The Potency of Shortcuts in Decision-Making

A growing body of research suggests that CEOs who use heuristics can make more effective decisions than those who take a more comprehensive approach.

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How do CEOs make good decisions? At a time when senior leaders have access to more data and sophisticated analytics tools than ever before, the central challenge of making good decisions about hiring, product development, and resource allocation is increasingly not a lack of information. Rather, it is knowing how much information is enough, and how to use it.

Scholars of decision-making have long recommended that CEOs and managers gather and analyze comprehensive information before making choices. This advice is based on two assumptions: (1) More information leads to better understanding of the decision at hand and possible consequences, and (2) an emphasis on gathering information rather than relying primarily on one’s own knowledge may reduce harmful biases.

However, a growing body of research shows that CEOs may often be better off putting more trust in the simple rules of thumb known as heuristics. These usually spring from a leader’s direct experience, are applied deliberately, and frequently result in superior decision outcomes. Our recent study, published in the Journal of Management Studies, suggests that CEOs who use more heuristics in making decisions can accelerate the speed of new product development in their organizations and achieve greater overall business performance. Another study, by Christopher Bingham et al. in Strategic Entrepreneurship Journal, suggests that using heuristics for international expansion decisions can lead to higher sales and revenue growth from those initiatives.

When (and Why) Heuristics

Research finds that heuristics work best under three conditions.

When the decision environment is noisy. In such an environment, more information is unlikely to lead to a better understanding of a specific decision problem. For example, when executives make choices about which innovation projects to invest in based on estimated market size, feasibility, or timeline, these data points often reflect the subjective evaluations of potential project leaders rather than objective facts. Using heuristics under such conditions filters out noise in the decision-making process.

Heuristics can be particularly effective when the decision environment is noisy, where more information is unlikely to lead to a better understanding of a specific decision problem.

Research shows that simple heuristics, such as “invest in projects with the most advantages” or “invest in the project that the most experienced team member prefers,” can be as accurate in selecting successful innovation projects as comprehensive decision-making while also accelerating the speed of decision-making.



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Comments (2)
Carlos Morales
Congrats on this article. It is very timely. It came across to me because I wanted insights on WHY the end of Q3 is good timing for reviewing the year performance as well as preparing for 2024.   It helped me to catch very good ideas.
Cris Casey
This article has multiple issues, the most serious of which is a distortion of the notion of what a heuristic is. 

From WIkidepia: "A heuristic, or heuristic technique, is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal or approximation."

Given this definition, many of the examples the authors cite, like Apple's policy for releasing a new iPhone every 24 months, or 3Ms 15% self-initiated development constraint, are not examples of heuristics. This is a conflating of heuristics with established policies whose only commonality is that they are expressed as simple to understand rules. 

Nor is developing a simple scoring system for evaluating opportunities. an example of applied heuristics. While the criteria determining the ranking/importance of the factors used for the model may have come from heuristically-derived experience, history shows a landscape littered with far more failed companies and initiatives driven by arbitrary leadership decisions based on "gut feeling" or prior experience, than ones who were repeatedly successful with that method.

While I applaud the authors attempt to illuminate the fact that sometimes more data or analysis does not yield any appreciable lift in decision quality or outcome, I am disappointed by the presentation of examples that truly support that position.