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 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 article proposes a framework for understanding 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 sense making, i.e. understanding which problems should or could be addressed. This shift in focus calls for new theories and brings design closer to leadership, which is, inherently, an activity of sense making.

Our insights are derived from and illustrated with two cases at the frontier of AI ‐‐ Netflix and AirBnB (complemented with analyses in 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 complex problems holistically with a systemic perspective. Algorithms instead handle complexity through very simple tasks, which are iterated continuously. This article discusses the implications of these insights for design and innovation management scholars and practitioners.

We report results of a natural field experiment conducted at a medical organization that sought contribution of public goods (i.e., projects for organizational improvement) from its 1200 employees. Offering a prize for winning submissions boosted participation by 85 percent without affecting the quality of the submissions. The effect was consistent across gender and job type. We posit that the allure of a prize, in combination with mission-oriented preferences, drove participation. Using a simple model, we estimate that these preferences explain about a third of the magnitude of the effect. We also find that these results were sensitive to the solicited person’s gender.

Utilizing suggestions from clinicians and administrative staff is associated with process and quality improvement, organizational climate that promotes patient safety, and added capacity for learning. However, realizing improvement through innovative ideas from staff depends on their ability and decision to contribute. We hypothesized that staff perception of whether the organization promotes learning is positively associated with their likelihood to engage in problem solving and speaking up. We conducted our study in a cardiology unit in an academic hospital that hosted an ideation contest that solicited frontline staff to suggest ideas to resolve issues encountered at work. Our primary dependent variable was staff participation in ideation. The independent variables measuring perception of support for learning were collected using the validated 27-item Learning Organization Survey (LOS). To examine the relationships between these variables, we used analysis of variance, logistic regression, and predicted probabilities. We also interviewed 16 contest participants to explain our quantitative results. The study sample consisted of 30% of cardiology unit staff (n=354) that completed the LOS. In total, 72 staff submitted 138 ideas, addressing a range of issues including patient experience, cost of care, workflow, utilization, and access. Figuring out the cost of procedures in the catheterization laboratory and creating a smartphone application that aids patients to navigate through appointments and connect with providers were two of the ideas that won the most number of votes and funding to be implemented in the following year. Participation in ideation was positively associated with staff perception of supportive learning environment. For example, one standard deviation increase in perceived welcome for differences in opinions was associated with a 43% increase in the odds of participating in ideation (OR=1.43, p=0.04) and 55% increase in the odds of suggesting more than one idea (OR=1.55, p=0.09). Experimentation, a practice that supports learning, was negatively associated with ideation (OR=0.36, p=0.02), and leadership that reinforces learning was not associated with ideation. The perception that new ideas are not sufficiently considered or experimented could have motivated staff to participate, as the ideation contest enables experimentation and learning. Interviews with ideation participants revealed that the contest enabled systematic bottom-up contribution to quality improvement, promoted a sense of community, facilitated organizational exchange of ideas, and spread a problem-solving oriented mindset. Enabling frontline staff to feel that their ideas are welcome and that making mistakes is permissible may increase their likelihood to engage in problem solving and speaking up, contributing to organizational improvement.

The purpose of this article is to suggest a (preliminary) taxonomy and research agenda for the topic of “firms, crowds, and innovation” and to provide an introduction to the associated special issue. We specifically discuss how various crowd-related phenomena and practices—for example, crowdsourcing, crowdfunding, user innovation, and peer production—relate to theories of the firm, with particular attention on “sociality” in firms and markets. We first briefly review extant theories of the firm and then discuss three theoretical aspects of sociality related to crowds in the context of strategy, organizations, and innovation: (1) the functions of sociality (sociality as extension of rationality, sociality as sensing and signaling, sociality as matching and identity); (2) the forms of sociality (independent/aggregate and interacting/emergent forms of sociality); and (3) the failures of sociality (misattribution and misapplication). We conclude with an outline of future research directions and introduce the special issue papers and essays.

We investigate the factors driving workers’ decisions to generate public goods inside an organization through a randomized solicitation of workplace improvement proposals in a medical center with 1200 employees. We find that pecuniary incentives, such as winning a prize, generate a threefold increase in participation compared to non-pecuniary incentives alone, such as prestige or recognition. Participation is also increased by a solicitation appealing to improving the workplace. However, emphasizing the patient mission of the organization led to countervailing effects on participation. Overall, these results are consistent with workers having multiple underlying motivations to contribute to public goods inside the organization consisting of a combination of pecuniary and altruistic incentives associated with the mission of the organization.

Tournaments are widely used in the economy to organize production and innovation. We study individual data on 2775 contestants in 755 software algorithm development contests with random assignment. The performance response to added contestants varies nonmonotonically across contestants of different abilities, precisely conforming to theoretical predictions. Most participants respond negatively, whereas the highest-skilled contestants respond positively. In counterfactual simulations, we interpret a number of tournament design policies (number of competitors, prize allocation and structure, number of divisions, open entry) and assess their effectiveness in shaping optimal tournament outcomes for a designer.