Sustained innovation success is not the result of artful intuition or heroic vision but of a deliberate search using key information signals.

In an era of low growth, companies need innovation more than ever. Leaders can draw on a large body of theory and precedent in pursuit of innovation, ranging from advice on choosing the right spaces to optimizing the product development process to establishing a culture of creativity.1 In practice, though, innovation remains more of an art than a science.

But it doesn’t need to be.

In our research with the London Institute, we made an exciting discovery.2 Innovation, much like marketing and human resources, can be made less reliant on artful intuition by using information in new ways. But this requires a change in perspective: We need to view innovation not as the product of luck or extraordinary vision but as the result of a deliberate search process. This process exploits the underlying structure of successful innovation to identify key information signals, which in turn can be harnessed to construct an advantaged innovation strategy.

Innovation in Legoland

Let’s illustrate the idea using Lego bricks. Think back to your childhood days. You’re in a room with two of your friends, playing with a big box of Legos (say, the beloved “fire station” set). All three of you have the same goal in mind: building as many new toys as possible. As you play, each of you searches through the box and chooses the bricks you believe will help you reach this goal.

Let’s now suppose each of you approaches this differently. Your friend Joey uses what we call an impatient strategy, carefully picking Lego men and their firefighting hats to immediately produce viable toys. You follow your intuition, picking random bricks that look intriguing. Meanwhile, your friend Jill chooses pieces such as axles, wheels, and small base plates that she noticed are common in more complex toys, even though she is not able to use them immediately to produce simpler toys. We call Jill’s approach a patient strategy.

At the end of the afternoon, who will have innovated the most?3 That is, who will have built the most new toys? Our simulations show that this depends on several factors. In the beginning, Joey will lead the way, surging ahead with his impatient strategy. But as the game progresses, fate will appear to shift.

References

1. See, for example, W.C. Kim and R. Mauborgne, “Blue Ocean Strategy” (Boston, Massachusetts: Harvard Business Review Press, 2015) on choosing spaces; B. Brown and S. Anthony, “How P&G Tripled Its Innovation Success Rate,” Harvard Business Review 89 (June 2011): 66-72 on product development; or L. de Brabandere and A. Iny, “Thinking in New Boxes” (New York: Random House, 2013) on creativity culture.

2. See T.M.A. Fink, M. Reeves, R. Palma and R.S. Farr, “Dynamics of Rapid Innovation,” September 2016, https://arxiv.org.

3. We model all innovations to have a uniform value to the innovator. If value is unequal, then we simply generate a different definition for a viable product. The same mathematical patterns apply to the new distribution. Thus quantity is a good proxy for quality in our model.

4. See M. Little, “Innovation and What’s Next,” webcast presentation, September 15, 2014, www.ge.com.