Roberto Verganti

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.

Explores the challenges of innovation in a marketplace crowded with ideas, addressing the benefits of envisioning direction and meaningful forethought and embracing a process of criticism (including self-criticism). Discusses the innovation of meaning--thriving in an overcrowded world; the search for new meaning--the value for people; competing through meaning--the value for business; innovation from the inside out--making gifts; the art of criticism--the quest for a deeper vision; envisioning--the purpose of the envisioning phase; the meaning factory--insiders' criticism; and the interpreter's lab--outsiders' criticism. Verganti is Professor of Leadership and Innovation at Politecnico di Milano. Index.

•We analyze the relevance of academics’ strategic approaches to collaborations.•Scientific leverage is higher when academics pursue a more proactive strategy and are selective.•Scientific leverage is higher when academics are selective.•This impact is influenced by the amount of financial resources obtained from industrial partners.
While recent research indicates that combining scientific and entrepreneurial activities at the level of academic scientists is feasible, the literature has remained muted on the dynamics behind such successful combinations. Indeed, little is known about how researchers avoid conflicts of commitment and conflicts of interest as well as the so-called ‘skewing’ of research agendas. This study, in seeking to address this gap in the literature, analyses the relevance of academics’ strategic approaches to collaborative projects with industry. Based on survey data collected from engineering professors at two European universities (Politecnico di Milano, Italy: n=117; and KU Leuven, Belgium: n=70), we analyze whether the scientific yield from collaborative projects with industry depends on the degree of proactiveness, selectiveness and novelty of research topics. We observe that the scientific leverage of collaborating with industrial partners is higher when academics pursue a more proactive strategy and are selective. At the same time, our findings reveal that this impact is indirect: selectiveness and pro-activeness influence the amount of financial resources obtained from industrial partners, while the scientific yield itself is contingent on these resources.

Until now, the literature on innovation has focused either on radical innovation pushed by technology or incremental innovation pulled by the market. In design-driven innovation: how to compete by radically innovating what things means, Roberto Verganti introduces a third strategy, a radical shift in perspective that introduces a bold new way of competing. Design-driven innovations do not come from the market; they create new markets. They don't push new technologies; they push new meanings.
It's about having a vision, and taking that vision to your customers. Think of game-changers like Nintendo's Wii or Apple's iPod. They overturned our understanding of what a video game means and how we listen to music. Customers had not asked for these new meanings, but once they experienced them, it was love at first sight. But where does the vision come from?

Recent studies on design management have helped us to better comprehend how companies can apply design to get closer to users and to better understand their needs; this is an approach usually referred to as user-centered design. Yet analysis of design-intensive manufacturers such as Alessi, Artemide, and other leading Italian firms shows that their innovation process hardly starts from a close observation of user needs and requirements. Rather, they follow a different strategy called design-driven innovation in this paper. This strategy aims at radically change the emotional and symbolic content of products (i.e., their meanings and languages) through a deep understanding of broader changes in society, culture, and technology. Rather than being pulled by user requirements, design-driven innovation is pushed by a firm's vision about possible new product meanings and languages that could diffuse in society. Design-driven innovation, which plays such a crucial role in the innovation strategy of design intensive firms, has still remained largely unexplored. This paper aims at providing a possible direction to fill this empty spot in innovation management literature. In particular, first it proposes a metamodel for investigating design-driven innovation in which a manufacturer's ability to understand, anticipate, and influence emergence of new product meanings is built by relying on external interpreters (e.g., designers, firms in other industries, suppliers, schools, artists, the media) that share its same problem: to understand the evolution of sociocultural models and to propose new visions and meanings. Managing design-driven innovation therefore implies managing the interaction with these interpreters to access, share, and internalize knowledge on product languages and to influence shifts in sociocultural models. Second, the paper proposes a possible direction to scientifically investigate the management of this networked and collective research process. In particular, it shows that the process of creating breakthrough innovations of meanings partially mirrors the process of creating breakthrough technological innovations. Studies of design-driven innovation may therefore benefit significantly from the existing body of theories in the field of technology management. The analysis of the analogies between these two types of radical innovations (i.e., meanings and technologies) allows a research agenda to be set for exploration of design-driven innovation, a relevant as well as under-investigated phenomenon.

