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This chapter explores the impact of innovation technologies such as simulation, modelling, and rapid prototyping on engineering practice. Innovation technologies help redefine the role of engineers in the innovation process, creating a new division of innovative labour both with and across organizations. This chapter also explores the boundaries of experimentation and inertia within particular domains of problem-solving to create new opportunities and value.

This chapter examines how design thinking, with its commitment to seeing challenges from the user perspective, prototyping and rapid learning, has begun to make head way in the policy world as a technique to review service delivery practices. This chapter will review the thinking behind it, connecting design to various social sciences theories and showing applications of the technique.

You can give any hunch the structure of a problem and make that problem tangible. Innovations are not prescribed, but rather emerge from what you do in the process of trying to understand and tame a real-world problem—that is, prototyping a problem. Get ready to be wrong, because a good solution can emerge from being wrong a lot and you need only be approximately right once. This process of prototyping a problem as an approach to innovation has several advantages: progress is about how much you learn about the problem; there are multiple strategies for making your problem tangible and getting to specific questions; and there is a demonstration possible of any problem at a scale that matches your current resources.

You can get started innovating right away: just assemble some parts you already have or can get easily; learn as you go; and use those parts to make your problem tangible. Anything can be a part: in a corporate setting, that includes anything you currently do, assemble, or procure; and for everything else—from physical parts to services to resources for acquiring knowledge—there’s the Web. You bring parts together you’ve chosen so you can make any aspect of your problem tangible—even a solution—and you ask questions, reasoning about your problem with your minds and your hands. Parts will tell you what you need to assume at your current scale, what you aren’t seeing, what you are missing, and whether the next scale is even possible.

You have a choice to set up a way for yourself or anyone else to start innovating right away. You just need something to empower exploration, and it ought to consist of things that already exist. If you accept that the trial and error reveals the innovations needed, and that you can at least simulate their impact using other parts, there is no point in trying to plan a “great innovation” or culling innovation ideas from the get-go. You can choose to focus on the problem and simply get started. So, create some tools of the trade and be a professional innovator. You can easily build an innovation prototyping kit, starting from anything—a product idea, a hunch about a problem, a development kit, a research paper, or a patent. Such a kit packages a hunch about a problem and helps you or others get started.

Organizations don’t just grown on their own. You build them, and you may end up building multiple organizations, each one atop the previous one. The scale-up logic is straightforward: You present what you did (the past) to motivate where you will go (the future), but what you work on is the middle (the present). Most emerging organizations fail because they build for the future having ignored the entire present. But you don’t have to worry about whether a decision is optimal for that rosy future—it just needs to work today. As you build the next organization, you’ll reuse parts from the old one and you’ll get to implement everything you’ve learned. Growth and scale-up work like problem solving: no one cares how you first came up with the solution. The organization that systematizes your current innovation prototype is your first big milestone.

This chapter focuses on the role of analysts in the development of an analytics program. Analysts need to realize that part of their role is to get new and valuable information into the decision-making process, and that requires a lot more effort and planning than simply writing a memo suggesting an innovative data source or defines a new metric. Decision makers cannot be simply informed about new ideas; new ideas must be sold to them. Decision makers need to buy into new tools for the tools to be useful. Analytics as innovation can take many shapes, but a fairly straightforward and effective process is for the analyst to view the process in four stages: creative, prototyping, engagement, and build. If analysts understand these four stages and plan for the entire innovation cycle from the beginning, they will maximize the probability that their new concepts will be put into practice.

This chapter looks at the migration of norms and practices of business innovation outside the business world as a consequence of their rising prestige. It provides an in-depth analysis of “life-design,” a set of commercially successful strategies developed by Silicon Valley innovators to help individuals “innovate” their lives and thereby achieve happiness. The chapter argues that the same modern-Romantic notions of the self that provided consultants with a model of creative potentiality and the cultural conditions of possibility for developing design thinking strategies for innovating technologies are now ironically being transformed as a result of the fact that the self has become the subject of those strategies as if it were a technology in need of innovation. The chapter unpacks what reflexivity means for the self as technology, what constitutes a well-designed life, what prototyping potential future lives entails, how the ideals of speed and instantaneity that suffuse business innovation affect notions of self-transformation when one’s life is approached as an object of innovation, what the presentation of self in the quest for a well-designed life means when it is the object of brainstorming sessions, and what socioeconomic conditions of possibility enable such a method of “self-innovation,” to begin with.

Advances in technology have always played a crucial role in human evolution. They have also changed the way in which we process information from the external world. The sense of touch seems to be more heavily exploited in order to produce technologies that improve the quality of our life than ever before. For example, touch is used in robotic technologies where certain forms of surgical operations are needed. Touch also plays a major role in the success of a number of everyday devices from mobile phones to PC and tablets. This sensory modality is also sought after in the development of virtual reality environments, in order to increase the sense of presence of the user. Touch also provides important information to designers and engineers performing virtual 3D modeling. Finally, tactile sensations can be adopted in order to provide a valid source of help to those people affected by visual impairments.

This chapter begins with a discussion of the philosophy and then definition of the RITE method. It then delves into the benefits of this method and provides practical notes on running RITE tests effectively. The chapter concludes with an overview of the original case study behind the 2002 article documenting this method.

I review developments in theory and methodology that may allow us to begin creating innovative forms of organizing, rather than rest content with studying them after they have emerged. We now have the conceptual and technical apparatus to prototype organization designs at small scale, cheaply and fast. The process of organization re-design can be seen in terms of multiple stages. It begins with careful observation of phenomena. Qualitative or indeed quantitative induction (i.e. data mining) can play a critical role here. Once we have some understanding or at least conjectures about underlying mechanisms, we can use the behavioral lab or an agent-based model to run cheap experiments to adjust the design. Once we have formulated a new design, we may want to run a field experiment with randomization. If the results look satisfactory, we can scale up and implement.

By recruiting our ability to toggle between concrete details (tokens, sense data) and generalizations (types, abstractions), lyrics allow our minds to make the most of our own experiences and of the experiences of others. Their openness to interpretation, however, would be overwhelming and debilitating if not bounded by culturally imposed frames. In a discussion of the Old English poem “The Wanderer,” this chapter surveys literary and cognitive studies of categorization, prototyping, exploration, and memory that contribute to the description of the back and forth movement of a mind trying to turn recalcitrant stuff (cold water, ruined buildings, jeweled cups) into ideas offering comfort.