analysis of different approaches by companies to using AI in their organizations. Based on these experiences, we identify key components of implementing and scaling AI in organizations and propose phases of implementing and scaling AI in firms.
•Artificial intelligence (AI) will profoundly impact firms in all industries and be the basis for powerful new capabilities.•To take full advantage of the potential benefits of AI technologies, firms must be able to successfully implement.•We systematically analyze the socio-technical components of firms’ approaches to using AI in their organizations•We identify phases of implementing and scaling AI in organizations: proving the concept, productionizing, and platformizing.
•Alphabet, Amazon, Apple, IBM, and Microsoft have all built complex business models to access and penetrate the healthcare markets.•For example, Google focuses strongly on the development of ...artificial intelligence (AI)-powered technologies to support physicians in clinical decision-making. Verily addresses pharmaceutical research with AI-enabled and wearable technologies to collect user-generated physiological data to better understand diseases mechanisms and improve drug discovery.•Although each Big Tech has different intentions and strategies, three dominant business logics can be derived: (i) more proactive and preventive care, (ii) more efficient and accurate diagnoses, and (iii) more efficient and data-driven pharmaceutical R&D.•The market entry of Big Tech indicates that pharmaceutical companies must rethink their current business models. In particular, they must accelerate their digital transformation to catch up with Big Tech and drive their digital revolution.
Digital health and digital pharma are considered supportive tools for patients and healthcare providers (HCPs), making the market highly attractive for industry players. Not surprisingly, Tech Giants have started to move into this area. We utilized established management models and publicly available information sources, such as annual company reports, and performed a thorough analysis to uncover the underlying business models of Alphabet, Amazon, Apple, IBM, and Microsoft in order to better understand their intention and course of entering the healthcare and pharma industries. Our results indicate that Big Tech or Tech Giants do address the needs of patients and physicians, while having built clear value propositions, value chains, and revenue models to sustainably revolutionize the healthcare and pharma industries.
An interview with Oliver Gassmann and Christian Nielsen, who work together at the University of St. Gallen on several research projects on AI-based business models, is presented. Among other things, ...they discuss about generative AI and its importance, the transformation of generative AI in the near future and the impact of generative AI on business models.
Are stakeholder management and innovation substitutes or complements in affecting firm performance? Extant research provides support for both positions and thus leaves us with a puzzle. We conduct an ...exploratory fuzzy set qualitative comparative analysis (fsQCA) of 204 publicly listed European firms combining survey and archival data to formulate theory on how stakeholder management and innovation work (in)effectively together. Distinguishing between internal and external stakeholders and exploitative and exploratory innovation, we elaborate that managing for stakeholders and innovation can be both substitutes and complements depending on a set of contingencies. We discuss boundary conditions and implications for future research.
As SMEs increasingly internationalize their innovation activities, our study strives to improve our understanding of the coordination mechanisms that SMEs can adopt to orchestrate these activities. ...Building on the evolutionary theory of organizations, we link three established coordination mechanisms (centralization, formalization, and socialization) to the time-to-market of SMEs' product innovations. We also argue that the complexity of the internationalized R&D tasks moderates the relationship between the three coordination mechanisms and time-to-market. Survey data from 103 SMEs with international innovation activities broadly support our theoretical account. With respect to the main effects, our findings suggest that a high degree of centralization tends to prolong the time-to-market, whereas formalization tends to shorten it. The moderation results further indicate that centralization can become more beneficial when a firm internationalizes highly complex R&D tasks, while formalization tends to become less beneficial with increasing task complexity. Main and moderation effects with respect to socialization are inconclusive. We discuss the implications of these findings for the academic literature and management practice.
PDF
