One of the interesting aspects of my work is that I get to spend time speaking with people from different professions about the topic of innovation. In a recent discussion with a group of senior business executives in Chicago the topic of ambiguity arose, and it created a lively debate. The specific issue in question was what is the role of ambiguity in innovation? In other words, how much ambiguity is a normal part of innovation and is there such a thing as “good” versus “bad” ambiguity?
One executive present made the argument that ambiguity is neither good nor bad and that any attempt to “control uncertainty” is futile in, if not destructive to, innovation. In his opinion one needs to let innovation teams work without structure or constraints, so that they are not hindered by “artificial barriers” or “legacy thinking.” He made a persuasive case, and it was tempting to agree with his position, since — as any parent knows — it’s easier to not set rules than to struggle to find the balance between too much and too little control. However, after listening to his position, I replied that I disagreed with his argument, and presented my belief that, first, there are two kinds of ambiguity in innovation — one good and one bad — and, second, that understanding the difference between both kinds is critical to making innovation work in a corporate setting.
Let’s examine each category of ambiguity to understand better what I mean by these terms. In order to do so, I’ll recap a discussion I had on the same topic with a medical researcher I know. When asked to consider the role that ambiguity plays in her work, she replied that, while some aspects of her search for medical innovations are indeed unknown, others are tightly controlled. For example, lab goals — say finding a new drug delivery molecule — are made as specific as possible, as are the procedures that lab members use on any given day. However, timelines and the exact sequence of events are left undefined, with the understanding that problems are solved at different rates of progress and with different combinations of techniques. After hearing her explanation, I suggested that she was describing two kinds of ambiguity: a “good” kind which allows for variations in timeline and activities and a “bad” which manifests itself in lack of clear goals or inconsistent application of tools and techniques. She agreed with my description, and also with the idea that understanding which aspects of innovation must be crystal clear and which are deliberately undefined at the start is often the difference between success and failure.
Turning to the corporate environment, let’s imagine a Procurement team in a mythical home appliance company tasked with finding the next generation of lighting technology for a new refrigerator line to be launched in three years. The standard approach for this kind of innovation effort would be for this team go out into the marketplace, speak with a large number of lighting suppliers, select a few to work with over the next year or so, and then hope that from that effort the right innovation would be selected and incorporated into the new product line. Keeping in mind the lab discussion, my opinion is that it would be critical have a very specific understanding and definition of several aspects of this innovation effort, e.g., the functional performance goals, target economics and specific procedures for evaluating the risk profile of any potential new supplier. On the other hand, certain aspects would have to be ambiguous: the way in which any new technology could impact product design or how technical challenges posed by new lighting systems would be solved at some point in the future. In this case, the former list should have as little ambiguity as possible, while the latter expects and embraces it as part of the innovation process. In other words, the Procurement team would make every effort to reduce bad ambiguity and every effort to understand and manage through good ambiguity.
The point of view shared above has some further implications that I will cover in later posts, but one is worth mentioning here and it’s the impact of innovation ambiguity on traditional project management efforts. As we know, traditional project management success was almost always gauged by the level of predictability in a project manager’s projections and reality. The smaller the variance between “plan” and “actual,” the better the project was thought to be managed. However, this is a difficult model to apply to efforts where, from the start, ambiguity is embraced as a natural part of the process. Moreover, the rise of development approaches such as Agile and Low-Code, in which work and re-work are integral to success, are also challenging the traditional way in which projects are planned, budgeted and executed.
Returning to my recent discussion in Chicago, after making my case for two kinds of ambiguity, the group agreed that its important to distinguish which kind of ambiguity is present in any innovation effort. Of course, this is something I think anyone overseeing or funding innovation efforts should do. We live in a world of increasing uncertainty, we are often told. If that’s the case, then understanding the nature of the unknowns that impact our search for innovation is a key to succeeding in the dynamic environments in which most of us work.