Innovation and Design in the Age of Artificial Intelligence

Abstract

At the heart of any innovation process lies a fundamental practice: the way people create ideas and solve problems. This "decision making" side of innovation is what scholars and practitioners refer to as "design." Decisions in innovation processes have so far been taken by humans. What happens when they can be substituted by machines? Artificial Intelligence (AI) brings data and algorithms to the core of the innovation processes. What are the implications of this diffusion of AI for our understanding of design and innovation? Is AI just another digital technology that, akin to many others, will not significantly question what we know about design? Or will it create transformations in design that current theoretical frameworks cannot capture?This paper proposes a framework for understanding the design and innovation in the age of AI. We discuss the implications for design and innovation theory. Specifically, we observe that, as creative problem-solving is significantly conducted by algorithms, human design increasingly becomes an activity of sensemaking, that is, understanding which problems should or could be addressed. This shift in focus calls for the new theories and brings design closer to leadership, which is, inherently, an activity of sensemaking.Our insights are derived from and illustrated with two cases at the frontier of AI-Netflix and Airbnb (complemented with analyses of Microsoft and Tesla)-which point to two directions for the evolution of design and innovation in firms. First, AI enables an organization to overcome many past limitations of human-intensive design processes, by improving the scalability of the process, broadening its scope across traditional boundaries, and enhancing its ability to learn and adapt on the fly. Second, and maybe more surprising, while removing these limitations, AI also appears to deeply enact several popular design principles. AI thus reinforces the principles of Design Thinking, namely: being people-centered, abductive, and iterative. In fact, AI enables the creation of solutions that are more highly user centered than human-based approaches (i.e., to an extreme level of granularity, designed for every single person); that are potentially more creative; and that are continuously updated through learning iterations across the entire product life cycle.In sum, while AI does not undermine the basic principles of design, it profoundly changes the practice of design. Problem-solving tasks, traditionally carried out by designers, are now automated into learning loops that operate without limitations of volume and speed. The algorithms embedded in these loops think in a radically different way than a designer who handles the complex problems holistically with a systemic perspective. Algorithms instead handle complexity through very simple tasks, which are iterated continuously. This paper discusses the implications of these insights for design and innovation management scholars and practitioners.

Digital Transformation and Innovation Management: A Synthesis of Existing Research and an Agenda for Future Studies

“…Being at the core of this research stream, it is obviously connected to all the other clusters and represents the most important node in the network. When it comes to the content of this cluster, we can observe that the concept of digital transformation (Berger et al, 2019; Nambisan et al, 2019; Verhoef et al, 2021) is usually adopted in relationship with a wide range of recently introduced enabling technologies such as AI, industrial internet of things, big data, and smart products (see Blackburn et al, 2017; Chandy et al, 2017; Farrington & Alizadeh, 2017; Fossen & Sorgner, 2019; Iansiti & Lakhani, 2014; Mariani & Fosso Wamba, 2020; Raff et al, 2020; Ransbotham et al, 2016; Sjödin et al, 2020; Verganti et al, 2020). Moreover, the studies included in the cluster offer evidence on service innovation (Sjödin et al, 2020), product‐service systems (Lerch & Gotsch, 2015), organizational practices and routines (Jackson, 2019), change management (Mugge et al, 2020; Solberg et al, 2020), innovation processes (Guenzi & Habel, 2020; Klein et al, 2020), competitive dynamics (Ferreira et al, 2019; Porter & Heppelmann, 2014, 2015), organizational design and structure (Kretschmer & Khashabi, 2020).…”

Reviewing the Field of External Knowledge Search for Innovation: Theoretical Underpinnings and Future (Re‐)search Directions

“…• Decomposition and coupling of knowledge (Yayavaram and Ahuja, 2008) • Information repository and transactive memory systems (Lewis and Herndon, 2011;Ren and Argote, 2011) • Digitalization of knowledge (Nambisan, Lyytinen, Majchrzak, and Song, 2017;Verganti et al, 2020;Yoo, Boland, Lyytinen, and Majchrzak, 2012)…”

“…For instance, smart algorithms are able to detect scientific laws unfamiliar to humans (Schmidt and Lipson, 2009), and the provision of published studies to a machine-learning setup has resulted in the detection of new laws for material science (Tshitoyan et al, 2019). Clearly, these opportunities go beyond cost optimization of external search and we are only beginning to understand the larger impact on innovation management (Verganti, Vendraminelli, and Iansiti, 2020). This impact relates to questions such as: How can modern technologies (e.g., artificial intelligence, blockchain) and analytics (e.g., semantic analysis, topic mapping) help us identify relevant knowledge even from unfamiliar fields?…”

Data-driven design: the new challenges of digitalization on product design and development

“…This has significant consequences for researchers involved in studying the phenomenon and/or developing design support methods and tools, for practitioners, involved in managing this deep change within firms and for educators, who must understand what new skills and competencies are to be given to students. As it happens in most paradigmatic shifts, the emergence of data-driven design has clear interdisciplinary implications, and it is noteworthy that researchers from adjacent fields, such as Verganti et al (2020), have just recently started working on this topic as well. It is, therefore, possible that the further explorations in the new landscape of data-driven design may lead to contaminations and changes in the current boundaries between disciplines.…”