Tough Calls

One of the things I say in my book, Thinking Clockwise, A Field Guide for the Innovative Leader is that this approach that forms the foundation of my work is at its core a decision making model. It’s about how we choose to think about the problems and challenges we face. It’s about what frame or lens we use.

Decision making is a sometimes underrated component of the process of creativity and innovation. Within the formal discipline of creative problem solving, convergence (i.e. selecting among possible ideas) is fully half of the process. Yet in both the research and practice, it has long been the poor relation, sometimes almost an afterthought to the sexier (and more fun) divergent thinking that generates ideas. Convergent thinking tends to be viewed as a necessary but “less creative” or “uncreative” part of the process. Some practitioners would argue that most folks are already too tempted to pass judgment on ideas, so there’s no need to shore up that side of the equation.

This bias meshes too well with the perceptions of many business executives, who tend to assume that they already know how to spot a good idea when they see one; the problem is coming up with them. They’re willing to pay,  sometimes handsomely, for help generating ideas, “But please don’t tell us how to make decisions, thank you.”

Yet, considerable research (generally outside the field of creativity) finds that these are flawed assumptions. Selecting among ideas is in effect an attempt to make predictions, predictions about the likely success of those ideas, something that’s not at all easy to do.

For example, research indicates that one’s expertise on a subject rarely enhances a person’s ability to forecast future events; that the so-called experts who we so often turn to for advice are no more skilled than the rest of us at knowing what will happen in the future. Yet they tend to assume that they are—an overconfidence that only compounds the problem.

Thought experiments have demonstrated that deliberation—the decision making model commonly used on teams—has a tendency to amplify rather than reduce bias and error.

There are proven techniques that get around these problems, approaches that encourage decentralized independent decisions, which are more accurately predictive, and boost engagement and commitment as well. These approaches emphasize differing perspectives and resist the temptation to force consensus (and the mediocrity that often entails).

Working with clients, we’ve developed decision applications that have demonstrated some very high value improvements over more traditional approaches.  If you’d like to see a white paper on Predictive Decision Making, go to

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