Foresight Thinking: A Thinking in Signals, Shifts, and Exponential Curves
Experts and futurists love telling what the future will bring. Often a lot of money is made with those predictions, but they rarely become reality. We still have no flying cars, but 140 character tweets, as the investor Peter Thiel once stated. Also the attention grabbing predictions of the Club of Rome about the limits of growth from 1972 didn’t turn into reality. Instead we completely missed smartphones, Instagram, Airbnb, Uber and similar technologies and services. Actually, it’s the total opposite: the incumbents never saw them coming and were taken out of business.
“The best way to predict the future is to create it” – Abraham Lincoln
Predictions versus Forecasts
There is a fine difference between predictions and forecasts. A prediction will tell you an end state, such as “Trump will never become president!” A forecasts on the other hand will tell you a probability of a possible outcome. “There is a 70 percent chance that Trump will not become president!”Forecasts are based on scientific data and reasoning, with a probability. Those are often used by scientists and people and institutions that created a reputation for themselves. Predictions are not so much based on facts, but more on opinions, on gut feelings. There are also often bold statements and are often done by people and organizations that have not yet created their standing in the public (the Club of Rome for instance was just founded four years prior to the bold statement of the limits of growth). They hope to win the public’s attention, or often have other reasons for their bold predictions: investors that try to hype a share they own, or pundits that spin a political agenda.
Those ways of thinking are not helping organizations that are anxious about the future of their business and don’t want to get hit without prior notice. While most organizations are familiar with incremental innovation, disruptive innovation often comes as surprise. Or they see it unfolding, but don’t react accordingly. A current example are the several disruptive innovations that are changing the automotive industry, which show the apparent confusion that the incumbents display.
But there is a mindset difference that would help a lot: the knowledge that ones own technology or business model has a limited lifespan and that something else will replace it. Smugglers know that sooner or later law enforcement will find the tunnel through which they transport drugs. Criminals have always to be one step ahead. And for companies this should be the same. One’s own product will sooner or later become obsolete. We can see with Diesel engines that they don’t have a future. But still German car makers are announcing billion dollar investments in this technology.
The blame is often with the managers. They made their career by avoiding risks and through optimization projects. They duly tried to avoid failures. That reinforces their believe that business this will always be like this. An entrepreneur who has experienced failure knows that this is not true. The next disruption is around the corner and will remove the incumbents from the market, and that faster than ever. The lifespan of companies listed in the S&P 500 index decreased from 60 years in the 1960s to less than 20 years today.
But there are methodologies that allow to prepare better for the future – and to create it.
This methodology is called Foresight Thinking. It encompasses three parts: collecting and clustering signals, identification of big shifts and trends, and the search and exploitation of exponential curves.
- A signal is a small or local innovation with the potential to disrupt the status quo and/or scale in size or geography. Signals are building blocks for imagining the future. Clustering them can inspire forecasts.
- Big shifts and trends are from the present to the future. Examples from the past and present help to discover the stories that drove them. The offer the context and proper framing for foresights.
- Disruptive changes come in S-curves that start slowly and then accelerate quickly in an exponential manner. Incremental innovation is the clustering those S-curves, while disruptive innovation stacks them.
When those tools are combined wit the understanding that one’s own product/service/business model life cycle has an expiration date, then organisations are much more willing to search for the next thing to protect themselves from obsolescence.
This Foresight Thinking goes beyond Design Thinking. While Design Thinking prefers incremental innovation, Foresight Thinking focuses on potentially disruptive innovation. Over longer periods, incremental innovation cannot be defended. A few percentage points fewer emissions, softer beds in a hotel, WIFI on the plane, an app to order a tax are easily replicated by competitors. A drive train based on batteries allows a different business model. A platform that throws empty rooms on the market as competition to hotel rooms or a truck that doesn’t require drivers wipe out old industries.
Foresight Thinking teaches the next steps that companies have to take to switch from reaction to action. If they don’t, then they will always be in danger that newcomers will push them out of the market.