An interesting piece in today’s WSJ Business Insight section (”The New, Faster Face of Innovation” by Eric Brynjolffson and Michael Schrage of MIT) asserts that information technologies are reducing the cost of business experimentation and increasing the speed of rolling out new processes and approaches to the organization as a whole. As a result, more and more businesses are moving to use experimentation as a basis of their innovation programs. Here’s an excerpt:
Innovation initiatives that used to take months and megabucks to coordinate and launch can often be started in seconds for cents.
And that makes innovation, the lifeblood of growth, more efficient and cheaper. Companies are able to get a much better idea of how their customers behave and what they want. This gives new offerings and marketing efforts a better shot at success.
Companies will also be willing to try new things, because the price of failure is so much lower. That will bring big changes for corporate culture—making it easier to challenge accepted wisdom, for instance, and forcing managers to give more employees a say in the innovation process.
There will be even better payoffs for customers: Their likes and dislikes will have much more impact on companies’ decisions. In globally competitive markets, they will ultimately end up getting products and services better tailored to their needs.
I agree with Brynjolffson and Schrage that experimentation-based innovation will have tremendous impact on improving products and reducing companies’ innovation costs. But while they credit IT enablers, I think there’s another crucial reason that experimentation is growing in popularity. Schrage touches on it in this video companion to the article:
Schrage mentions that companies need to be creating “a culture of experimentation rather than a culture of grand planning and initiatives.” And when he uses these terms, I start thinking about the Cynefin framework, devised initially by Dave Snowden and first published in a mind-blowing article in the IBM Systems Journal (Cynthia Kurtz and Dave Snowden, “The New Dynamics of Strategy: Sensemaking in a Complex and Complicated World“) .
The framework is useful for lots of purposes: knowledge management, strategic planning, managerial action (the subject of Snowden and Boone’s HBR article, “A Leader’s Framework for Decision Making“). But here I’m discussing applying it to thinking about innovation.
Schrage’s casual comment illuminates innovation’s relationship to Cynefin. “The culture of grand planning and initiatives” is dominant in most companies, and shows that they view innovation initiatives in the Complicated domain of the Cynefin framework (I find the original terminology from Kurtz and Snowden helpful–”knowable”). Knowable or complicated systems are ones where cause and effect are related–but may be separated in time. You often need expertise to diagnose and act on a situation, but once the system is solved, the way forward is clear.
Traditional innovation initiatives treat the interactions between companies, customers and markets as a Complicated system. Innovation projects are expensive and time-consuming – you often hire consultants to lend their expertise. There is a solution–certain objectives and expectations that the initiatives must meet (these are often encoded into business plans and pro forma P&L’s). Of course, you only need to be involved in one such initiative to know that they never deliver to plan. Innovation initiatives are always surprises–sometimes delightful upside surprises, but more often long, expensive failures. This is because they treat a Complex problem (in the Cynefin definition) with a tool suited for the Complicated domain.
In the Complex domain, cause and effect are not observable in advance–”grand planning” is not productive. The outcomes of a complex process seem logical – but only in retrospect. Why did Twitter evolve the way it did? Why was iPod/iTunes so revolutionary and so successful?
“The culture of experimentation,” on the other hand, acknowledges this complexity – that the objectives of innovation – creating interesting, popular, valuable and attractive new products and getting them into the hands of customers – are not straightforwardly attained and cannot be planned. When customers buy certain products, when they linger on certain web pages over others, when they flock to some brand-new platform, they are exhibiting behavior best described by the language of complexity. And the way to achieve progress in this domain is to use experimentation: generate lots of ideas – perhaps even some deliberate mistakes. Try them out. If something works, spread it around. If it doesn’t, kill it quickly and move on. Iterate. [The Toyota Production System applies this experimental thinking to manufacturing innovation.]
So, is the ability of information technology to make experimentation fast & cheap responsible for the increasing use of experimentation to achieve innovation’s goals? Yes, in part. But a great deal of the reason lies in the fact that the old way of innovating, “grand planning,” isn’t the right tool for the task.
[Another viewpoint on innovation and experimentation is in McGrath and MacMillan's recent book, "Discovery-Driven Growth." While the book doesn't use the Cynefin terminology or share the complex adaptive systems roots, it nonetheless focuses on the uncertainty of the innovation process and emphasizes the need to cheaply and quickly experiment, allowing successful projects to emerge.]
(Image source: Wikipedia article on the Cynefin Framework)