The challenge of being open to innovation is in breaking out of familiar patterns. Before COVID-19, there was a dominant pattern that many didn’t question. The pattern was something like: “when you ‘go’ to work, you ‘travel’ to work.” The pattern has been one of the factors behind cities building ever-larger roads and public transportation systems. Before COVID, there were some voices saying, “We would like to be able to work from home more.” Slow-moving bureaucracies were grudgingly considering these requests. Then, COVID hit and overnight many people were working exclusively from home.
Before the crisis, why was it so difficult to respond to staff requests to do more work from home? Many companies were very reluctant to experiment with it. If there had been more experimentation beforehand, perhaps the transition at the beginning of the crisis would have gone better. But prior to the pandemic, it was very difficult for institutions to consider alternatives. The structure wasn’t in place. The lesson that we should be asking now is: how can we become more open to innovation? Yes, most organizations were able to go into emergency mode and handle the crisis; that’s not really our point. Rather, the crisis exposed the fact that companies had blindly resisted looking at new patterns.
A broader concern is that companies have to deal with serious crises other than COVID as well. For example, one of the most serious crises is the failure to innovate. This failure can be hard to detect because it is often a “creeping crisis.” We all know companies that used to be doing well that fell on hard times because they didn’t keep up with the market: General Motors, Sears, Kodak, to name a few.
Creeping crises are caused by sticking to the same old patterns just as companies were sticking to the “travel to work” pattern. The process of breaking patterns and seeing new patterns is known as transformative learning. Breaking ingrained patterns is not easy. In psychology this resistance was first demonstrated in the 1920s as the Stroop effect (see Figure 1).
Typically, transformative learning happens only as a result of a crisis. So, the question is, how can you ensure transformative learning is happening without suffering from a crisis? Here we can learn from IT. In IT, so-called game days or disaster recovery testing (DiRT) events are common, in which a major crisis is simulated (e.g., some services become unavailable, a power outage occurs, or an application like Netflix’s Chaos Monkey kills servers in the system). The crisis forces engineers to design resilient software to ensure that services can continue to operate as individual machines fail.
These events are designed to protect against damage, and they are meant to create an artificial technical crisis and not an organizational (maybe even societal) crisis that would require different experiments. They test the failure to function, but they don’t test the failure to innovate.
So, what about protection against failure to innovate? How can these creeping crises be detected in time and then how can they be handled? What conditions would enable questioning the patterns and probe to work better? We recommend that you begin by scheduling game days for setting up an organizational crisis in a similar way that you simulate a technical crisis in IT:
Start with questions about what you know can go wrong.
Also ask yourself what could go right (although the DiRT events are about negative assumptions only, especially for organizational crises, we recommend considering positive effects as well).
Be open to games. Here are a few game ideas. Try playing with roles. Maybe the CEO could do their work at the receptionist desk while the lowest-level staff moves into the executive’s office. Or, at a routine office gathering, invite everyone to play with different patterns of interaction. For example, before the COVID crisis, the play acting might have included pretending to work from home.
Next step is anticipating. Everyone is familiar with the concept of feedback. It means adjusting your current behavior based on what you just experienced. “Feedforward” means that you create a model of the future and you adjust your current behavior based on information from that future model. Constantly update how you view the future. Simulate future crises — especially ones that appear gradually. You can find some ideas for anticipated behavior in the collection of so-called probes in Part III of our BOSSA nova book.
In conclusion, a lot of what we do is guided by patterns. These patterns help us in a stable context but get in the way in a dynamic or complex context and hinder innovation. The first step in dealing with suboptimal patterns is to be aware that they are hard to see. The patterns make you especially vulnerable when conditions don’t change suddenly, but rather creep up on you. To keep your patterns up to date, schedule regular times to test them such as with games and simulations. Having fun with games may be your best defense!