Why success is the biggest risk to your company

A three-stage model to avoid complacency

May 2, 2014 @ 2:59 pm EST


Success begets risk. When your company has customers and supplies value to those customers, thereby earning profits, it settles into a stable state. Refining, tweaking, honing, and optimizing become the main activities. Employees have jobs that are well-defined and are rewarded for performing those jobs effectively. Customers get value, employees are happy doing their jobs, management is happy for the results they produce, ownership is happy for the profits being generated. Who would want to rock that boat?

In exploitation mode, the boat sails smoothly until something changes. The change may be disruptive new technology, or a change in the competitive landscape, or a change in the preferences and expectations of customers. Whatever it is, the change puts pressure on the smooth execution of the status quo business model, and it threatens the profitability and perhaps even the existence of the company.

In "The Design of Business" (Harvard University Press, 2009) Roger Martin describes a simple model to understand the risk of this stable state, and a way to think about mitigating that risk. If understanding is a prerequisite to solving any problem, Martin contributes a simple, visual model and vocabulary to discuss and therefore address the risk of operating a successful business.

The knowledge funnel — A model for business renewal

The knowledge funnel has three stages: mysteries, heuristics and algorithms.

Companies are created to address mysteries that matter to people in a way that they'll pay to have them solved effectively. Mysteries are things like, “How do you create reliable, bug-free software?", “What do working mothers need help with in a car?" or “Is there a way to rent people music?"

An entrepreneur wrestles with a mystery, perhaps applying a formal technique like design thinking, or a more ad hoc approach, researching and eventually making an abductive leap to a solution to the mystery. Abduction is a form of logic, like deduction and induction, but unlike those more familiar forms, it seeks to predict the future, rather than draw conclusions from the past. As such, it isn't air-tight and guaranteed to produce a successful result. Abduction is about validity (what might be) versus reliability (what is proven). Abductive leaps for the mysteries above might look like: ”Writing tests that prove software is correct will increase code quality,” “A video camera in the headrest focused on the rear seat and displaying in the center stack will allow for safe monitoring of babies" or ”People will pay monthly to stream music to an app on their smartphone.”

From an intuitive understanding of a mystery, an entrepreneur forms a heuristic for an effective solution. Heuristics are rules of thumb that generally work to solve a problem. They work well enough to be given a name and to build a process around. Once an entrepreneur has heuristics sufficient to solve mysteries that matter to people, he or she has a company and can deliver value to customers. Heuristics can be refined and improved over time as the company gains experience, learns from experiments, and refines its understanding of the mystery.

Some companies reach a point where their heuristics have become so well-defined, predictable and effective that they become algorithms. Algorithms produce the same result every time for a given input. Making hamburgers at McDonald's is an algorithm; cooking steaks at home on the grill runs on heuristics. Companies that have developed algorithms to solve the problems of their customers have the potential to scale up dramatically. Martin uses the example of the McDonald brothers solving the mystery of what people in Southern California in the 1950s wanted to eat while away from home (hamburgers, fries, shakes, served quickly), and Ray Kroc turning their heuristic solution into rigorous algorithms that could be successfully applied on a grand scale.

Some businesses never reach the algorithm stage. Software design and development firms are good examples of these. My company, Atomic Object, has many heuristics we apply very successfully to our projects (one-week iterations, pair programming, test-driven development, fixed budget engagements, human-centered design practices, etc). But these heuristics require a great deal of experience and adaptation on each project — no one knows how to turn successful software product development into a set of algorithms that could be predictably applied on a mass scale.

The risk: Exploitation mode makes innovation difficult

The risk in exploitation mode, when your heuristics and algorithms produce good results and your company is making money, comes from being complacent. Companies can mitigate this risk by occasionally returning to the top of the knowledge funnel. By considering anew the mysteries that have been solved in the past, their current relevance in the world, or by tackling new mysteries entirely, a company can adapt to changes in the world and respond accordingly before their profitable operations are disrupted.

In theory, it should be easier for a company in exploitation mode to start at the top of the funnel than for a company in startup mode. After all, the profits generated from successful exploitation can be used for the exploration necessary. Unfortunately, there are often many structural impediments to successful companies starting from the top and moving through the funnel again.

Reward structures for employees may be set to encourage exploitation, not exploration. Exploration mode is inherently risky — a company that punishes failure may find few employees interested in these assignments.

When a return to the top of the funnel meets with success, the new product or service may very well threaten the status quo and hence the jobs of many people, even though the disruption comes from within, not a competitor.

Budgets may not adequately support exploration and/or ownership may not be willing to sacrifice short-term profitability to exploration.

Employees hired and trained to execute on the current set of heuristics and algorithms may not have the right skills to explore effectively.

Exploration can be stressful. If an employee is asked to both run the machine and explore a new mystery, the very human tendency to seek comfort and certainty may short-change the exploration effort when exploration gets complicated and uncertain and uncomfortable.

The knowledge funnel is a simple model that makes it easier to talk about the need for renewal and the risk of complacency. Unfortunately the necessary action is complex and difficult. Roger Martin's book is a great place to start.

This story was originally published in Crain's Detroit Business.

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