Nowadays, virtually no companies innovate alone. Firms team up with a variety of partners, in a wide number of ways, to create new technologies, products, and services. But what is the best way to leverage the power of outsiders? To help executives answer that question, Pisano, of Harvard Business School, and Verganti, of Politecnico di Milano, developed a simple framework focused on two questions: Given your strategy, how open or closed should your network of collaborators be? And who should decide which problems to tackle and which solutions to adopt? There are four basic modes of collaboration, say the authors. An elite circle is a closed network with a hierarchical governance: One company selects the participants, defines the problem, and chooses the solution. For instance, Alessi, an Italian home-products company, invited 200 outside experts in postmodern architecture to contribute ideas for new home-product designs. An innovation mall is hierarchical but open: Anyone can post a problem or propose solutions in it, but the company posting the problem chooses the solution. An example is lnnoCentive.com, an eBay-like site where companies post scientific challenges. An innovation community is open and decentralized: Anyone can propose problems, offer solutions, and decide which ideas to use - as happens in the Linux open-source software community. A consortium is a private group of participants that operate as equals and jointly select problems, decide how to conduct work, and choose solutions. IBM has set up a number of consortia with other companies to develop next generation semiconductor technologies. No one approach is superior; each involves strategic trade-offs. When choosing among modes, firms must weigh their advantages and challenges, and assess which will work best with their strategy, capabilities, structure, and assets.

In 1985 the architect Michael Graves designed his first consumer product-a now famous teakettle-for Alessi, the northern Italian home-furnishings manufacturer. Although Graves later designed a knockoff for Target that goes for one-fifth the price, Alessi has sold more than 1.5 million of the original version, which grew out of a process that Roberto Verganti calls "design-driven innovation." Alessi, the lighting manufacturers Flos and Artemide, the furniture maker Kartell, and a handful of other firms based in the Lombardy region ignore the design industry's two norms: "tech push," whereby an improvement in performance and functionality dictates a modification in design, and "market pull," whereby the design accommodates consumers' demand for new features. Instead, they favor an R&D operation in which a community of architects, suppliers, critics, publishers, artists, designers, and others immerse themselves in a discourse about the role, identity, and meaning of a product well before they address its form. The products that result often represent a dramatic break from their predecessors-giving them longer commercial lives and creating high consumer expectations for the brand's future offerings. A familiar example of how a change in a product's meaning can lead to a change in its design and identity is the iMac, whose friendly colors and ovoid form declared it to be, in contrast to the typical desktop computer, an appliance for the home. The author's eight years of research into seven European design communities revealed the Lombardy cluster's special strengths: the number and quality of the links between components of the design system, such as schools, studios, and manufacturers. In addition, Lombardy is strong on imagination and motivation-qualities within reach of any group of businesses. Verganti uses the Finger Lakes region of New York State to demonstrate that the potential for a design discourse exists almost everywhere.

Uncertain and dynamic environments present fundamental challenges to managers of the new product development process. Between successive product generations, significant evolutions can occur in both the customer needs a product must address and the technologies it employs to satisfy these needs. Even within a single development project, firms must respond to new information, or risk developing a product that is obsolete the day it is launched. This paper examines the characteristics of an effective development process in one such environment—the Internet software industry. Using data on 29 completed development projects we show that in this industry, constructs that support a more flexible development process are associated with better-performing projects. This flexible process is characterized by the ability to generate and respond to new information for a longer proportion of a development cycle. The constructs that support such a process are greater investments in architectural design, earlier feedback on product performance from the market, and the use of a development team with greater amounts of “generational” experience. Our results suggest that investments in architectural design play a dual role in a flexible process: First, through the need to select an architecture that maximizes product performance and, second, through the need to select an architecture that facilitates development process flexibility. We provide examples from our fieldwork to support this view.