, , ,

Double Loop Learning

There’s more than enough literature out there for corporate growth. Academics talk about implementing cultures of innovation. Consultants present case studies on sophisticated financial strategies to drive bottom line revenue. And yet, a fundamental model for growth mechanics remains elusive. In this post I present a model for scalable or better than average corporate growth.

Most companies provide a service or product to a customer. They assess customer needs and track their respective competitive landscape to continually upgrade the service or product. They also employ internal quality processes to drive operational costs down. The underlying growth model is incremental. That is, profit is incrementally increased as both sales and margins are simultaneously improved. The company achieves average growth by learning about its execution processes, both external (sales) and internal (operations). Let’s call this type of generic company a “first order learning enterprise”.

Now let’s introduce the notion of a “second order learning enterprise”. It does the same things as the first but employs an additional step. It continually assesses its execution processes as a source of new value that can be packaged into distinct new products and services.

The first order learning enterprise executes and tries to execute better. The second order learning enterprise executes while evaluating its execution process for new patentable methods, new brandable solutions. The second order learning enterprise assesses innovative potential as it executes. It walks and chews gum at the same time.

What is the difference of the two approaches as far as growth? By the time the first order learning enterprise has increased margins by some linear amount, the second order learning enterprise would have extracted distinct intellectual property. The first order enterprise sells the product or service itself, while the second order learning enterprise sells the product or service plus the learning it acquired in the process in the form of derivative products and services. In terms of revenue, the difference is between linear and scalable growth. Over time, the second order learning enterprise develops an ecosystem of interrelated and synergistic products and services. The entire ecosystem is orders of magnitude more powerful in terms of sales generation potential than the individual lines of products and services tended to by the first order learning enterprise. Apple’s exponential growth success in the last decade is an example of the generative power of an ecosystem of products and services. HP on the other hand is still selling lines of products and services. A product line fulfills a need. An ecosystem does that and provides a seamless user experience! And experiences, particularly shared experiences, generate cult following and loyalty. The generative possibilities for a second order learning enterprise are truly limitless.

The theory that underlies this model can be traced to several sources. Chris Argyris highlighted the relevance of Double Loop Learning (DDL) to management in the 1970’s. His definition of the process reinforces the model presented above. According to Argyris, DLL recognises that the way a problem is defined and solved can be a source of the problem. In DDL one gets value and revenue both ways: from solving a problem, and from selling the approach used to solve the problem to new clients! Several decades earlier, the English psychiatrist and pioneer in cybernetics Ross Ashby defined nested feedback loops that allow both learning within a process and learning to improve the process as DLL. Ross Ashby further noted this as a characteristic of adaptive systems. From this perspective, the second order learning enterprise exhibits higher order adaptive capacity.

Photo source: ‘Chris Argyris: theories of action, double-loop learning and organizational learning’, the encyclopedia of informal education. [http://infed.org/mobi/chris-argyris-theories-of-action-double-loop-learning-and-organizational-learning/. Retrieved: 3/13/2015]